I found the field of Engineering; Environmental Sciences & Ecology very interesting. Saw the article Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning published in 2020. COA of Formula: C7H4Cl2O2, Reprint Addresses Zhang, HC (corresponding author), Case Western Reserve Univ, Dept Civil & Environm Engn, Cleveland, OH 44106 USA.. The CAS is 50-84-0. Through research, I have a further understanding and discovery of 2,4-Dichlorobenzoic acid
Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent-adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23-0.31 versus 0.23-0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.
COA of Formula: C7H4Cl2O2. About 2,4-Dichlorobenzoic acid, If you have any questions, you can contact Zhang, K; Zhong, SF; Zhang, HC or concate me.
Reference:
Chloride – Wikipedia,
,Chlorides – an overview | ScienceDirect Topics