首页|Tsinghua University Reports Findings in Machine Learning (Dual cross-linked magn etic gelatin/carboxymethyl cellulose cryogels for enhanced Congo red adsorption: Experimental studies and machine learning modelling)
Tsinghua University Reports Findings in Machine Learning (Dual cross-linked magn etic gelatin/carboxymethyl cellulose cryogels for enhanced Congo red adsorption: Experimental studies and machine learning modelling)
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New research on Machine Learning is th e subject of a report. According to news reporting originating from Beijing, Peo ple's Republic of China, by NewsRx correspondents, research stated, "To achieve highly efficient and environmentally degradable adsorbents for Congo red (CR) re moval, we synthesized a dual-network nanocomposite cryogel composed of gelatin/c arboxymethyl cellulose, loaded with FeO nanoparticles. Gelatin and sodium carbox ymethylcellulose were cross-linked using transglutaminase and calcium chloride, respectively." Our news editors obtained a quote from the research from Tsinghua University, "T he cross-linking process enhanced the thermal stability of the composite cryogel s. The CR adsorption process exhibited a better fit to the pseudo-second-order m odel and Langmuir model, with maximum adsorption capacity of 698.19 mg/g at pH o f 7, temperature of 318 K, and initial CR concentration of 500 mg/L. Thermodynam ic results indicated that the CR adsorption process was both spontaneous and end othermic. The performance of machine learning model showed that the Extreme Grad ient Boosting model had the highest test determination coefficient (R = 0.9862) and the lowest root mean square error (RMSE = 10.3901 mg/g) among the 6 models. Feature importance analysis using SHapley Additive exPlanations (SHAP) revealed that the initial concentration had the greatest influence on the model's predict ion of adsorption capacity. Density functional theory calculations indicated tha t there were active sites on the CR molecule that can undergo electrostatic inte ractions with the adsorbent."
BeijingPeople's Republic of ChinaAsi aArylsulfonic AcidsCongo RedCyborgsEmerging TechnologiesGelatinMachi ne LearningNaphthalenesulfonatesProteinsScleroproteinsSulfur Acids
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