Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log KF and n (R2 > 0.98 for log KF, and R2 > 0.91 for n). The neural network models are based on parameters generally available for carbonaceous sorbents and/or parameters freely available from online databases. A freely accessible graphical user interface is provided.

A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of microplastics controlling this process are elusive. Here, we show that the gastrointestinal bioaccessibility, assessed using an in vitro digestive model, of two model xenobiotics (pyrene, at 391-624 mg/kg, and 4-nonylphenol, at 3054-8117 mg/kg) bound to 18 microplastics (including pristine polystyrene, polyvinyl chloride, polyethylene terephthalate, polypropylene, thermoplastic polyurethane, and polyethylene, and two artificially aged samples of each polymer) covered wide ranges: 16.1-77.4% and 26.4-83.8%, respectively. Sorption/desorption experiments conducted in simulated gastric fluid indicated that structural rigidity of polymers was an important factor controlling bioaccessibility of the nonpolar, nonionic pyrene, likely by inducing physical entrapment of pyrene in porous domains, whereas polarity of microplastics controlled bioaccessibility of 4-nonylphenol, by regulating polar interactions. The changes of bioaccessibility induced by microplastics aging corroborated the important roles of polymeric structures and surface polarity in dictating sorption affinity and degree of desorption hysteresis, and consequently, gastrointestinal bioaccessibility. Variance-based global sensitivity analysis using a deep learning neural network approach further revealed that micropore volume was the most important microplastics property controlling bioaccessibility of pyrene, whereas the O/C ratio played a key role in dictating the bioaccessibility of 4-nonylphenol in the gastric tract.

Z et al. published a paper on machine learning based predictions of organic contaminant sorption onto carbonaceous materials and resins. The authors provide a novel approach to predict concentration-dependent sorption distribution coefficients (KD) to these materials, without the need to link it to any specific isotherm model. This study is a valuable contribution to the field that can stimulate the scientific discussion in the adsorption-modeling community regarding (i) mechanistic assumptions prior to model building, (ii) the parametrization of the model based on these assumptions, (iii) the grouping of data to train the algorithm, and (iv) data filtering strategies. We recently published a paper on a similar topic and are confident that this discussion is valuable to improve the future applicability of machine learning techniques to sorption phenomena.