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Theory Suite Retreat (Granby, CO) November 10 – 12th 2023

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Group Celebrates Collin and Hojin


Kim Group Attends ACS in San Francisco


Group Receives NSF Award

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Group publishes work aiding process monitoring in biomass upgrading

Congrats to group members Mohammed and Yeonjoon for their work on the prediction of bio-oil output composition being published in ACS Sustainable Chemistry and Engineering!

Short Overview:
In collaboration with NREL, we developed a preliminary simple ML model (random forest) capable of translating a mass spectra (MS) to a set of chemically-interpretable compositional descriptors, the Paraffins, Isoparaffins, Olefins, Naphthenes, and Aromatics (PIONA) fractions. The key spectral features were extracted from the MS, and these features are used as the input of the random forest model which outputs the predicted PIONA values. These studies are expected to provide generally applicable strategies to predict bio-oil output compositions.


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Group celebrates three new PhD graduate students

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Group Celebrates Sabari and Chris!

Congrats Chris and Sabari!

The Kim group celebrates Chris passing his preliminary exam and Sabari passing his literature seminar.

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Dr. Kim Presents to Universitat Rovira i Virgili (CiTQ Multidisciplinary Seminar Series)

The overarching goal of our group is to develop new methods to extract sustainable fuels and chemicals from plants. Our approach has been to develop and apply computational tools to both biological and chemical conversion processes as part of an iterative ‘model-validate-predict’ design process for de novo catalysts. With its high carbon and hydrogen content, lignocellulosic biomass presents an alternative to petroleum as a nearly carbon-neutral precursor to upgraded liquid fuels. I will present some representative results in designing new catalysts for biological and chemocatalytic processes of biomass.

Our group has introduced a “Fuel property first” design approach to reduce emissions and increase performance. Traditional approaches for developing these mechanistic models require many years for each new molecule, a pace that is poorly suited to the large-scale search for new bioderived blendstocks. We have developed a quantitative structure-property relationship (QSPR) model for sooting tendency based on the experimental yield sooting index (YSI), developed by collaborators at Yale (Prof. L. Pfefferle and Dr. C. McEnally). This is the first fuel property predictive tool using ML (Machine Learning) approaches in combustion research. We have started to build kinetic mechanisms of soot precursor formation during combustion using DFT and flow reactor experiments (collaboration with Dr. R. McCormick, NREL) to show how the fundamental chemistry affects this practical engineering problem..