Diving Deeper into VOCs

By Jessica Lum , Madeline Schultz, Erik Sapper

The identification, measurement, and reduction of volatile organic compounds (VOCs) has been a key motivator in recent coatings research and development efforts. Analytical methods for determining VOC levels in organic coatings continue to improve, as chromatographic and spectroscopic approaches afford a means of quantifying VOC content directly in waterborne as well as solventborne coatings. Heuristic methods for estimating the volatility of formulation components are common but are not extensively validated using quantitative structure-property relationships. Thus, a clearer link between component transport through an evolving coating matrix during curing processes, the bulk volatility of a compound, and the elution and quantification of compounds in a gas chromatograph (GC) still must be made to promote innovation in this area. To address these issues, digital tools such as molecular descriptors and machine learning models are being combined with experimental measurements to better understand the time-dependent mechanistic nature of VOCs in coatings and to enable predictive control over the volatility and in-coating behavior of newly developed formulation components. Here, we present the development and validation of a molecular structure-based neural network for the prediction of response factor for formulation components in a gas chromatography (GC) analysis. This represents an important step in creating large-scale computational design tools that enable in silico formulation, optimization, and end use property prediction of environmentally benign coatings.