Physical/ chemical models

The tools we created in Chapter 3, Physical/Chemical Models, form the core of the fitting algorithms of this chapter. The model defines a mathematical function, either explicitly (e.g. first order kinetics) or implicitly (e.g. complex equilibria), which in turn is quantitatively described by one or several parameters. In many instances the function is based on such a physical model, e.g. the law of mass action. In other instances an empirical function is chosen because it is convenient (e.g. polynomials of any degree) or because it is a reasonable approximation (e.g. Gaussian functions and their linear combinations are used to represent spectral peaks). 

Consider the data in Figure 5-7, spectra that were collected during the progress of the reaction A — B. For the present application, not the whole reaction was covered. The first spectrum is taken a while after the reaction began and the last spectrum before the reaction reached completion. Thus, the data include neither the pure spectrum of the starting material A, nor the spectrum of the product B. The spectrum of pure A is somewhere before the first measured spectrum and the spectrum of pure B somewhere past the last measured spectrum. But, where exactly Of course it is not possible to define the spectra perfectly, but it is possible to be more precise than the above statement. As a reminder, fitting the data with a physical/chemical model will produce the perfect result but we are now in the chapter on... 

The cloud chemistry simulation chamber (5,6) provides a controlled environment to simulate the ascent of a humid parcel of polluted air in the atmosphere. The cloud forms as the pressure and temperature of the moist air decreases. By controlling the physical conditions influencing cloud growth (i.e. initial temperature, relative humidity, cooling rate), and the size, composition, and concentration of suspended particles, chemical transformation rates of gases and particles to dissolved ions in the cloud water can be measured. These rates can be compared with those derived from physical/chemical models (7,9) which involve variables such as liquid water content, solute concentration, the gas/liquid interface, mass transfer, chemical equilibrium, temperature, and pressure. 

When a molecule takes part in a reaction, it is properties at the molecular level which determine its chemical behaviour. Such intrinsic properties cannot be measured directly, however. What can be measured are macroscopic molecular properties which are likely to be manifestations of the intrinsic properties. It is therefore reasonable to assume that we can use macroscopic properties as probes on intrinsic properties. Through physical chemical models it is sometimes possible to relate macroscopic properties to intrinsic properties. For instance 13C NMR shifts can be used to estimate electron densities on different carbon atoms in a molecule. It is reasonable to expect that macroscopic observable properties which depend on the same intrinsic property will be more or less correlated to each other. It is also likely that observed properties which depend on different intrinsic properties will not be strongly correlated. A few examples illustrate this In a homologous series of compounds, the melting points and the boiling points are correlated. They depend on the strengths of intermolecular forces. To some extent such forces are due to van der Waals interactions, and hence, it is reasonable to assume a correlation also to the molar mass. Another example is furnished by the rather fuzzy concept nucleophilicity . What is usually meant by this term is the ability to donate electron density to an electron-deficient site. A number of measurable properties are related to this intrinsic property, e.g. refractive index, basicity as measured by pK, ionization potential, HOMO-LUMO energies, n — n ... 

Demonstrating that it is possible to form molecules in the ISM that may subsequently assemble to form the building blocks of life on Earth (or any other planet) is not in itself evidence that the origins of life lie in the ISM. It is also necessary to know how such molecules might be transported to the planet. Our knowledge of how stars and planets form relies on physical/chemical models but we believe we know the basic mechanisms. Within the ISM... 

In principle, it would be possible to determine the outcome of any chemical reaction if (a) The reaction mechanisms were known in detail, i.e. if all equilibrium constants and all rate constants of intermediary steps were known and (b) the initial concentrations of the reactants and the activity coefficients of all species involved were perfectly known. However, this is never the case in practice. It would be impossible to derive such a model by deduction from physical chemical theory without introducing drastic assumptions and simplifications. A consequence of this is, that the precision of any detailed prediction from such hard models will be low. In addition to this, physical chemical models rarely take interaction effects between experimental variables into account, which means that, in practice, such models will not be very useful for analysing the influence of experimental variables on synthetic operations. 

By a more elaborate, but equally misleading, one-variable-at-a time analysis, viz. to use an hypothesized reaction mechanism to derive the optimum conditions from physical chemical models. Such models are as a rule univariate models, and as such they cannot account for interaction effects. 

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