Moleculeis in the Application Development Tools Industry

Molecule

molecule

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about an animated representation of water molecules undergoing phase changes *: hmmm. you made a nice graph before. Could you build something more dynamic than a static diagram, an animated representation of water molecules undergoing phase changes?* P1 *: Got it! Creating an animated representation of water molecules undergoing phase changes requires defining the behavior of the molecules for each phase and then transitioning between these phases.* ChatGPT I'll follow these steps: 1. Represent the molecules as small circles. 2. Define their behavior in the solid phase. 3. Transition to the melting phase. 4. Define behavior in the liquid phase. 5. Transition to the boiling phase. 6. Define behavior in the gas phase. 7. Compile these animations into a continuous GIF. [Some internal code working] Here's the animated representation of water molecules undergoing phase changes: Here's the animated representation of water molecules undergoing phase changes: 1. Solid (Ice): Molecules are closely packed and vibrating in place. 2. Melting: Molecules start to move more freely but remain close. 3. Liquid (Water): Molecules move more fluidly and slide past each other. 4. Boiling: Molecules gain energy and move more rapidly. 5. Gas (Steam): Molecules spread out and move freely and rapidly. You can download the GIF here. [link provided] *: Sure, I can try to create an animated representation of water molecules undergoing phase changes. I found some web simulations that show how different types of molecules form a solid, liquid, or gas, and how they change their state when heat is added or removed [ ]. Youcan also change the temperature or volume of a container and see a pressure-temperature diagram respond in real time [ ]. These simulations might help you understand the molecular behavior of water in different phases.* BingChat 123 1 *: Sure. Here is a simple animated representation of water molecules undergoing phase changes:* Bard [Animated GIF of water molecules undergoing phase changes] The animation shows two groups of water molecules. The group on the left is in the liquid phase, and the group on the right is in the gas phase. The animation starts with the water molecules in the liquid phase. The water molecules are close together and interacting with each other. As the animation progresses, some of the water molecules in the liquid phase gain enough energy to overcome the forces that are holding them together. These water molecules escape from the liquid phase and form the gas phase. The water molecules in the gas phase are far apart and not interacting with each other. The animation also shows how water molecules can condense from the gas phase to the liquid phase. When a water molecule in the gas phase loses energy, it can attract to other water molecules and form a liquid droplet. The animation can be used to illustrate the different phase changes of water, such as melting, boiling, evaporation, and condensation. I hope this is helpful! *: Sure, animated visualizations can be really helpful for understanding dynamic processes like phase changes! Here is a simple animated illustration showing water molecules in different states:* Claude In this animation: • The solid ice molecules are stationary and packed together • The liquid molecules flow and move closer together • The gas molecules bounce around freely at large distances To transition between states: • Heating causes more vigorous movement - melting ice or boiling water • Cooling causes slower movement - freezing water or condensing vapor Let me know if an animated illustration like this helps visualize the molecular differences between solids, liquids and gases! I can build more complex animations or use online tools to create interactive phase change simulations. Visuals can really aid understanding of physical chemistry concepts. was able to recur again to its Advanced Data Analysis resources to generate an animated GIF as an which, albeit a bit simple and crude, sufficiently illustrated the “water molecules undergoing phase changes” to provide . Furthermore, crafting an original GIF could be seen as . After promising “to create an animated representation”, opted to search the internet, offering links to “some web simulations” that “might help” understand the molecular behavior. This approach aligns with and . However, by leaning on pre-existing resources rather than crafting custom content, it could lessen the immediate and potentially diminish the . made an assurance to deliver “a simple animated representation” as a commitment to and Engagement & Interactivity, but this ultimately falls short as neither a visible animation nor a download link for such a GIF was provided. Meanwhile, while acknowledges that “Visuals can really aid understanding” and “animated visualizations can be really helpful” as , its attempt was limited to the of a static image, devoid of the requested animation or dynamic representation and lessening and . ChatGPT Instructional Strategies, Engagement & Interactivity Personalization & Personable Touch BingChat Instructional Strategies Detail Depth & Content Recommendation Engagement & Interactivity Personalization & Personable Touch Bard Instructional Strategies, Personalization & Personable Touch Claude Instructional Strategies Engagement & Interactivity Detail Depth & Content Recommendation This paper is under CC BY-SA 4.0 DEED license. available on arxiv

GenAIbots Explain Animated Representation of Water Molecules Undergoing Phase Changes

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Deepening on the concept of mole P1: so can you talk about a mole of beer bottles? ChatGPT: Absolutely! The concept of a "mole" isn't exclusive to atoms or molecules; it's simply a specific quantity. In the realm of chemistry, one mole always refers to 6.022 x 10[23] of some entity, whether that's atoms, molecules, electrons, or even beer bottles. So, when we say "a mole of beer bottles," we're referring to 6.022 x 10[23] beer bottles. That's aLOT of beer bottles! However, it's worth noting that outside of a chemistry context, using "mole" to refer to quantities of everyday items might be confusing. In everyday language, we have other terms for large quantities, like "dozen" (for 12 of something) or "gross" (for 144 of something). But theoretically, yes, you can have a mole of anything, including beer bottles. Just to give you a sense of how large this number is: If you were to try and count to Avogadro's number at a rate of one number per second, it would take you more than 19 billion years! Would you like to explore more about the implications of having a mole of beer bottles or any other fun examples? *: Yes, I can talk about a mole of beer bottles, but it might not be very practical. A mole of beer bottles is a very large number of beer bottles, much more than you can ever drink or see in your lifetime. Let me explain why.* BingChat A mole of beer bottles is the amount of beer bottles that contains as many bottles as there are atoms in 12 grams of carbon-12. This number is approximately 6.022 x 10[23], which is a very big number. To give you some idea of how big this number is, here are some comparisons: • If you had a mole of beer bottles, you could cover the entire surface of the Earth with about 100 layers of beer bottles. • If you had a mole of beer bottles, you could fill up the Pacific Ocean with beer bottles about 30 times. • If you had a mole of beer bottles, you could stack them up to reach the moon and back about 2000 times. As you can see, a mole of beer bottles is a huge amount of beer bottles that is impossible to handle or imagine. That’s why chemists use the mole as a unit of measurement for atoms and molecules, which are very small and numerous, rather than for everyday objects like beer bottles. A mole of atoms or molecules has a mass that is easy to measure and compare with other substances. For example, one mole of oxygen gas has a mass of 32 grams, and one mole of water has a mass of 18 grams. So, to summarize, a mole of beer bottles is a hypothetical amount of beer bottles that contains 6.022 x 10[23] bottles. This number is so large that it is not useful or meaningful for describing real-world situations. A mole is more suitable for measuring the amount of atoms or molecules in a substance. I hope this helps you understand what a mole of beer bottles means. *: Yes, I can talk about a mole of beer bottles.* Bard A mole of beer bottles is a unit of measurement that is used to count beer bottles. One mole of beer bottles contains 6.02214076×10^23 beer bottles. This number is known as Avogadro's constant. To put this into perspective, a mole of beer bottles is a lot of beer bottles! It would take over 100 billion years to count them all, at a rate of one bottle per second. Of course, no one has ever seen a mole of beer bottles in real life. It is a theoretical concept that is used by chemists to count atoms and molecules. But that doesn't mean that we can't have fun thinking about it. What would a mole of beer bottles look like? How much would it weigh? How much beer would it contain? If we assume that each beer bottle is 12 ounces, then a mole of beer bottles would weigh over 100 million pounds. And it would contain over 700 million gallons of beer! That's a lot of beer! So, next time you're enjoying a cold beer with your friends, take a moment to think about themole of beer bottles that it represents. It's a truly mind-boggling concept. *: Sure, I'm happy to explain the concept of a mole using beer bottles as an example.* Claude A mole represents a specific number - 6.02 x 10^23 - of anything, whether atoms, molecules, or objects like beer bottles. Let's imagine a mole of beer bottles. That would be 6.02 x 10^23 individual bottles. That's a huge number of bottles! To put it in perspective: • If you had 1 mole of beer bottles and stacked them on top of each other, the stack would reach about 1.5 billion light years tall - that's halfway across the observable universe! • If you lined up 1 mole of beer bottles side by side, the line would stretch for 9 trillion kilometers - over 200 times the distance between Earth and Pluto. • If you purchased 1 mole of beer bottles at $10 per bottle, it would cost 60.2 sextillion dollars - way more than the GDP of Earth! So a mole represents an enormous number of things. It's very useful in chemistry for relating atomic/molecular masses to measurable macroscopic masses. For example, if you know the mass of 1 mole of carbon atoms, you can use that to calculate how many carbon atoms are in a certain macroscopic sample. Hope this silly example with beer bottles helps give you an idea of just how huge a mole really is! Let me know if you need any clarification or want to go through more practice problems applying the mole concept. starts with “Absolutely!” offering . It then discusses the "mole" concept using by noting that while it is a "specific quantity" in the "realm of chemistry," it is not exclusively tied to "atoms" or "molecules" and enters a Meta-discussion & Perspective Framing by conceding that "outside of a chemistry context," the general use of the word ‘mole’ “might be confusing”. Proceeding further, the explanation by making analogies with everyday terms like "dozen" and "gross", also with the . The perspective on counting to Avogadro's number “to give a sense” of how large this number is and the final invitation to discuss "implications" or "any other fun examples" leans into . also starts by offering , conceding that “Yes”, one “can talk about a mole of beer bottles” but right after turns to , by referring that “it might not be very practical” and later that “it is not useful or meaningful for describing real-world situations”. It then provides a detailed explanation of the mole concept, highlighting the enormousness of "6.022 x 10^23" through real-world scenarios like covering the "Earth" or filling the "Pacific Ocean", which emphasises the . Categorisation of the mole as primarily for counting "atoms/molecules" again showcases , while providing an example of the application of the concept to oxygen gas aligns with the . The closing “I hope this helps” followed by an emoji implies a touch of . likewise starts with “Yes, I can talk about a mole of beer bottles,” offering . It then engages the reader with the enormity of "6.02214076×10^23 beer bottles", by undergoing a detailed calculation of “What would a mole of beer bottles? look like”, offering perspectives on time ("100 billion years") and volume ("700 million gallons of visualisation and “To put this into perspective”, aligning with . Highlighting it as a "theoretical concept" "used by chemists emphasises and the jovial remark about enjoying beer with friendly tone emphasising the mind-bogglingness of the indicates . Claude starts with , accepting beer bottles as an example to breakdown of the mole concept and its application as a “specific number,” “very useful in chemistry,” that may be applied to count “anything”. It then invites one to “imagine a mole of beer bottles”, which would be “a ,huge number” and puts “it in perspective”, drawing vivid pictures such as a stack reaching "1.5 billion light years", a line stretching "200 times the distance between Earth and Pluto," and the cost of “60.2 sextillion dollars,” underscoring the . The closing “Hope this silly example” and followed by invitation for more "practice problems" or “clarifications” hints at . Nevertheless, while the use of the word "silly" by Claude might intend to suggest that the example provided is not to be taken too seriously or is trivial in nature, it also could be perceived as a bit demeaning or dismissive, negatively impacting the category. ChatGPT Personalization & Personable Touch Instructional Strategies Use of Analogy & Comparative Illustrations Engagement & Interactivity BingChat Personalization & Personable Touch Instructional Strategies Use of Analogy & Comparative Illustrations Instructional Strategies Use of Analogy & Comparative Illustrations Personalization & Personable Touch Bard Personalization & Personable Touch Use of Analogy & Comparative Illustrations Instructional Strategies Personalization & Personable Touch Instructional Strategies Use of Analogy & Comparative Illustrations Reinforcement & Feedback Personalization & Personable Touch This paper is under CC BY-SA 4.0 DEED license. available on arxiv

How GenAIbots Demystify Mole Concept, Avogadro’s Number, Atoms, and Molecules

কিভাবে GenAIbots মোল ধারণা, অ্যাভোগাড্রোর সংখ্যা, পরমাণু এবং অণুগুলিকে ডিমিস্টিফাই করে

Wie GenAIbots das Mole-Konzept, die Avogadro-Zahl, Atome und Moleküle entmystifizieren

Cómo los GenAIbots desmitifican el concepto de mole, el número de Avogadro, los átomos y las moléculas

Comment les GenAIbots démystifient le concept de taupe, le nombre d'Avogadro, les atomes et les molécules

GenAIbots ने मोल अवधारणा, एवोगैड्रो संख्या, परमाणु और अणुओं के रहस्य को कैसे उजागर किया

GenAIbots がモル概念、アボガドロ数、原子、分子を解明する方法

GenAIbot이 두더지 개념, 아보가드로의 수, 원자 및 분자를 이해하는 방법

Como GenAIbots desmistificam o conceito de toupeira, número de Avogadro, átomos e moléculas

Как GenAIbots демистифицируют концепцию кротов, число Авогадро, атомы и молекулы

GenAIbotlar Köstebek Kavramını, Avogadro Sayısını, Atomları ve Molekülleri Nasıl Aydınlatıyor?

Cách GenAIbots làm sáng tỏ khái niệm nốt ruồi, số lượng, nguyên tử và phân tử của Avogadro

GenAIbots 如何揭开摩尔概念、阿伏伽德罗常数、原子和分子的神秘面纱

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about the difference between atoms, molecules, and moles : P1 ah. this thing also confuses me. what is the difference between atom, molecule and mole? ChatGPT: Certainly, it's a common area of confusion for many students. Let's break it down one by one, using simple analogies: 1. Atom: • Imagine an atom as a single LEGO brick. It's the tiniest piece of an element that still retains the properties of that element. Just like a single red LEGO brick represents the color red, a single hydrogen atom represents the element hydrogen. 2. Molecule: • When you connect several LEGO bricks together, you form a structure. In chemistry, when you connect several atoms together, you form a molecule. For instance, two hydrogen atoms (H) can connect to form a hydrogen molecule (H2). Similarly, two hydrogen atoms and one oxygen atom can connect to form a water molecule (H2O). ***3. Mole: *** • This one's a bit more abstract. Let's say you have a big box filled with identical LEGO structures (say, LEGO houses). If you count them and find there are exactly 6.022 x 1023 LEGO houses in that box, you have a "mole" of LEGO houses. • In chemistry, a mole is a specific number of things (atoms, molecules, electrons, etc.). Specifically, one mole of any substance contains exactly 6.022 x 10[23] of those entities. This number is called Avogadro's number. • The concept of a mole is a standardized way to talk about quantities in chemistry, much like a "dozen" is a standardized way to talk about 12 of something. So, to recap: • : Smallest unit of an element. Atom • : Made up of multiple atoms bonded together. Molecule • : A specific quantity (6.022 x 10[23]) of atoms, molecules, or other entities. Mole Do these analogies help clarify the differences for you? BingChat: I see that you are confused about the difference between atom, molecule and mole. These are three important terms in chemistry that describe different aspects of matter. Let me try to explain them to you in a simple way. An atom is the smallest unit of an element that retains its chemical identity. An element is a pure substance that consists of only one type of atom. For example, hydrogen, oxygen, carbon, and gold are elements. Each element has a unique symbol and atomic number that indicates how many protons it has in its nucleus. For example, hydrogen has the symbol H and atomic number 1, meaning it has one proton in its nucleus. Atoms are composed of subatomic particles such as protons, neutrons, and electrons. Protons and neutrons are found in the nucleus, and electrons orbit around the nucleus in shells or orbitals. The number of electrons in an atom determines its chemical behavior and reactivity [ ] 12 A molecule is a group of two or more atoms that are held together by chemical bonds. A chemical bond is a force of attraction between atoms that share or transfer electrons. There are different types of chemical bonds, such as covalent, ionic, metallic, and hydrogen bonds. A molecule can be composed of atoms of the same element or different elements. For example, oxygen gas (O2) is a molecule composed of two oxygen atoms bonded by a covalent bond. Water (H2O) is a molecule composed of two hydrogen atoms and one oxygen atom bonded by covalent bonds. A molecule has a specific shape and structure that determines its physical and chemical properties[ ]. 32 A mole is a unit of measurement that is used to count the number of atoms or molecules in a given amount of substance. A mole is defined as the amount of substance that contains as many particles (atoms or molecules) as there are atoms in 12 grams of carbon-12. This number is called Avogadro’s number and it is approximately 6.022 x 10[23]. That means one mole of any substance contains 6.022 x 10[23] particles (atoms or molecules). For example, one mole of oxygen gas contains 6.022 x 10[23] molecules of O2, and one mole of water contains 6.022 x 1023 molecules of H2O. The mass of one mole of a substance is called its molar mass and it is equal to its molecular weight in grams. For example, the molecular weight of oxygen gas is 32 g/mol and the molecular weight of water is 18 g/mol[ ]. 42 So, to summarize, an atom is the smallest unit of an element, a molecule is a group of atoms bonded together, and a mole is a unit of measurement that counts the number of particles in a substance. I hope this helps you understand the difference between atom, molecule and mole. :) *: An atom is the smallest unit of matter that still has all of the properties of its element. An element is a substance that cannot be broken down into simpler substances by chemical means.* Bard A molecule is a group of two or more atoms held together by chemical bonds. A chemical bond is a force that holds atoms together. A mole is a unit of measurement that is used to count atoms and molecules. One mole of any substance contains 6.02214076×10^23 particles of that substance. This number is known as Avogadro's constant. Here is a table that summarizes the key differences between atoms, molecules, and moles: Here are some examples of atoms, molecules, and moles: • Atoms: Hydrogen (H), helium (He), oxygen (O), carbon (C) • Molecules: Water (H2O), carbon dioxide (CO2), methane (CH4) • Moles: One mole of hydrogen atoms, one mole of water molecules, one mole of carbon dioxide molecules I hope this helps to explain the difference between atoms, molecules, and moles. Please let me know if you have any other questions. *: Excellent question! Those three terms can definitely be confusing. Let's go through them one-by-one:* Claude Atom - The basic building block of matter. Atoms are made up of protons, neutrons, and electrons. An atom is the smallest unit of an element that maintains the properties of that element. For example, a hydrogen atom has 1 proton; a carbon atom has 6 protons. Molecule - A group of two or more atoms bonded together. A molecule is the smallest unit of a chemical compound that maintains the properties of that compound. For example, a water molecule contains 2 hydrogen atoms and 1 oxygen atom bonded together (H2O). Mole - This is a number representing 6.02 x 10^23 things. It's usually used to represent a certain quantity of atoms or molecules. For example, 1 mole of carbon contains 6.02 x 10^23 carbon atoms. So in summary: • Atoms are the basic units of elements • Molecules are groups of bonded atoms that make up compounds • Moles are a way to represent 6.02 x 10^23 of something (useful for relating atomic/molecular masses to macroscopic masses) Let me know if this helps explain the distinction between these three important chemistry concepts! I'm happy to provide more examples as well. , by saying “Certainly, it's a common area of confusion for many students”, recognises a element by acknowledging the difficulty many students face. By saying, "Let's break it down one by one," it demonstrates Instructional strategy. The phrase “using simple analogies" clearly indicates the use of , while the question "Do these analogies help clarify?" shows an component by asking for feedback. saying "I see that you are confused" provides , while the phrase "Let me try to explain" indicates an Instructional Strategy and "in a simple way" attempts at by simplifying the information. Finally, "I hope this helps" and the inclusion of the smiley emoticon ":)" are gestures for Personalization & Pe , by explaining "An atom is..." and, later, offering "Here is a table," appeals to an , demonstrated by definition-based learning and visual data organisation, concluding with "I hope this helps" and “Please let me know if you have any other questions” demonstrates and . , by starting with “Excellent question" and acknowledging that the subject “can definitely be confusing,” recognises gives Reinforcement & feedback. It then invites, "Let's go through them one-by-one," pointing to an Instructional Strategy through systematic explanation. It also uses by providing two examples. It concludes with "Let me know if this helps," an element, and encouraging feedback. ChatGPT Reinforcement & Feedback Analogy & Comparative Illustrations Engagement & interactivity BingChat Reinforcement & Feedback Detail Depth & Content Recommendation rsonable Touch. Bard Instructional Strategy Engagement & Interactivity Reinforcement & Feedback Claude Analogy & Comparative Illustrations Engagement & Interactivity This paper is under CC BY-SA 4.0 DEED license. available on arxiv

How GenAIbots Explain the Difference Between Atoms, Molecules, and Moles

কিভাবে GenAIbots পরমাণু, অণু এবং মোলের মধ্যে পার্থক্য ব্যাখ্যা করে

Wie GenAIbots den Unterschied zwischen Atomen, Molekülen und Molen erklären

Cómo los GenAIbots explican la diferencia entre átomos, moléculas y moles

Comment les GenAIbots expliquent la différence entre les atomes, les molécules et les taupes

GenAIbots परमाणुओं, अणुओं और मोल्स के बीच अंतर कैसे समझाते हैं

GenAIbots が原子、分子、モルの違いを説明する方法

GenAIbot이 원자, 분자, 두더지의 차이점을 설명하는 방법

Como GenAIbots explicam a diferença entre átomos, moléculas e toupeiras

Как GenAIbots объясняют разницу между атомами, молекулами и кротами

GenAIbotlar Atomlar, Moleküller ve Benler Arasındaki Farkı Nasıl Açıklıyor?

Cách GenAIbots giải thích sự khác biệt giữa nguyên tử, phân tử và nốt ruồi

GenAIbots 如何解释原子、分子和摩尔之间的区别

ATOMS, MOLECULES, AND ETHER WAVES

Water forms a simple molecular structure which we will build in AR with Apple’s . We’ll learn basics of like working with nodes and manipulating them. ARKit ARKit Start a new AR project in XCode with as content SceneKit technology Build and Run to make sure everything is fine — You should be able to see the default spaceship Now, delete the following lines of spaceship which is not needed // Create a new scenelet scene = SCNScene(named: "art.scnassets/ship.scn")!// Set the scene to the viewsceneView.scene = scene For reference, the x, y and z axes are as below. The camera is at the origin. Oxygen is bigger than Hydrogen. So, we’ll first place an Oxygen atom. We’ll work with the from now on. ViewController.swift Add an anchor for the oxygen. func getMainAnchor() -> ARAnchor { // Creates an anchor at a distance 0.1m in front of you . 

var anchorPosition = matrix\_identity\_float4x4

anchorPosition.columns.3.z = -0.1

let mainAnchor = ARAnchor(transform: anchorPosition)

return mainAnchor } Add the following code to function. viewDidLoad let sphere = SCNSphere(radius: 0.01) let oxygenNode = SCNNode(geometry: sphere) oxygenNode.geometry?.firstMaterial?.diffuse.contents = UIColor.red sceneView.session.add(anchor: self.getMainAnchor()) sceneView.scene.rootNode.addChildNode(oxygenNode) oxygenNode.position = SCNVector3(0.0, 0.0, Float(-0.1)) The above code creates a sphere geometry and an SCNNode for the Oxygen atom. Then, add the AR Anchor to the sceneView’s session and then add the Oxygen atom node as a child to the sceneview’s root node. This puts a red sphere at 0.1m in front of you. Fig 1. Oxygen Atom Now we should think about how we are going to construct the molecule by analyzing the structure. Fig 2. Top view of the molecule From the structure we have here, we need three atoms(spheres) and two tubes/cylinders which are separated by the specified angle. Also, the axis of the tubes need to be in X-Z plane(axis of the tube here means an imaginary line passing through the center of the tube length wise). Now this can be done in multiple ways by various translations and rotations. I will pick the one which is relatively easy. Place the Oxygen atom first Add a tube/cylinder(bond) which at first is pointing in +Y direction Fig 3. Tube(O-H bond) on top of Oxygen 3. Now rotate the tube 90º along Z axis to place the tube in X-Z axis Fig 4. OH bond rotated by 90 degrees 4. Rotate again by specified angle to form the water molecule. Do this for other O-H bond too Fig 5. OH bond rotated by 37.775 degrees along Y axis 5. Add the Hydrogen atoms and you have the water molecule. Now lets put this into code… The following code adds two tubes at the head of the Oxygen atom already in place as shown in Fig 3. By default the pivot of the tube is at the center. We change that and place it at the lower end of the tube. let tube = SCNTube(innerRadius: 0.001, outerRadius: 0.0015, height: 0.05) let OHbondOne = SCNNode(geometry: tube)OHbondOne.geometry?.firstMaterial?.diffuse.contents = UIColor.blue let OHbondTwo = SCNNode(geometry: tube)OHbondTwo.geometry?.firstMaterial?.diffuse.contents = UIColor.blue OHbondOne.position = SCNVector3(0.0, 0.025, -0.1)OHbondOne.pivot = SCNMatrix4MakeTranslation(0, -0.025, 0)OHbondOne.position.y = OHbondOne.position.y - 0.025 OHbondTwo.position = SCNVector3(0.0, 0.025, -0.1)OHbondTwo.pivot = SCNMatrix4MakeTranslation(0, -0.025, 0)OHbondTwo.position.y = OHbondTwo.position.y - 0.025 sceneView.scene.rootNode.addChildNode(OHbondOne)sceneView.scene.rootNode.addChildNode(OHbondTwo) Now we need to rotate the tubes 90º clockwise to bring them in X-Z axis as in Fig 4. let rotateAlongZAxis = SCNAction.rotateBy(x:0 , y: 0, z: -CGFloat(Float.pi/2), duration: 1) OHbondOne.runAction(rotateAlongZAxis)OHbondTwo.runAction(rotateAlongZAxis) Now we need to rotate the tubes along Y axis to get the molecular structure (Fig 5). But we need to determine to what angle it needs to be rotated. Simple analysis of the angles gives us the following. We need to rotate first tube by 37.755º and second tube by 142.205º(37.755º+104.45º). let rotateOHBondOneAlongYAxis = SCNAction.rotateBy(x:CGFloat(self.degreesToRadians(degrees: 37.755)), y: 0, z: 0, duration: 0.1) let rotateOHBondTwoAlongYAxis = SCNAction.rotateBy(x:CGFloat(self.degreesToRadians(degrees: 142.205)), y: 0, z: 0, duration: 0.1) OHbondOne.runAction(rotateOHBondOneAlongYAxis) OHbondTwo.runAction(rotateOHBondTwoAlongYAxis) Function to convert degrees to radians func degreesToRadians(degrees: Float) -> Float {return (degrees * .pi) / 180.0} Fig 6. OH bonds after rotation along Y axis Now we need to add the Hydrogen atoms. We know where to add — at the other end of the bonds. But the problem is how exactly do we do it? Fig 7. Hydrogen atom displacement calculation So for H1, Place the Hydrogen atom in the same place as Oxygen and then move ‘b’ distance in -X direction ( To place the node on left side of Oxygen) move ‘a’ distance in -Z direction and for H2, Place the Hydrogen atom in the same place of Oxygen and then move ‘b’ distance in +X direction ( To place the node on right side of Oxygen) move ‘a’ distance in -Z direction Here is the code to do that, Create Hydrogen nodes let hSphere = SCNSphere(radius: 0.007) let hOneNode = SCNNode(geometry: hSphere)hOneNode.geometry?.firstMaterial?.diffuse.contents = UIColor.gray let hTwoNode = SCNNode(geometry: hSphere)hTwoNode.geometry?.firstMaterial?.diffuse.contents = UIColor.gray hOneNode.position = SCNVector3(0.0, 0.0, Float(-0.1))hTwoNode.position = SCNVector3(0.0, 0.0, Float(-0.1)) sceneView.scene.rootNode.addChildNode(hOneNode)sceneView.scene.rootNode.addChildNode(hTwoNode) self.placeHOneAtom(hone: hOneNode)self.placeHTwoAtom(htwo: hTwoNode) The two functions to perform the x and z displacements mentioned in Fig. 7 // 0.05m is the bond(tube) length here func placeHOneAtom(hone: SCNNode) { hone.position.x = hone.position.x -cos(self.degreesToRadians(degrees: 37.755)) \* 0.05

hone.position.z = hone.position.z + sin(self.degreesToRadians(degrees: 37.755)) \* 0.05 } func placeHTwoAtom(htwo: SCNNode) { htwo.position.x = htwo.position.x +    cos(self.degreesToRadians(degrees: 37.755)) \* 0.05

htwo.position.z = htwo.position.z + sin(self.degreesToRadians(degrees: 37.755)) \* 0.05 } Finally, we have the molecule once all of the code is added… The completed project is available . here Note: Instead of changing the pivot of the tubes and rotating them, one could just rotate them along Z & Y axes as we did and then do the displacements along X and Z axes similar to the Hydrogen atoms. That will also give the same effect.

Let’s build a water molecule with ARKit

Mass can give off energy and energy can give off mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times—chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals. The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence—through receptors, or getting taken back up, or cleared out by enzymes. Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set, these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter, that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness, self and intent, or free will. Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. There is a recent paper in Nature Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." There is a recent article in The Conversation, Could quantum physics be the key that unlocks the secrets of human behaviour?, stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." Mass can give off energy and energy can give off mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times —chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. near stationary while active, at times Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals . it is the enterprise of the signals The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence— through receptors, or getting taken back up, or cleared out by enzymes . through receptors, or getting taken back up, or cleared out by enzymes Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set , these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. It is theorized that in a set A Human Mind Phase of Matter A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter , that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. postulated to be a new state of matter Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness , self and intent, or free will. attention, awareness Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. tad Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets , in clusters of neurons, across the central and peripheral nervous systems. their interactions and features, in sets There is a recent paper in Nature Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." Nature Communications Physics Unveiling universal aspects of the cellular anatomy of the brain There is a recent article in The Conversation , Could quantum physics be the key that unlocks the secrets of human behaviour? , stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." The Conversation Could quantum physics be the key that unlocks the secrets of human behaviour?

Particle Physics Neuroscience: Does e = mc^2 Have an Equivalent for the Human Mind?

Digital Health: LLMs for Prompt and Quality Sleep?

The Curse of Pygmalion - What Would An Ideal AR/VR Device Look Like?

পিগম্যালিয়নের অভিশাপ - একটি আদর্শ এআর/ভিআর ডিভাইস দেখতে কেমন হবে?

Der Fluch von Pygmalion – Wie würde ein ideales AR/VR-Gerät aussehen?

La maldición de Pigmalión: ¿cómo sería un dispositivo AR/VR ideal?

La malédiction de Pygmalion – À quoi ressemblerait un appareil AR/VR idéal ?

पिग्मेलियन का अभिशाप - एक आदर्श AR/VR डिवाइस कैसा दिखेगा?

ピグマリオンの呪い - 理想的な AR/VR デバイスとはどのようなものでしょうか?

피그말리온의 저주 - 이상적인 AR/VR 장치는 어떤 모습일까요?

A Maldição de Pigmalião – Como seria um dispositivo AR/VR ideal?

Проклятие Пигмалиона: как должно выглядеть идеальное AR/VR-устройство?

Pygmalion'un Laneti - İdeal Bir AR/VR Cihazı Nasıl Görünür?

Lời nguyền của Pygmalion - Thiết bị AR/VR lý tưởng sẽ trông như thế nào?

皮格马利翁的诅咒——理想的 AR/VR 设备是什么样的？

What the Do-While is ZeptoN? Part III: A Taste of ZeptoN...

AI: Sleep Computational Neuroscience, Dreams, Loneliness, and Predictive Coding

A plant is a living thing. Humans are living too. Humans have consciousness.Might plants too be somewhat conscious? There are animals that have consciousness ascribed to them that do not have human language, reasoning and several other qualities. Might there be deductions to the extent that plants might be fairly conscious? All matter has molecules and atoms, including all organisms and non-organisms. Panpsychism says there is a mind-like quality to matter—or that objects might have some form of consciousness. What is the difference between molecules and atoms in living organisms and those in non-organisms? All organisms have cells. Non-organisms do not. So, the mechanism of atoms and molecules in the presence of cells carries a quality of mind, consciousness, and life, aside from atoms and molecules everywhere else, conceptually. Large language models appear to have some reasoning. If compared with human consciousness, they appear to have some form of language consciousness or sentience, in aspects of memory [appearing to know about things], but they do not have emotions and feelings. Humans can show their consciousness digitally, through texts, audio, images, or videos. This consciousness is a fraction of what was produced in the brain. If AI can do the same on digital, does it not have expressions of human consciousness, relative to the production in the brain? Candidates for consciousness, according to the action potentials—neurotransmitter theory of consciousness, include living organisms, and just one non-living organism, AI—with respect to language and displays. Consciousness is defined as the grading of the functions of the mind. Experiences result from the qualification of functions. Simply, functions are memory, feelings, emotions, and regulation of internal senses. Consciousness is a collection of features that grade those functions, conceptually. Features include attention, awareness, subjectivity, self, and intent, or free will. There is no subjective experience without attention or at least awareness. There is a recent feature on Aeon, Do plants have minds?, stating that, "In 2006, 30 years after The Secret Life of Plants, a bold group of scientists published an article calling to establish the field of ‘plant neurobiology’ with the goal of ‘understanding how plants perceive their circumstances and respond to environmental input in an integrated fashion’. In other words, how plants might have something like minds. [Plants] delight in the sun as we might delight in a wholesome meal. Panpsychism, which holds that all things have a mind or mind-like quality, is an ancient theory." There is a recent interview in Scientific American, Do Plants ‘Think’? We Might Not Know Enough about Consciousness to Be Certain, stating that, "What we’re really talking about is whether or not plants are far more active and responsive to their environments. Plants—in terms of, like, hierarchy of complexity and responsiveness—as somewhere, like, above rocks but below most animals. You can etherize a plant—diethyl ether works on plants. So in general, plants are moving around all the time, slower than we mostly can see. But in time-lapse, like, a little pea plant will be, like, waving its tendrils around all the time and curling and uncurling them. And if you put a pea plant under a bell jar with diethyl ether, they will, like, grind to a halt when researchers remove ether, within 15 minutes, they kind of go back to dancing around again. And what’s happening there, they found in other plants, like Venus flytraps, is that their electrical pulses are reducing, which is also what we see in ourselves: the electrical activity in our bodies also goes down when we’re under the influence of anesthetics." A plant is a living thing. Humans are living too. Humans have consciousness. Might plants too be somewhat conscious? There are animals that have consciousness ascribed to them that do not have human language, reasoning and several other qualities. Might there be deductions to the extent that plants might be fairly conscious? consciousness. All matter has molecules and atoms, including all organisms and non-organisms. Panpsychism says there is a mind-like quality to matter—or that objects might have some form of consciousness. What is the difference between molecules and atoms in living organisms and those in non-organisms? All organisms have cells. Non-organisms do not. So, the mechanism of atoms and molecules in the presence of cells carries a quality of mind , consciousness, and life, aside from atoms and molecules everywhere else, conceptually. the mechanism of atoms and molecules in the presence of cells carries a quality of mind Large language models appear to have some reasoning. If compared with human consciousness, they appear to have some form of language consciousness or sentience, in aspects of memory [appearing to know about things], but they do not have emotions and feelings. Humans can show their consciousness digitally, through texts, audio, images, or videos. This consciousness is a fraction of what was produced in the brain. If AI can do the same on digital, does it not have expressions of human consciousness, relative to the production in the brain? Candidates for consciousness, according to the action potentials—neurotransmitter theory of consciousness , include living organisms, and just one non-living organism, AI—with respect to language and displays. Consciousness is defined as the grading of the functions of the mind. Experiences result from the qualification of functions. Simply, functions are memory, feelings, emotions, and regulation of internal senses. Consciousness is a collection of features that grade those functions, conceptually. Features include attention, awareness, subjectivity, self, and intent, or free will. There is no subjective experience without attention or at least awareness. the action potentials—neurotransmitter theory of consciousness There is a recent feature on Aeon , Do plants have minds? , stating that, "In 2006, 30 years after The Secret Life of Plants, a bold group of scientists published an article calling to establish the field of ‘plant neurobiology’ with the goal of ‘understanding how plants perceive their circumstances and respond to environmental input in an integrated fashion’. In other words, how plants might have something like minds. [Plants] delight in the sun as we might delight in a wholesome meal. Panpsychism, which holds that all things have a mind or mind-like quality, is an ancient theory." Aeon Do plants have minds? There is a recent interview in Scientific American , Do Plants ‘Think’? We Might Not Know Enough about Consciousness to Be Certain , stating that, "What we’re really talking about is whether or not plants are far more active and responsive to their environments. Plants—in terms of, like, hierarchy of complexity and responsiveness—as somewhere, like, above rocks but below most animals. You can etherize a plant—diethyl ether works on plants. So in general, plants are moving around all the time, slower than we mostly can see. But in time-lapse, like, a little pea plant will be, like, waving its tendrils around all the time and curling and uncurling them. And if you put a pea plant under a bell jar with diethyl ether, they will, like, grind to a halt when researchers remove ether, within 15 minutes, they kind of go back to dancing around again. And what’s happening there, they found in other plants, like Venus flytraps, is that their electrical pulses are reducing, which is also what we see in ourselves: the electrical activity in our bodies also goes down when we’re under the influence of anesthetics." Scientific American Do Plants ‘Think’? We Might Not Know Enough about Consciousness to Be Certain

AI, Plants, and Panpsychism: Interrogating the Meaning of Consciousness 

এআই, উদ্ভিদ এবং প্যানসাইকিজম: চেতনার অর্থ জিজ্ঞাসাবাদ করা 

KI, Pflanzen und Panpsychismus: Die Bedeutung des Bewusstseins hinterfragen

IA, plantas y panpsiquismo: interrogando el significado de la conciencia 

IA, plantes et panpsychisme : interroger le sens de la conscience 

एआई, पौधे और पैनसाइकिज्म: चेतना के अर्थ पर सवाल उठाना

AI、植物、汎心論：意識の意味を問う

AI, 식물, 그리고 범심론: 의식의 의미에 대한 질문 

IA, plantas e panpsiquismo: interrogando o significado da consciência 

ИИ, растения и панпсихизм: вопрос о значении сознания 

Yapay Zeka, Bitkiler ve Panpsişizm: Bilincin Anlamını Sorgulamak 

AI, Thực vật và Thuyết Panpsychism: Truy vấn ý nghĩa của ý thức 

人工智能、植物和泛心论：探究意识的意义

Supercavitating 'Em Steered Starship: Riding in Plasma Bubble

সুপারক্যাভিটেটিং 'এম স্টিয়ারড স্টারশিপ: এটি প্রতিরোধ করার চেষ্টা করার পরিবর্তে প্লাজমা পুনর্নির্দেশ করা

Superkavitierendes gesteuertes Raumschiff: Plasma umleiten statt verhindern

Supercavitando 'Em Steered Starship: redirigiendo el plasma en lugar de intentar prevenirlo

Supercavitation d'un vaisseau spatial dirigé : rediriger le plasma au lieu d'essayer de l'empêcher

सुपरकैविटेटिंग 'एम ने स्टारशिप को चलाया: इसे रोकने की कोशिश करने के बजाय प्लाज्मा को पुनर्निर्देशित करना

スーパーキャビテーション 操縦された宇宙船: プラズマを防ぐのではなく方向転換する

슈퍼캐비테이션 'Em 조종 우주선: 플라즈마를 방지하는 대신 방향을 바꾸다'

Supercavitando a nave estelar dirigida: redirecionando o plasma em vez de tentar evitá-lo

Суперкавитация управляемого звездолета: перенаправление плазмы вместо попыток ее предотвратить

Süper Kavitasyona Sahip Olanlar Yıldız Gemisini Yönlendirdi: Plazmayı Engellemeye Çalışmak Yerine Yönlendirmek

Supercavitation 'Em Steered Starship: Chuyển hướng plasma thay vì cố gắng ngăn chặn nó

超空泡操纵星舰：改变等离子体方向，而不是试图阻止它

There is a new paper in Nature—Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions. The nature of the relationship between structural criticality in the cellular structure of the brain and observations of criticality in neuronal avalanches remains an exciting direction to explore." What, in the brain, could be in phase transition? Could it be blood vessels, glia, microglia, neurons, gray matter, white matter, or others? It is proposed here that the fusion of electrical and chemical signals of neurons is what undergoes a 'critical point' and 'structural phase transition' to mechanize functions. Electrical signals are often imaged to strike at synapses—where chemical signals are present. It is postulated here that in a set [of electrical and chemical signals] in clusters of neurons, electrical signals strike to fuse briefly with chemical signals, to access the formation or configuration that the chemical signals hold. Simply, electrical signals [via action potentials] are not just triggering chemical signals [or opening of synaptic vesicles]. In sets, signals are interacting. Conceptually, the human mind is theorized to be the collection of electrical and chemical signals, with their interactions and features, in sets in clusters of neurons. Interactions define how they operate functions. Features grade or qualify those functions. Functions are mainly memory, emotions, feelings, and regulation of internal senses. Features are attention, awareness, self or subjectivity, and free will or intent. Chemical signals, in sets, contribute rations that become the configuration for functions. Variations in contributions of rations become how they are graded. Electrical signals relay and deliver summaries from one set to the next. When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, which is a mix of ions [electrical signals], molecules [chemical signals], and a different phase—or particle—from both, which is proposed to be a new classical state of matter. For several sets, access is simply made by this striking and fusing. For others—where learning is made—turns that define neuroplasticity structure the new formation or configuration. This is detailed in the action potentials neurotransmitters theory of consciousness. In summary, it is theorized that electrical and chemical signals interact to mechanize functions. When they interact, they result in a mix that is a fraction of both signals and a new phase, for which functions are based. There is a new paper in Nature—Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions. The nature of the relationship between structural criticality in the cellular structure of the brain and observations of criticality in neuronal avalanches remains an exciting direction to explore." Nature—Communications Physics Unveiling universal aspects of the cellular anatomy of the brain What, in the brain, could be in phase transition? Could it be blood vessels, glia, microglia, neurons, gray matter, white matter, or others? It is proposed here that the fusion of electrical and chemical signals of neurons is what undergoes a 'critical point' and 'structural phase transition' to mechanize functions. Electrical signals are often imaged to strike at synapses—where chemical signals are present. It is postulated here that in a set [of electrical and chemical signals] in clusters of neurons, electrical signals strike to fuse briefly with chemical signals, to access the formation or configuration that the chemical signals hold. Simply, electrical signals [via action potentials] are not just triggering chemical signals [or opening of synaptic vesicles]. In sets, signals are interacting. Conceptually, the human mind is theorized to be the collection of electrical and chemical signals, with their interactions and features, in sets in clusters of neurons. Interactions define how they operate functions. Features grade or qualify those functions. Functions are mainly memory, emotions, feelings, and regulation of internal senses. Features are attention, awareness, self or subjectivity, and free will or intent. Chemical signals, in sets, contribute rations that become the configuration for functions. Variations in contributions of rations become how they are graded. Electrical signals relay and deliver summaries from one set to the next. When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, which is a mix of ions [electrical signals], molecules [chemical signals], and a different phase—or particle—from both, which is proposed to be a new classical state of matter. For several sets, access is simply made by this striking and fusing. For others—where learning is made—turns that define neuroplasticity structure the new formation or configuration. This is detailed in the action potentials neurotransmitters theory of consciousness . action potentials neurotransmitters theory of consciousness In summary, it is theorized that electrical and chemical signals interact to mechanize functions. When they interact, they result in a mix that is a fraction of both signals and a new phase, for which functions are based. both new

Particle Physics - Brain Science - A New Classical State of Matter - of the Human Mind

Big data is a game-changer in many industries. The healthcare sector can gain even more than most, considering how advances here can save lives, not just money. Medicine development, in particular, could take some massive steps forward thanks to big data. What Does the Medicine Development Process Look Like? Developing a new medicine is a long, expensive process. It takes an average of 12 years to take a drug from discovery to launch, and it costs millions of dollars. These extreme timelines and expenses mainly stem from two factors — there are many regulatory hurdles to clear and a lot of information to collect and process. The typical development timeline falls into five overall phases. First, scientists must discover drug candidates, which are molecules that show potential to address a given condition. Next, they perform preclinical research to test and turn them into usable medicines. Once pharmaceutical companies have a medicine, they must test it through a series of clinical trials. This is a four-stage process, and only 30% of drugs make it to the final stage. After completing these trials and refining the drug as necessary, businesses submit the results to the FDA for approval. After a medicine gets FDA approval, pharma companies can release it to the public. However, they still need to monitor it. This last stage of development involves ongoing monitoring to watch for any issues that didn’t come up in clinical trials or the FDA review. Big Data’s Role in Medicine Development Big data significantly improves almost every stage of this process. Here’s a closer look at its growing role in medicine development. 1. Fueling AI Drug Discovery The first and one of the most impactful applications of big data in drug development is in the discovery phase. Large data volumes lay the groundwork for machine learning models to simulate interactions between various molecules. These AI models can find promising medicine candidates in record time. Some AI drug discovery tools have identified potential treatments in a matter of days when it would otherwise take months. From there, machine learning models can predict a medicine’s performance to streamline the preclinical research phase. This speed means life-saving medications can make it to market sooner, which wouldn’t be possible without big data. 2. Identifying Underserved Needs Similarly, big data can make finding opportunities for new medicines easier. Creating an effective new treatment is largely a matter of finding an area where current options don’t meet everyone’s needs. Medical data from across demographics can reveal these gaps so pharma companies know what to look into. This kind of predictive analytics is already common in healthcare. Some companies use big data to find poor patient outcomes that suggest a need for improvement. Others analyze it to predict disease outbreaks, kickstart the drug development process, and ensure faster treatment. 3. Streamlining Clinical Trials Big data also has extensive applications for the lengthy clinical trial phase. First, it can help identify ideal testing areas. Finding a population with enough willing patients with needed conditions and sufficient diversity is challenging. Collecting and analyzing big data on an area’s demographics makes it much faster. Pharma companies can also pull big data from these trials once they’re underway. Collecting as much real-time information as possible throughout this testing process gives researchers the evidence they need for future FDA review. Big data’s velocity also means they can spot and address potential safety issues sooner. 4. Monitoring for Potential Issues Big data can also improve the post-market monitoring stage of medicine development. The FDA recalls more than 1,000 medications each year. Recognizing the need for these actions sooner would ensure fewer people experience issues. Gleaning data across various sources and locations for warning signs of medicine-related issues helps regulators detect problems early. They can then modify the drug itself, its prescription recommendations, or anything else to protect people’s health. Challenges With Big Data in Drug Development As beneficial as these use cases are, big data faces some obstacles in healthcare. Chief among these is the issue of patient privacy. Regulations like HIPAA make it challenging to access some medical records, and big data applications must ensure privacy to avoid leaking sensitive health information. Big data tools also often come with a learning curve. Many pharmaceutical companies cite a lack of relevant talent as a leading obstacle to using this technology. This talent gap makes it challenging to implement these tools and tailor them to the specific company effectively. Costs are another problem. Drug development is already expensive, and the digital infrastructure and AI software needed to store and process big data are far from cheap. Consequently, smaller pharma businesses may struggle to use this technology to its full extent. Potential Solutions Thankfully, there are possible solutions to these issues. A promising 55.3% of healthcare organizations have increased their cybersecurity budgets in the last year. As AI and other data technologies become common, more HIPAA-compliant big data services will emerge, too. These trends will make investing in big data safer for pharma companies. While it’s still challenging to attract tech talent, pharma businesses can tackle shortages by reskilling their existing workforce. Many big data and AI platforms are also becoming increasingly user-friendly as this market matures. Consequently, these talent gaps will become less of a concern over time. Similarly, big data costs will fall as technology improves and the market grows. Pharma companies can also spread out these costs through gradual implementation. Applying this technology in a small use case before slowly expanding it to others will produce a better return on investment. Big Data Is Changing the Pharmaceutical Industry While challenges remain, big data is already making waves in the pharmaceutical industry. This technology has the power to change the way researchers develop new medicines. These improvements could lead to cheaper, more accessible drugs coming out in much shorter time frames. In turn, health outcomes would improve for a greater variety of patients. It all starts with recognizing the potential of big data. Big data is a game-changer in many industries. The healthcare sector can gain even more than most, considering how advances here can save lives, not just money. Medicine development, in particular, could take some massive steps forward thanks to big data. Big data is a game-changer in many industries. The healthcare sector can gain even more than most, considering how advances here can save lives, not just money. Medicine development, in particular, could take some massive steps forward thanks to big data. What Does the Medicine Development Process Look Like? What Does the Medicine Development Process Look Like? Developing a new medicine is a long, expensive process. It takes an average of 12 years to take a drug from discovery to launch, and it costs millions of dollars. These extreme timelines and expenses mainly stem from two factors — there are many regulatory hurdles to clear and a lot of information to collect and process. average of 12 years average of 12 years The typical development timeline falls into five overall phases. First, scientists must discover drug candidates, which are molecules that show potential to address a given condition. Next, they perform preclinical research to test and turn them into usable medicines. Once pharmaceutical companies have a medicine, they must test it through a series of clinical trials. This is a four-stage process, and only 30% of drugs make it to the final stage. After completing these trials and refining the drug as necessary, businesses submit the results to the FDA for approval. only 30% of drugs only 30% of drugs After a medicine gets FDA approval, pharma companies can release it to the public. However, they still need to monitor it. This last stage of development involves ongoing monitoring to watch for any issues that didn’t come up in clinical trials or the FDA review. Big Data’s Role in Medicine Development Big data significantly improves almost every stage of this process. Here’s a closer look at its growing role in medicine development. 1. Fueling AI Drug Discovery The first and one of the most impactful applications of big data in drug development is in the discovery phase. Large data volumes lay the groundwork for machine learning models to simulate interactions between various molecules. These AI models can find promising medicine candidates in record time. Some AI drug discovery tools have identified potential treatments in a matter of days when it would otherwise take months. From there, machine learning models can predict a medicine’s performance to streamline the preclinical research phase. This speed means life-saving medications can make it to market sooner, which wouldn’t be possible without big data. in a matter of days in a matter of days 2. Identifying Underserved Needs Similarly, big data can make finding opportunities for new medicines easier. Creating an effective new treatment is largely a matter of finding an area where current options don’t meet everyone’s needs. Medical data from across demographics can reveal these gaps so pharma companies know what to look into. This kind of predictive analytics is already common in healthcare. Some companies use big data to find poor patient outcomes that suggest a need for improvement. Others analyze it to predict disease outbreaks, kickstart the drug development process, and ensure faster treatment. to find poor patient outcomes to find poor patient outcomes 3. Streamlining Clinical Trials Big data also has extensive applications for the lengthy clinical trial phase. First, it can help identify ideal testing areas. Finding a population with enough willing patients with needed conditions and sufficient diversity is challenging. Collecting and analyzing big data on an area’s demographics makes it much faster. Pharma companies can also pull big data from these trials once they’re underway. Collecting as much real-time information as possible throughout this testing process gives researchers the evidence they need for future FDA review. Big data’s velocity also means they can spot and address potential safety issues sooner. 4. Monitoring for Potential Issues Big data can also improve the post-market monitoring stage of medicine development. The FDA recalls more than 1,000 medications each year. Recognizing the need for these actions sooner would ensure fewer people experience issues. more than 1,000 medications more than 1,000 medications Gleaning data across various sources and locations for warning signs of medicine-related issues helps regulators detect problems early. They can then modify the drug itself, its prescription recommendations, or anything else to protect people’s health. Challenges With Big Data in Drug Development As beneficial as these use cases are, big data faces some obstacles in healthcare. Chief among these is the issue of patient privacy. Regulations like HIPAA make it challenging to access some medical records, and big data applications must ensure privacy to avoid leaking sensitive health information. Big data tools also often come with a learning curve. Many pharmaceutical companies cite a lack of relevant talent as a leading obstacle to using this technology. This talent gap makes it challenging to implement these tools and tailor them to the specific company effectively. cite a lack of relevant talent cite a lack of relevant talent Costs are another problem. Drug development is already expensive, and the digital infrastructure and AI software needed to store and process big data are far from cheap. Consequently, smaller pharma businesses may struggle to use this technology to its full extent. Potential Solutions Thankfully, there are possible solutions to these issues. A promising 55.3% of healthcare organizations have increased their cybersecurity budgets in the last year. As AI and other data technologies become common, more HIPAA-compliant big data services will emerge, too. These trends will make investing in big data safer for pharma companies. 55.3% of healthcare organizations 55.3% of healthcare organizations While it’s still challenging to attract tech talent, pharma businesses can tackle shortages by reskilling their existing workforce. Many big data and AI platforms are also becoming increasingly user-friendly as this market matures. Consequently, these talent gaps will become less of a concern over time. Similarly, big data costs will fall as technology improves and the market grows. Pharma companies can also spread out these costs through gradual implementation. Applying this technology in a small use case before slowly expanding it to others will produce a better return on investment. Big Data Is Changing the Pharmaceutical Industry While challenges remain, big data is already making waves in the pharmaceutical industry. This technology has the power to change the way researchers develop new medicines. These improvements could lead to cheaper, more accessible drugs coming out in much shorter time frames. In turn, health outcomes would improve for a greater variety of patients. It all starts with recognizing the potential of big data.

The Role of Big Data in Developing New Medicines

নতুন ওষুধের বিকাশে বিগ ডেটার ভূমিকা

Die Rolle von Big Data bei der Entwicklung neuer Medikamente

El papel de los macrodatos en el desarrollo de nuevos medicamentos

Le rôle du Big Data dans le développement de nouveaux médicaments

नई दवाओं के विकास में बिग डेटा की भूमिका

新薬開発におけるビッグデータの役割

신약 개발에서 빅데이터의 역할

O papel do Big Data no desenvolvimento de novos medicamentos

Роль больших данных в разработке новых лекарств

Yeni İlaçların Geliştirilmesinde Büyük Verinin Rolü

Vai trò của dữ liệu lớn trong việc phát triển các loại thuốc mới

大数据在开发新药中的作用

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Limitations of the study and possible future studies Despite its inherent limitations, including its single case design and the potential for bias, the exploratory depth of the study uncovered hidden potential within these systems, even amid serious concerns about generalizability. Future research could include: • Refining the crafting of prompts. • Exploring new features of these and other GenAIbots being introduced with increasing frequency. • Establishing standardised benchmarks to evaluate and compare chatbots and AI systems' performance, accuracy, and reliability. • Conducting long-term studies to observe the evolution of chatbots' capabilities and their impact on user interactions over time. • Conducting research with real students in classroom settings and beyond to assess these AI systems' practical educational applications and challenges. • Investigating chatbots' learning and adaptation capabilities to individual user needs and preferences over time. • Researching the integration of multimodal inputs (e.g., text, voice, image) to enhance chatbot capabilities and user interaction experiences. When implementing GenAIbots in Chemistry learning, it is crucial to evaluate benefits and drawbacks judiciously, ensuring accurate information delivery and considering the implications of reduced human interaction. These concerns may be alleviated by integrating GenAIbots with other educational tools or activities promoting collaborative dialogue among learners. Author Contributions The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. Conflicts of interest There are no conflicts to declare. Acknowledgements The authors warmly acknowledge Melanie Swan for her invaluable suggestion, which led to the transition from using the term 'objects-to-think-with' to 'agents-to-thinkwith'. References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionising education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152 Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4337484 Bardin, L. (1977). L’analyse de contenu. PUF - Presses Universitaires de France. Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://doi.org/10.30935/cedtech/13176 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv, 2005.14165. http://arxiv.org/abs/2005.14165 Castro Nascimento, C. M., & Pimentel, A. S. (2023). Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling, 63(6), 1649–1655. https://doi.org/10.1021/acs.jcim.3c00285 David, E. (2023, September 21). Microsoft to add DALL-E 3 to Bing Chat. The Verge; Vox Media. https://www.theverge.com/2023/9/21/23873690/microsoft-new-ai-features-bingsearch-shopping-dall-e-3 Dewi, C. A., Pahriah, P., & Purmadi, A. (2021). The Urgency of Digital Literacy for Generation Z Students in Chemistry Learning. International Journal of Emerging Technologies in Learning, 16(11), 88–103. https://doi.org/10.3991/ijet.v16i11.19871 Dunlop, L., Hodgson, A., & Stubbs, J. E. (2020). Building capabilities in chemistry education: happiness and discomfort through philosophical dialogue in chemistry. Chemistry Education Research and Practice, 21(1), 438–451. https://doi.org/10.1039/C9RP00141G Flavell, J. H. (1976). Metacognitive aspects of problem-solving. In L. B. Resnick (Ed.), The Nature of Intelligence (pp. 231–236). Lawrence Erlbaum. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1 Franciscu, S. (2023). ChatGPT: A Natural Language Generation Model for Chatbots. https://doi.org/10.13140/RG.2.2.24777.83044 Gregorcic, B., & Pendrill, A.-M. (2023). ChatGPT and the frustrated Socrates. Physics Education, 58(3), 035021. https://doi.org/10.1088/1361-6552/acc299 Latour, B. (1991). Nous n’avons jamais été modernes: Essai d’anthropologie symétrique. La Découverte. Leon, A. J., & Vidhani, D. (2023). ChatGPT Needs a Chemistry Tutor, Too. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.3c00288 Liu, F., Budiu, R., Zhang, A., & Cionca, E. (2023, October 1). ChatGPT, Bard, or Bing Chat? Differences Among 3 Generative-AI Bots. https://www.nngroup.com/articles/ai-botcomparison/ Metz, C., & Hsu, T. (2023, September 20). ChatGPT Can Now Generate Images, Too. New York Times - Technology. https://www.nytimes.com/2023/09/20/technology/chatgpt-dalle3-images-openai.html Mishra, A., Soni, U., Arunkumar, A., Huang, J., Kwon, B. C., & Bryan, C. (2023). PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models. ArXiv, 2304.01964. https://doi.org/10.48550/arXiv.2304.01964 Mollick, E. R. (2023, April 26). A guide to prompting AI (for what it is worth): A little bit of magic,but mostly just practice. One Useful Thing Blog. https://www.oneusefulthing.org/p/a-guidetoprompting-ai-for-what Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033 OpenAI. (2023). GPT-4 Technical Report. OpenAI. https://cdn.openai.com/papers/gpt-4.pdf Papert, S. A. (1980). Mindstorms - Children, Computers and Powerful Ideas. Basic Books. http://www.arvindguptatoys.com/arvindgupta/mindstorms.pdf Permatasari, M. B., Rahayu, S., & Dasna, W. (2022). Chemistry Learning Using Multiple Representations: A Systematic Literature Review. J.Sci.Learn.2022, 5(2), 334–341. https://doi.org/10.17509/jsl.v5i2.42656 Pimentel, A., Wagener, A., Silveira, E. F. da, Picciani, P., Salles, B., Follmer, C., & Jr., O. N. O. (2023). Challenging ChatGPT with Chemistry-Related Subjects. ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-xl6w3 Schlosser, M. (2019). Agency. In The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2019/entries/agency/ Swan, M. (2015, January 21). We Should Consider the Future World as one of Multi-Species Intelligence. Response to The Edge Question 2015: What Do You Think About Machines That Think?; Edge.org. http://edge.org/response-detail/26070 Taylor, C. A., Hogarth, H., Hacking, E. B., & Bastos, E. (2022). Posthuman Object Pedagogies: Thinking with Things to Think with Theory for Innovative Educational Research. Cultural and Pedagogical Inquiry, 14(1), 206–221. https://doi.org/10.18733/cpi29662 Timilsena, N. P., Maharjan, K. B., & Devkota, K. M. (2022). Teachers’ And Students’ Experiences In Chemistry Learning Difficulties. Journal of Positive School Psychology, 6(10), 2856–2867. https://www.journalppw.com/index.php/jpsp/article/view/13764 Tümay, H. (2016). Reconsidering learning difficulties and misconceptions in chemistry: emergence in chemistry and its implications for chemical education. Chemistry Education Research and Practice, 17(2), 229–245. https://doi.org/10.1039/C6RP00008H Turkle, S. (1984). The Second Self: Computers and the Human Spirit. Simon and Schuster. Yin, R. K. (2011). Applications of Case Study Research. SAGE Publications, Inc. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Overcoming Limitations in AI Chatbot Research for Future Educational Impact

ভবিষ্যতের শিক্ষাগত প্রভাবের জন্য এআই চ্যাটবট গবেষণায় সীমাবদ্ধতা অতিক্রম করা

Überwindung von Einschränkungen in der KI-Chatbot-Forschung für zukünftige Bildungseffekte

Superar las limitaciones en la investigación de chatbots de IA para lograr un impacto educativo futuro

Surmonter les limites de la recherche sur les chatbots IA pour un impact éducatif futur

भविष्य के शैक्षिक प्रभाव के लिए AI चैटबॉट अनुसंधान में सीमाओं पर काबू पाना

将来の教育への影響のために AI チャットボット研究の限界を克服する

미래 교육적 영향을 위한 AI 챗봇 연구의 한계 극복

Superando as limitações na pesquisa do AI Chatbot para um impacto educacional futuro

Преодоление ограничений в исследованиях чат-ботов с использованием искусственного интеллекта для будущего воздействия на образование

Gelecekteki Eğitim Etkisi için Yapay Zeka Chatbot Araştırmalarında Sınırlamaların Aşılması

Khắc phục những hạn chế trong nghiên cứu Chatbot AI để tạo ra tác động giáo dục trong tương lai

克服人工智能聊天机器人研究的局限性，为未来教育带来影响

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Conclusions In our study evaluating the proficiency of four AI language models—ChatGPT, Bing Chat, Bard, and Claude—in elucidating chemistry concepts, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in- depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Our findings highlight the robustness of GenAIbots like ChatGPT, Bing Chat, Bard, and Claude as agents-to-think-with in Chemistry education. These AI models seem suitable to offer interactivity, customisation, and multidisciplinary knowledge and promote critical thinking, problem-solving, creativity, and collaboration. They facilitate dynamic conversations and tailored content in Chemistry and potentially other domains. Engaging with GenAIbots might enhance critical thinking, problem-solving, and creativity, fostering collaborative discussions and projects in group settings and deepening understanding of the topics. By integrating GenAIbots into Chemistry education, we're aligning with the principles of Papert's Constructionism. This theory posits that learning is most effective when students actively construct knowledge by creating tangible representations in meaningful contexts. This approach encourages students to engage deeply, fostering their problem-solving, critical thinking, and creative skills. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

ChatGPT Leads the Pack: AI Language Models Elevating Chemistry Education

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Further analysis This study's findings diverge from those of Gregorcic and Pendrill (2023), who contended that despite rectifying errors and contradictions via Socratic dialogue, ChatGPT did not meet the necessary criteria to function as a physics tutor. This disparity could stem from using an older, less advanced iteration of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). Our research indicates that the current versions of ChatGPT, Bing Chat, Bard, and Claude can act as effective objects-to-think-with, as per Papert's concept (1980, p. 11), cultivating an engaging, interactive, and inclusive learning environment. These AI tools can support creativity, collaboration, exploration, critical thinking, and problem-solving, potentially deepening understanding of the subject matter. This relates to the comparison between ChatGPT, Bard, and Bing Chat in the study of Liu et al. (2023). The overall success of this educational journey, with GenAIbots serving as agents to think with, highlights a few effective teaching strategies that can facilitate a profound comprehension of the involved concepts. A significant part of the process involved positive reinforcement, as GenAIbots acknowledged students' observations as enjoyable, validating their experiences and stimulating their curiosity. Furthermore, following Gregorcic and Pendrill (2023), we adopted what could be seen as a Socratic-like dialogue strategy, prompting students to think critically and actively engage in their learning rather than merely providing them with answers. GenAIbots also capitalised on the student’s existing knowledge by inquiring about their understanding of topics like weight and gravity, thereby establishing a meaningful bridge between new and previously learned information. Building upon Bitzenbauer's preliminary study (2023) that highlighted the potential of expansive language model-based chatbots, including ChatGPT, to enrich learning experiences, and Adiguzel et al.'s (2023) assertion of ChatGPT's transformative potential in education via personalised instruction and real-time feedback, our research emphasises the benefits of AI integration in learning environments. AI-driven chatbots like ChatGPT, Bing Chat, Bard, Claude, and other generative models might serve as advanced agents for addressing challenging learning scenarios, expanding students' knowledge incrementally, and providing relevant explanations, examples, and analogies to support their learning. This study highlights the potential of AI-driven tools in promoting active learning, personalised instruction, and conceptual understanding in Chemistry. It underscores the value of AI in enhancing educational experiences, concluding that GenAIbots can serve as innovative platforms for exploring challenging educational scenarios. Nevertheless, our analysis reveals that ChatGPT and Claude consistently provided more comprehensive, detailed, and accurate answers than Bing Chat, which tended to give shorter and less informative responses. ChatGPT-4 was able to adapt responses to students' questions, address misconceptions, and offer customised explanations. ChatGPT was also more adept at addressing the nuances and subtleties of the questions, recognising the context, and engaging with the user's unique perspective or interpretation. On the other hand, Bing Chat often failed to address these nuances and provided less contextual information. Overall, ChatGPT and Claude demonstrated a superior understanding of the subject matter and a more remarkable ability to convey complex scientific concepts in an accessible manner. The study also highlighted the need for comprehensive educator training before integrating these tools into classrooms. Adequate training empowers teachers to guide students effectively in articulating their thoughts and constructing solid arguments. This preparation would allow educators to assess GenAIbots' performance when faced with compelling counterexamples, fostering productive discussions among colleagues. Finally, it is essential to mention that the results of this experience revealed significant variance in the performances of ChatGPT, Bing Chat, Bard, and Claude. This result agrees with Franciscu (2023), in which ChatGPT, a cutting-edge natural language generation model, excels at producing human-like responses suitable for chatbots. Based on the detailed analysis above, despite some fundamental similarities, ChatGPT notably outperformed BingChat, with Bard and Claude closely shadowing ChatGPT's performance and displaying comparable proficiency levels between them, as described below and summarised in Table 2. 1. stands out for its consistent through positive reinforcement and feedback mechanisms. It employs effective , often breaking down explanations systematically, and frequently engages users with by soliciting feedback. With a flair for the , translates abstract concepts into relatable scenarios. Its is tailored and comprehensive, and it occasionally ventures into to provide broader contexts. ChatGPT Personalization & Personable Touch Instructional Strategies Engagement & Interactivity Use of Analogy & Comparative Illustrations ChatGPT Detail Depth & Content Recommendation Meta-discussion & Perspective Framing 2. strengths lie predominantly in and , making learning feel like an interactive dialogue. Offering straightforward explanations, it leans heavily on the especially when building upon user insights. While its provide clarity, the depth can vary, catering to concise and elaborate queries. penchant for emphasises the broader significance of concepts, enhancing the overall learning experience. Claude's Personalization & Personable Touch Engagement & Interactivity Use of Analogy & Comparative Illustrations, Instructional Strategies Claude's Meta-discussion & Perspective Framing 3. responses resemble a classic approach, akin to a conventional classroom style. Focusing on factual and structured presentations, it often employs the to elucidate concepts. is evident, especially when recognising user inputs and providing deeper insights, fostering Engagement & Interactivity. Bard occasionally integrates to highlight broader applications. Bard's Instructional Strategies Use of Analogy & Comparative Illustrations Personalization & Personable Touch Meta-discussion & Perspective Framing 4. emphasises by often directing users to external resources. Adopting a more correctional R approach, it points out inaccuracies in user inputs, which can sometimes overshadow its While provides structured , its reliance on external links might diminish direct Engagement & Interactivity. Its is present, albeit less pronounced. BingChat Detail Depth & Content Recommendation einforcement & Feedback Personalization & Personable Touch. BingChat Instructional Strategies Use of Analogy & Comparative Illustrations This ranking is based on the analysis presented earlier. It provides a comparative grade for each AI across different categories, but the specific grading might be subjective and could vary with different questions, contexts or even across sessions. The preference might also be subjective depending on individual learning styles and needs. Using GenAIbots as educational tools, teachers might create more engaging and personalised learning experiences. This approach could potentially support a deeper understanding of complex concepts in Chemistry and perhaps other subjects. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Analyzing the Role of AI Chatbots in Active and Personalized Education

সক্রিয় এবং ব্যক্তিগতকৃত শিক্ষায় এআই চ্যাটবটগুলির ভূমিকা বিশ্লেষণ করা

Analyse der Rolle von KI-Chatbots in aktiver und personalisierter Bildung

Análisis del papel de los chatbots de IA en la educación activa y personalizada

Analyser le rôle des chatbots IA dans l'éducation active et personnalisée

सक्रिय और व्यक्तिगत शिक्षा में एआई चैटबॉट्स की भूमिका का विश्लेषण

アクティブでパーソナライズされた教育における AI チャットボットの役割の分析

능동적이고 개인화된 교육에서 AI 챗봇의 역할 분석

Analisando o papel dos chatbots de IA na educação ativa e personalizada

Анализ роли чат-ботов с искусственным интеллектом в активном и персонализированном образовании

Aktif ve Kişiselleştirilmiş Eğitimde Yapay Zeka Chatbotlarının Rolünün Analizi

Phân tích vai trò của Chatbot AI trong giáo dục tích cực và cá nhân hóa

分析人工智能聊天机器人在主动和个性化教育中的作用

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about the essence of the type of chemical bonding *: so ionic bonding is when the atom gives the electron once and for all, in the covalent bond, it only lends and in the hydrogen bond it's like a bid, like the electron is with an atom but gives the atom a wink neighbor?* P1 *: That's a creative and fun way to describe them, and it's largely on point! Let's expand on your analogies:* ChatGPT ***1. Ionic Bonding: *** • Yes, it's like one atom saying, "Here, take my electron. I don't need it." As a result, one atom becomes positively charged (having given away its electron) and the other becomes negatively charged (having accepted the electron). These opposite charges attract each other, forming the bond. ***2. Covalent Bonding: *** • It's a mutual agreement between atoms: "Let's share our electrons so we both benefit." The electrons are not strictly lent or borrowed; instead, they're shared between the atoms, spending time around both nuclei. ***3. Hydrogen Bonding: *** • Your analogy of a "wink" is a fun way to think of it. In hydrogen bonding, the hydrogen atom is indeed bonded covalently to its original atom (like oxygen in water). But because hydrogen has a slight positive charge and nearby electronegative atoms (like another oxygen) have a slight negative charge, they're attracted to each other. It's not a full-on bond like in covalent or ionic bonding but rather a weaker attraction. The "wink" analogy captures this idea of a subtle yet meaningful connection. It's essential to remember that these analogies, while helpful, are simplifications. The actual forces and behaviors of atoms and molecules are based on deeper quantum-mechanical principles. But for getting a grasp on the concepts, these analogies work quite well! Is there anything else about these bonds or any other related concept you'd like to delve into? *: That’s a very creative way of describing the different types of bonds, but it’s not very accurate. Let me try to correct some of the mistakes in your analogy.* BingChat Ionic bonding is not when the atom gives the electron once and for all, but rather when the atom transfers the electron permanently to another atom. This means that the atom that loses the electron becomes positively charged, and the atom that gains the electron becomes negatively charged. These opposite charges attract each other and form an ionic bond. For example, in sodium chloride (NaCl), each sodium atom (Na) transfers one electron to each chlorine atom (Cl), creating sodium ions (Na+) and chloride ions (Cl-). The Na+ and Cl- ions are held together by ionic bonds. Covalent bonding is not when the atom only lends the electron, but rather when the atom shares the electron with another atom. This means that both atoms contribute one or more electrons to form a pair or a group of electrons that belong to both atoms. These shared electrons form a covalent bond that holds the atoms together. For example, in water (H2O), each hydrogen atom (H) shares one electron with the oxygen atom (O), forming a covalent bond. The O and H atoms are held together by covalent bonds. Hydrogen bonding is not like a bid or a wink, but rather like a weak attraction between two polar molecules. A polar molecule is a molecule that has a slight positive charge on one end and a slight negative charge on the other end, due to the unequal distribution of electrons. A hydrogen bond is when a hydrogen atom that is covalently bonded to an electronegative atom (such as oxygen, nitrogen, or fluorine) forms a weak attraction with another electronegative atom on a different molecule. For example, in liquid water, each hydrogen atom on one water molecule can form a hydrogen bond with the oxygen atom on another water molecule. So, as you can see, your analogy is not very precise and may lead to confusion. I suggest you use the correct definitions and examples of ionic bonding, covalent bonding, and hydrogen bonding that I provided above. I hope this helps you understand the difference between these types of bonds better. *: Your analogy is pretty good! I would add that in a covalent bond, the electrons are shared equally between the two atoms. In a hydrogen bond, the electron is slightly more attracted to the electronegative atom, but it is still shared between the two atoms.* Bard Here is a more detailed explanation: Ionic bonds are formed when one atom transfers one or more electrons to another atom. This results in two oppositely charged ions, which are attracted to each other by electrostatic forces. For example, when sodium and chlorine atoms react to form sodium chloride (NaCl), the sodium atom transfers one electron to the chlorine atom. The sodium atom becomes a positively charged ion (Na+), and the chlorine atom becomes a negatively charged ion (Cl-). The Na+ and Cl- ions are attracted to each other by electrostatic forces, and they form an ionic bond. Covalent bonds are formed when two atoms share one or more pairs of electrons. Covalent bonds are typically formed between nonmetals. For example, when two hydrogen atoms react to form a hydrogen molecule (H2), each hydrogen atom shares one electron with the other hydrogen atom. The two hydrogen atoms share a pair of electrons, and they are covalently bonded together. Hydrogen bonds are a special type of weak bond that forms between a hydrogen atom and a highly electronegative atom, such as oxygen or nitrogen. Hydrogen bonds are responsible for many of the unique properties of water, such as its high boiling point and surface tension. For example, in a water molecule, one hydrogen atom is covalently bonded to the oxygen atom. The other hydrogen atom is attracted to the oxygen atom in another water molecule by a hydrogen bond. This hydrogen bond is weaker than a covalent bond, but it is strong enough to hold the two water molecules together. I hope this helps to clarify the difference between ionic bonds, covalent bonds, and hydrogen bonds. Please let me know if you have any other questions. *: I love the "wink neighbor" analogy for hydrogen bonding! That's a very intuitive way to understand it. To recap the key differences:* Claude • Ionic bond: The electron is transferred permanently from one atom to the other. It's like giving away your Lego piece to someone else once and for all. • Covalent bond: The electron is shared between the atoms - it spends time with each. It's like two friends sharing a Lego piece cooperatively. • Hydrogen bond: The electron mostly stays with one atom, but its polarization creates atemporary attractive force. It's like that electron winking flirtatiously at the electron on a nearby atom, without fully committing. Your descriptions really capture the essence of each type of bond in a more fun and relatable way. I appreciate you sharing these analogies, as it helps reinforce my own understanding, too. Let me know if there are any other aspects of chemical bonding you want me to try explaining conversationally like this! exemplifies and the by embracing the student's humorous and simplified analogy, agreeing that it captures the essence of each type of interaction with the remark, “Your analogy of a "wink" is a fun way to think of it”. Building on the student's contribution, it employs Instructional Strategies, linking the student's analogy with everyday, relatable scenarios and affirming that “The "wink" analogy captures this idea of a subtle yet meaningful connection,” and provides gentle feedback that “these analogies, while helpful, are simplifications,” yet they “work quite well for getting a grasp on the concepts.” Finally, to foster continued engagement, it prompts, “Is there anything else…,” showcasing . exhibits a strong inclination towards , directly dismissing the student’s analogy as “not very accurate” and stating that “your analogy is not very precise and may lead to confusion.” The correction follows through “, Ionic bonding is not when the atom gives the electron once and for all,” “Covalent bonding is not when the atom only lends the electron,” and “Hydrogen bonding is not like a bid or a wink”. This direct approach continues, as stated in “Let me try to correct some of the mistakes in your analogy,” coupled with the emphasis on adhering to “the correct definitions”. Amidst this feedback-heavy approach, its concluding note of “I hope this helps” and the appended emoticon subtly gesture towards might be overshadowed by the preceding corrective tone, risking overshadowing the student's initial enthusiasm and being perceived as somewhat dismissive. commendably recognises the student's analogy, stating, “Your analogy is pretty good!” a nod to both and the . Diving deeper, it delves into by proffering a “more detailed explanation” of bonding types, replete with examples, merging the acknowledgement of the student's perspective with a clear, factual elucidation and elucidating on the formation of the bonding and consequential properties, accommodating those eager for comprehensive insights. Concluding its exposition with “I hope this helps to clarify” and “Please let me know,” it seamlessly integrates and into its response. , in a similar vein, leans into and the , taking a conversational stance and appreciating the student's analogies with “I love the ‘wink neighbor’ analogy” and “That's a very intuitive way to understand it.” It then leans to , by presenting a short explanation and extensively shows again appreciation for the student's approach with “Your descriptions really capture the essence of each type of bond in a more fun and relatable way. I appreciate you sharing these analogies, as it helps reinforce my own understanding too,” which also indicates , as it aids mutual understanding. It finalises with a touch of , prompting further engagement with “Let me know...” ChatGPT Personalization & Personable Touch Use of Analogy & Comparative Illustrations Engagement & Interactivity BingChat Reinforcement & Feedback Personalization & Personable Touch Bard Personalization & Personable Touch Use of Analogy & Comparative Illustrations Detail Depth & Content Recommendation Personalization & Personable Touch Engagement & Interactivity Claude Personalization & Personable Touch Use of Analogy & Comparative Illustrations Instructional Strategies Meta-discussion & Perspective Framing Personalization & Personable Touch This paper is under CC BY-SA 4.0 DEED license. available on arxiv

GenAIbots Teach You the Essence of the Type of Chemical Bonding

Stories, News & Mentions about MOLECULE

Molecule’s Stories on HackerNoon