Posted in Education, Physical Chemistry, Science Education, Spectroscopy

What is pchem?

Pchem is short for Physical Chemistry. It is hated by all. It is SO bad (the audience asks, “How bad is it?”), that it has it’s own bumper sticker!
Why is pchem so mistreated?
Like a scientific tax accountant, a P-chemist worries about the energetic balance sheet, the gains and losses of energy, the ratio of usable to unusable energy. We pull the thread through all states of matter – liquid, solid, gas, plasma, elastic, plastic, glass, etc.
My favorite subsection of pchem is symmetry and spectroscopy. Spectroscopy is the study of light interacting with matter. And symmetry is used to decipher these interactions. There is no better example of the mathematical beauty of our universe than the unexpected explanatory power of group theory as it applies to absorption and emission of light.
Fireworks, hair dye, crayons, ink, glow sticks, lightning bugs, and all the rest can be understood through pchem – specifically my field of spectroscopy.
There is much more to pchem. If you have made it this far, then you are truly curious. Therefore, I give you the table of contents to a typical pchem textbook. (You will have to “Look Inside” at the Amazon site to view the TOC.)
And, I ask you to share and subscribe to this blog. Comment below with suggestions for posts.
Posted in D L Williams, Education, Physical Chemistry

This Week in Pchem – Energy Minimization Techniques

This week in pchem we are discussing the energy minimization techniques that are used in computational chemistry. The students will build a mock energy function that models the dihedral rotation of 1,2-dichloroethane. Then three methods (The Monte Carlo Method, Newton minimization, and Metropolis simulated annealing) will be employed to solve for the preferred (lowest energy) dihedral angle (a).   

A performance plot will also be generated that shows the lowest energy and its root mean squared deviation RMSD from the known minimum structure (a = 180 degrees).  This plot clearly shows Newton’s propensity to get stuck in local minima.  It also clearly shows that the Monte Carlo method will always find the global minimum, but with increasing inefficiency.  And finally, the Metropolis simulated annealing technique is found to be flexible enough to accurately locate the minimum energy structure every time provided that the step size and temperatures are “tuned”. 

Stay “tuned” for a planned video of the spreadsheet in action.

You can participate!  Download the Rosetta@home screen saver, and solve protein folding problems in your sleep.  (I have no official connection to the Rosetta at home folks, but their work is great!

Posted in Contact Angle, D L Williams, Hansen Solubility Parameters, Physical Chemistry, Solubility, Solvent Blending

Hansen Solubility Parameters via QSPR

Williams, D. L.; Kuklenz, K. D. A QSAR Model for Predicting Solvents and Solvent Blends for Energetic Materials, Proceedings of the International Annual Conference of ICT, 40th (Energetic Materials), Karlsruhe, Germany, 2/1-2/11, (2009)

Researchers in the paint and polymer industry have shown that the Hansen solubility parameters (HSP) are useful for predicting suitable solvents for the filled-polymer formulation process. To apply this work to the high explosive formulation process, the HSPs of the various energetic materials must be determined or predicted.

A quantitative structure activity relationship (QSAR) was developed that is based upon the output of a density functional theory optimization and frequency calculation (B3LYP/6- 31G(d)//B3LYP/6-31G(d)) using the Gaussian 03 computational package. Structural parameters were extracted from the Gaussian output files of each molecular species. These consisted of the geometric mean of the exact polarizability tensors (α , Å3), the dipole moment (μ, Debye) the highest occupied molecular orbital energy (HOMO, Hartree), the number of each type of atom, and the delta charge (Δq) – defined as the difference between the most negative heteroatom and the most positive hydrogen in the molecule. The value of Δq = 0 was given to hydrocarbons by fiat. A stepwise linear regression was used to determine the correlation of these inputs and mathematical transformations of these inputs to the HSPs for a training set of 54 solvents and nitrated compounds. The resulting QSAR matrix was then applied to 23 energetic materials and precursors yielding the HSPs (δD, δP, δH) in MPa1/2.

The HSPs were also determined for HMX, RDX, PETN, and HNS using experimental solubility data and the group additivity methods of Van Krevelen and Stefanis. The QSAR model outperformed the group additivity methods in matching the experimentally determined HSPs using the Hansen distance parameter (Ra) as the figure of merit.

En route to the QSAR model, a very simple model of molar volume was developed wherein the molar volume is computed directly from the molecular formula CaHbNcOdSePfFgClhBri via the following equation: Vm = 12.53 + 8.77a + 3.96b + 4.87c + 6.12d + 17.22e + 19.45f + 9.70g + 18.66h + 20.74i. The correlation of this equation with the literature values of 183 molecules was 99.67% with an R2 = 0.9847 over a range of 400 cm3/mol.

Posted in Contact Angle, Education, Hansen Solubility Parameters, Physical Chemistry

Surface Tension and Density Determination

When moving into our new chemistry building, my graduate student uncovered a relic made in the 1930’s.  It was a cast-iron Du Nuoy ring tensiometer, but we didn’t know that.  We guessed that it had something to do with surface tension, and he did a literature search.  Up popped a 1930’s paper by Harkins and Jordan on the ring method for determining surface tension. 

Continue reading “Surface Tension and Density Determination”