4.2: Phenomena and Data Patterns
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Kinds of Questions and Explanations
First kind of Question
- Why is thermodynamics such a useful tool throughout all the sciences?
- Why does almost everything you look up in a chemistry or biochemistry reference have a “∆H” associated with it? And why is it so often negative?
- What the heck is “entropy,” anyway? Does the 2nd law of Thermo have anything to do with the perpetual state of my desk? My hectic life?
- Why does Gibbs Energy seem to be such an important idea? And what is it that is “free” about it anyway.”
The kinds of questions listed above will be the focus of much of our short and intense involvement with our Intro Model of Thermodynamics. Combined with the Intro Statistical Model of Thermodynamics, you should be able to feel pretty comfortable regarding your answers to these types of questions. But in addition, you should also be able to make a lot of sense and construct convincing explanations for questions like those in the second category below.
Second kind of Question
- What are the differences when water evaporates from your skin on a warm summer day in California and from the surface of a pot of nearly boiling water in an open pot on the stove?
- What is the temperature of the water vapor as it leaves either surface? Which evaporation process, if either, produces more cooling?
- Which “∆H” is different, if either, in the two cases? Give a numerical prediction for the difference.
We won’t have time to practice constructing answers to too many of these second kinds of questions, but in your own work in advanced science courses and making sense of research papers you are likely to get ample opportunities, provided you get a good foundation with some of the basic underlying ideas. That is the learning outcome we are aiming far.
Phenomena and Data Patterns
Although thermodynamics can be applied to physical systems as large as galaxies and even our entire universe, it really comes into its natural power when we have questions and need answers about what is going on internally in rather controlled environments. This might be in a biochemistry lab or in the cells in the human body. Thermodynamics (and the additional insight and understanding obtained from a statistical approach to entropy and Gibbs energy) connect the real world of empirical data with an extremely powerful approach to analysis that enables scientists to get answers to questions and make numerical predictions when it would seem that it is impossible to get the necessary data to do so. That is, often our questions are of a nature that we would normally believe that the only way they could be answered would by doing specific experiments directly on the phenomenon in question. Frequently, however, these experiments are either impossible to do directly or they are extremely difficult/expensive/illegal, etc. But here is where thermodynamics comes to the rescue.
How does this work? Well, the simple short answer is that for most physical systems, there might be tens, maybe even close to a hundred various physical parameters that characterize that physical system and distinguish it from a similar physical system. But, (here is the short answer) all of these parameters are not independent of each other! Thermodynamics is the tool that tells us how to determine the values of the ones we can’t directly measure (and which no one else has measured and tabulated either) from values of parameters that have been measured and tabulated. At first it seems almost like magic. And to an “outsider” to the field, it is very analogous to magic, in the sense that you know there must “be a trick,” but you can’t imagine what it is. When you are reading and making judgments on scientific papers, you don’t want to be like the “jaw-dropped-open in amazement” spectator, but rather “the insider,” who at least knows where the “trick” is occurring, even if you don’t have the skill to perform it yourself.
A tremendous amount of data has been collected and tabulated on the seemingly infinite variety of biochemical compounds and interactions among these compounds. Thermodynamics is the tool that lets you get maximum benefit from all the work so many thousands of scientists have done on so many different physical systems. So, in a sense, the data patterns of thermodynamics are mostly all of the data that has ever been collected and tabulated on the properties of every imaginable kind of substance or composite material.
In this chapter, we don’t really even scratch the surface. With more and more data becoming accessible on-line, “doing thermodynamics” is getting easier every day. Don’t forget its power when you need it.
Contributors
Authors of Phys7A (UC Davis Physics Department)