Interfacial Enrichment of Lauric Acid Assisted by Long-Chain Fatty Acids, Acidity and Salinity at Sea Spray Aerosol Surfaces
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Engineering topics
Allen, Heather C.
Publications and source records attributed to Allen, Heather C..
Non-Equilibrium Nucleation of Rare Earth Metals at Aqueous Interfaces
We conducted mechanical and electrical perturbation experiments on several systems of NdCl 3 aqueous solutions with two phosphonic acids, one as a stable surfactant with high surface activity and another that was fully soluble in aqueous solution. We also conducted experiments with solely the salt in aqueous solution. Our goal was to stimulate surface aggregation at the air/aqueous solution interface that was observable by Brewster angle microscopy, surface tension, and/or surface spectroscopy. We observed changes only with the surface active phosphonic acid. Additional work is needed to further explore confined environments to stimulate surface aggregation events.
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Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry
Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.
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