Posted in D L Williams, Education, Physical Chemistry

Brainstorm Session on 3D Printer Waste

IMG_3265-1.JPG

Friends, Romans, Countrymen, lend me your ideas. Look at the picture of the dual extruder tip cleaning fences and give me your thoughts on what they could be used for.

Background: I have recently added a 3D printer to my lab to prototype chemical laboratory accessories.

I purchased a dual extrusion Makerbot and I love it. I will blog a review of its performance sometime soon.

Technical Details: Aside from the waste produced from being a noob at 3D printing, there is also a lot of built in waste from raft and tip cleaning fence material. Turning off rafts and supports does not save filament because these help ensure a successful print. If the print fails without rafts and supports, then you have still wasted a lot of filament.

So get those creative juices flowing, and comment on what you would use these tiny little fences for. Toys? Rear view mirror bling? Let me know.

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Posted in Contact Angle, D L Williams, FTIR, Hansen Solubility Parameters, Physical Chemistry, Raman, Solubility, Solvent Blending, Spectroscopy, UV-VIS-NIR, XPS

Corporate Research Funding in Uncertain Times

Some points that describe the current R&D funding climate:

  • Continued uncertainty in corporate R&D hiring
    US non-financial corporate cash holdings rose to $1.24 trillion at the end of 2011 according to Moody’s. One reason among many is a reluctance to hire until the uncertainty surrounding benefits costs is reduced.
  • Tightening of government funding of university R&D
    The US government still funds a significant amount of chemical research, but competition for those funds is increasing greatly. The growing deficit must eventually have an impact on the availability of funds for chemical research.

As a physical chemist, I am partial to APPLIED chemistry research, and the interactions I have had with corporations and government contractors have been enjoyable and fruitful for both parties.

I have prepared this blog post and my new “Sponsors Page” on my university website to actively address the R&D needs of corporations and government contractors.  Many of these entities are under a hiring freeze, and yet, their chemistry-related problems continue unaddressed.

When I worked for a government contractor, I dealt with these issues:

  • “I could solve this problem in 6 months, if I didn’t have to support production, also.”
  • “I’d love to hire someone to research this and other issues but
    1) we are under a hiring freeze,
    2) we don’t have the budget for a whole person (1.0 FTE),
    3) we can afford the salary but are reluctant to commit to an unknown fringe benefit committment,
    4) we can afford a science temp, but we need a Ph.D. chemist.”
  • “Maybe a university researcher could help, but there’s no telling what an Ivory Tower Pinhead is going to spend our money on.  And, what would we have to show for it?”

To quote a recent President, “I feel your pain.”  But not all residents of the Ivory Tower are Pinheads.  Here are the benefits of funding an APPLIED-SCIENCE-MINDED university professor and his students to address your problem.

  • Academic salaries for Ph.D. chemists ($70k, 2012 median) are 65% of that in Industry ($107k, 2012 median) according to the ACS Employment Survey, so renting a brain is potentially cheaper than buying one.  Often these are 9-month salaries, but this annualizes to $93k, which is $14k less than the industry median 12-month salary.
  • Academic chemists are able to spend 100% of their effort on your problem during the summer months.  If the median salary of $70k is for 9-months, then funding this scientist for three full months in the summer is only $23k.  Universities tack on varying amounts of overhead and fringe benefits costs to this number so the actual costs will be more like $40k ($85 / hr all-inclusive).  This is still a very reasonable amount for 3-months of a PhD chemist’s time.
  • Academic institutions have an amazing array of instrumentation that your company could not justify purchasing.  The overhead costs tacked onto the academic chemist’s labor rate is the price of admission to the instrumentation lab or computational facility.  Our lab charges consumables costs on a per-day or per-sample basis in the range of $20.  This may seem to add up, but so do the costs of solvents, vials, etc.
  • Academic institutions are FULL of eager chemistry majors who LOVE to study research problems that are “real life”.  These students are also inexpensive when compared to hourly chemical technicians.  A typical student will have a fully-burdened (with overhead) rate of $20 per hour all-inclusive.  These students will graduate with a working knowlege of your industry and will be excellent prospects for future hires.

The number-one factor to consider is the principal investigator (PI).  Does he or she understand your problem?  Have they done similar work in the past?  I have turned down funding because I did not think I could deliver value to the sponsor.  Find someone who understands your terms, your culture, your requirements, and the practical aspects of implementing the ideas proposed.

If your interests are in any of these areas, I’d love for you to contact me.

  • Cleanliness verification, contact angle measurements, coupon tests
  • Solvent properties, surface tension and hydrostatic densities, Hansen solubility parameters vs Hildebrand solubility parameters
  • Solvent blending, solvent blend prediction, miscibility
  • Solvent substitution, reduction of hazards, reactivity, ozone depletion potential, or global warming potential
  • Material compatibility, polymer stress cracking, polymer swell, polymer processing solvents
  • Recrystallization and crystal morphology control based upon non-solvent interactions
  • High-explosive detection, solubility, modeling, spectroscopy, recrystallization, precipitation, and PBX production/processing
  • Spectral assignments and predictions (FTIR, Raman, UVVIS, XPS)
  • Computational chemistry, ab initio, density functional theory, quantitative structure property relationships (QSPR/QSAR)
  • Six-Sigma Blackbelt – consulting services

There are ways to continue innovating in the current business climate.  I’d love to help if I can.

-Darren

Posted in D L Williams, Education, Physical Chemistry

Potential Source of the Fukushima Daiichi 1 Explosion

Here is my personal analysis of the Fukushima Daiichi 1 explosion that occurred March 12, 2011. It is possible that the cooling water level in the spent fuel storage pool dropped enough to expose the fuel elements. This could generate hydrogen gas, and is a potential source of the explosion. Hat tip to the Nuclear Energy Institute for providing an image of the reactor design and secondary containment area.

I have created a Prezi to walk you through my thoughts.

I welcome your thoughts in the comments area.

Thanks for watching, and keep praying for the Japanese people as they continue to battle the after effects of this earthquake.

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! http://boinc.bakerlab.org/rosetta/)

Posted in D L Williams, Education, Physical Chemistry

Pchem4u is going to participate in Post a Week 2011

Postaweek2011 

WordPress is issuing a challenge (to drive traffic of course), and I am going to go for it.  Here are the pchem4u blog plans for the 2011 calendar year.

  1. Post short communications on the research interests of the blog author.
  2. Post a “week in the year” of the Physical Chemistry Curriculum at Sam Houston State University.
  3. Post an occasional commentary on pchem-related current events.
Posted in Contact Angle, D L Williams, Hansen Solubility Parameters, Physical Chemistry

Contact Angle Standards and Measurement System Evaluation

As my research group began to enter into the world of precision cleaning, we needed to come up to speed on contact angle measurement.  In doing so, we met Anselm Kuhn who was a great help and mentor.  Together, we produced an inexpensive way to standardize contact angle measurements using spherical ruby lenses.  We also evaluated the many freely-available contact angle measurement programs that act as plugins for ImageJ.  This work was published in the German metal finishing journal Galvanotechnik.

Williams, D. L.; Kuhn, A. T.; Amann, M. A.; Hausinger, M. B.; Konarik, M. M.; Nesselrode, E. I. Computerized Measurement of Contact Angles, Galvanotechnik, 101, 2502-2512, (2010)

Abstract

Measurement of contact angles often provides valuable information as to the cleanliness of a surface as well as the ease of wetting of a surface with a coating such as paint or other organic species. Previous methods based on use of a sessile drop were subject to considerable operator error. In order to minimise such errors, the computer-based analysis of drop shape has been developed. The use of such software which is Windows-compatible and easy to learn, is described, giving results where operator-error is minimised. The method has considerable potential for Quality Control in surface finishing.

 

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.