Topic 2) Do Atoms Matter?
The Concept of Models
What is a Model?
In science, a model is a representation of an idea, an object or even a process or a system that is used to describe and explain phenomena that cannot be experienced directly. Models are central to what scientists do, both in their research as well as when communicating their explanations. Models are a mentally visual way of linking theory with experiment, and they guide research by being simplified representations of an imagined reality that enable predictions to be developed and tested by experiment.
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Why do we need Scientific Models?
Models have always been important in science and continue to be used to test hypotheses and predict information. Often they are not accurate because the scientists may not have all the data. It is important that scientists test their models and be willing to improve them as new data comes to light. Think about our Model of the Atom. In 1897, J J Thomson proposed the "Plum Pudding" model of the atom after he discovered the electron. Then from 1912 to 1913, Rutherford and Bohr further developed the model of the atom after proton and neutron were discovered. Now we have the Quantum Model of the Atom which was proposed in the 1930's. In this model, the electrons do not exist as particles of matter but as waves of matter that extend around the atom.
What are their limitations?
A model is a description of natural phenomenon that scientists can use to make predictions. A good model is both as accurate as possible and as simple as possible, which makes it not only powerful but also easy to understand. However, no matter how good they are, models will almost always have limitations.
a) Most models can't incorporate all the details of complex natural phenomena. For example, when measuring distances around the Earth it's convenient to model the Earth as a sphere, but this doesn't incorporate variations in distance because of mountain ranges, valleys and other topological features the traveler must traverse.
b) Most models include some approximations as a convenient way to describe something that happens in nature. These approximations are not exact, so predictions based on them tend to be a little bit different from what you actually observe -- close, but not bang on. In quantum mechanics, for example, there are no exact solutions to the Schrodinger equation for atoms from helium onward; exact solutions exist only for hydrogen. Consequently, physicists use approximations for higher elements. These approximations are good, but they are approximations nonetheless.
c) Sometimes a model can be made more accurate but at the expense of simplicity. In cases like these, the simpler model may actually be superior, because it gives you a way to visualize a process so you can understand it and make predictions about it. In chemistry, for example, structural formulas and ball-and-stick models are unrealistic depictions of molecules; they completely ignore what chemists know from quantum mechanics about the nature of matter at the subatomic level. Nonetheless, they are simple, easy to draw and offer a wealth of insights into molecular structure and properties in a way that's easy to visualize and understand.
d) Ultimately, models are subject to some trade-offs. You want as much predictive power as possible. At the same time, you also want the model to be as simple as possible. Nature is indifferent to the human need for simplicity and ease of comprehension, however, and many natural phenomena are complex. Just think, for example, about the chain of biochemical processes that take place merely in order to relay information from the photoreceptors in your eye to the visual cortex of your brain. If you try to incorporate everything that actually happens into a model, it becomes unwieldy and difficult to use. In the end you find that you rely to some degree on approximations and conceptual frameworks that make a process easy to visualize but don't necessarily reflect the true nature of reality.
a) Most models can't incorporate all the details of complex natural phenomena. For example, when measuring distances around the Earth it's convenient to model the Earth as a sphere, but this doesn't incorporate variations in distance because of mountain ranges, valleys and other topological features the traveler must traverse.
b) Most models include some approximations as a convenient way to describe something that happens in nature. These approximations are not exact, so predictions based on them tend to be a little bit different from what you actually observe -- close, but not bang on. In quantum mechanics, for example, there are no exact solutions to the Schrodinger equation for atoms from helium onward; exact solutions exist only for hydrogen. Consequently, physicists use approximations for higher elements. These approximations are good, but they are approximations nonetheless.
c) Sometimes a model can be made more accurate but at the expense of simplicity. In cases like these, the simpler model may actually be superior, because it gives you a way to visualize a process so you can understand it and make predictions about it. In chemistry, for example, structural formulas and ball-and-stick models are unrealistic depictions of molecules; they completely ignore what chemists know from quantum mechanics about the nature of matter at the subatomic level. Nonetheless, they are simple, easy to draw and offer a wealth of insights into molecular structure and properties in a way that's easy to visualize and understand.
d) Ultimately, models are subject to some trade-offs. You want as much predictive power as possible. At the same time, you also want the model to be as simple as possible. Nature is indifferent to the human need for simplicity and ease of comprehension, however, and many natural phenomena are complex. Just think, for example, about the chain of biochemical processes that take place merely in order to relay information from the photoreceptors in your eye to the visual cortex of your brain. If you try to incorporate everything that actually happens into a model, it becomes unwieldy and difficult to use. In the end you find that you rely to some degree on approximations and conceptual frameworks that make a process easy to visualize but don't necessarily reflect the true nature of reality.
The Importance of Patterns
Why are we interested in PATTERNS???
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