Recently I am researching about MULTIPLE LINEAR-REGRESSION; WATER SORPTION COEFFICIENTS; PROPERTY RELATIONSHIP QSPR; FUNCTION NEURAL-NETWORK; SINGLE-WALLED CARBON; POLANYI-BASED MODELS; AROMATIC-COMPOUNDS; ENERGY RELATIONSHIP; PHENOLIC-COMPOUNDS; CONTAMINANTS, Saw an article supported by the National Science FoundationNational Science Foundation (NSF) [CBET-1804708]. Computed Properties of C7H4Cl2O2. Published in AMER CHEMICAL SOC in WASHINGTON ,Authors: Zhang, K; Zhong, SF; Zhang, HC. 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.
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Reference:
Chloride – Wikipedia,
,Chlorides – an overview | ScienceDirect Topics