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Updated: Sep 2, 2026

Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
Published on: June 9, 2023
Neuro-Computational Modeling and Mechanistic Elucidation of Ultrasonication-Induced Pb2+ Sequestration on
Shashi Bhushan Singh1, Ahana Dutta2, Bishnupada Mandal1,2
1Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam781039, India.
Abstract:
Owing to its severe toxicity and environmental persistence, Pb2+ poses a critical threat among waterborne heavy-metal contaminants. To address this challenge, we developed a bimetallic cerium/molybdenum metal-organic framework (CM(1:1)) via an in situ approach for rapid, efficient, and selective Pb2+ removal. The composite exhibited a high removal efficiency, reducing Pb2+ concentrations below the WHO limit within 1 (∼2.83 ppb) and 3 min (∼2.57 ppb) for 10 and 100 ppm, respectively. The adsorption behavior followed the Langmuir isotherm most closely, yielding a maximum uptake capacity of 991.67 ± 20.25 mg/g and confirming a monolayer adsorption mechanism. The closer the conformity with the pseudo-first-order model, the more consistent it is with a fast surface-controlled process. High-resolution XPS detected Pb-bound surface species, accompanied by slight shifts in O 1s and C 1s signals, confirming the involvement of oxygenated groups in Pb2+ uptake. Thermodynamic parameters demonstrated that the Pb2+ uptake was spontaneous and exothermic. The composite also exhibited remarkable selectivity toward Pb2+ in the presence of competing ions (Na+, K+, Mg2+, Cd2+, Zn2+, and Ni2+) and maintained more than 80% removal efficiency through the sixth regeneration cycle. To complement the experimental findings, we constructed an artificial neural network (ANN) predictor for Pb2+ adsorption based on key operating variables (contact time, initial pH, initial metal ion concentration, adsorbent dose, and temperature). The model employed a three-layer backpropagation multilayer perception (MLP) trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm, and achieved high predictive accuracy (R2 = 0.993) with low prediction error, demonstrating strong consistency between experimental and modeled outcomes and supporting process optimization. These results highlight CM(1:1) as a promising, durable adsorbent for Pb2+ remediation and advanced wastewater treatment.
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