Optimization of electrochemical regeneration of intercalated MXene for the adsorptive removal of ciprofloxacin: Prospective mechanism.

2D-Ti3C2Tx MXene nanosheets intercalated with sodium ions (SI-Ti3C2Tx) were synthesized and utilized in simultaneous adsorption and electrochemical regeneration with ciprofloxacin (CPX). The primary focus of this study is to investigate the long-term stability of SI-Ti3C2Tx MXene and to propose the underlying regeneration mechanisms. The successful synthesis of Ti3AlC2, Ti3C2Tx MXene, and SI-Ti3C2Tx MXene was confirmed using X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy. Electrochemical regeneration parameters such as charge passed, regeneration time, current density, and electrolyte composition were optimized with values of 787.5 C g-1, 7.5 min, 10 mA cm-2, and 2.5w/v% sodium chloride, respectively, enabling the complete regeneration of the SI-Ti3C2Tx MXene. In addition, the electrochemical regeneration significantly enhanced CPX removal from the SI-Ti3C2Tx MXene owing to partial amorphization, disorderliness, increased functional groups, delamination, and defect creation in the structure. Thus, the synthesized nano-adsorbent has proven helpful in practical water treatment with optimized electrochemical regeneration processes.

Experimental analysis and prediction of radionuclide solubility using machine learning models: Effects of organic complexing agents.

Radioactive wastes contain organic complexing agents that can form complexes with radionuclides and enhance the solubility of these radionuclides, increasing the mobility of radionuclides over great distances from a radioactive waste repository. In this study, four radionuclides (cobalt, strontium, iodine, and uranium) and three organic complexing agents (ethylenediaminetetraacetic acid, nitrilotriacetic acid, and iso-saccharic acid) were selected, and the solubility of these radionuclides was assessed under realistic environmental conditions such as different pHs (7, 9, 11, and 13), temperatures (10 °C, 20 °C, and 40 °C), and organic complexing agent concentrations (10-5-10-2 M). A total of 720 datasets were generated from solubility batch experiments. Four supervised machine learning models such as the Gaussian process regression (GPR), ensemble-boosted trees, artificial neural networks, and support vector machine were developed for predicting the radionuclide solubility. Each ML model was optimized using Bayesian optimization algorithm. The GPR evolved as a robust model that provided accurate predictions within the underlying solubility patterns by capturing the intricate relationships of the independent parameters of the dataset. At an uncertainty level of 95%, both the experimental results and GPR simulated estimations were closely correlated, confirming the suitability of the GPR model for future explorations.