Chemistry

The recycling of spent lithium-ion batteries (LIBs) requires efficient and sustainable methods for recovering critical metals. In this study, the leaching behavior of LiCoO₂ cathode material obtained from spent LIBs was investigated in phosphoric acid, using copper powder recovered from waste LIBs as a reducing agent. Leaching experiments were conducted under various conditions (temperature, solid-to-liquid ratio, agitation rate) and compared with systems without copper. In the absence of copper, lithium and cobalt, recoveries after 30 min were approximately 77% and 23%, respectively. The addition of copper significantly enhanced leaching, achieving >96% recovery for both metals at 80 °C, with most extraction occurring within the first 30 min. Kinetic analysis using the shrinking core model indicated a mixed-control mechanism involving both surface chemical reaction and product layer diffusion. The calculated activation energies were 20.2 kJ·mol⁻¹ for lithium and 16.1 kJ·mol⁻¹ for cobalt. Solid residues were characterized by X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS). XRD results revealed that the composition of the residues varied with leaching temperature: Co₃O₄ was consistently detected, whereas Cu₈(PO₃OH)₂(PO₄)₄·7H₂O appeared only when leaching was performed above 50 °C. Thermodynamic calculations supported the reductive role of copper and provided insight into possible reaction pathways. These findings confirm the effectiveness of copper-mediated leaching in phosphoric acid and demonstrate that temperature strongly influences residue phase evolution, thereby offering valuable guidance for the design of sustainable LIB recycling processes.

This study investigated the efficiency of biochar in eliminating Cd(II) and Pb(II) ions from slurries generated from construction-derived waste materials. The construction waste slurry samples consisted of genuinely contaminated sludge sediments. To improve the adsorption capacity of biochar for metal ions, coconut shell-derived biochar was subjected to hydrochloric acid treatment. The modified biochar demonstrated an improved porous structure and showed a higher concentration of oxygen-containing functional groups compared to the untreated biochar. After a 48 h contact with the contaminated slurry, the treated biochar attained removal efficiencies of 21.15% for Cd(II) and 19.43% for Pb(II). The kinetic study of the adsorption process conformed to a pseudo-second-order model. Density functional theory (DFT) computations clarified the adsorption mechanism of Cd(II) and Pb(II) by carboxyl (-COOH) and hydroxyl (-OH) functional groups. The findings demonstrated that functional groups contribute lone-pair electrons for the adsorption of heavy metal ions. The carboxyl (-COOH) functional group exhibited a greater affinity for binding Cd(II) and Pb(II) ions than the hydroxyl (-OH) group, which explains the improved adsorption efficiency seen in biochar treated with hydrochloric acid. These findings offer theoretical validation for the use of hydrochloric acid-modified biochar as an efficient adsorbent for the remediation of sludge contaminated with Cd(II) and Pb(II).

Accurate potential energy surfaces (PESs) are the prerequisite for precise studies of molecular dynamics and spectroscopy. The permutationally invariant polynomial neural network (PIP-NN) method has proven highly successful in constructing full-dimensional PESs for gas-phase molecular systems. Building upon over a decade of development, we present CQPES v1.0 (ChongQing Potential Energy Surface), an open-source software package designed to automate and accelerate PES construction. CQPES integrates data preparation, PIP basis generation, and model training into a modernized Python-based workflow, while retaining high-efficiency Fortran kernels for processing dynamics interfaces. Key features include GPU-accelerated training via TensorFlow, the robust Levenberg–Marquardt optimizer for high-precision fitting, real time monitoring via Jupyter and Tensorboard, and an active learning module that is built on top of these. We demonstrate the capabilities of CQPES through four representative case studies: CH₄ to benchmark high-symmetry handling, CH₃CN for a typical unimolecular isomerization reaction, OH + CH₃OH to test GPU training acceleration on a large system, and Ar + H₂O to validate the active learning module. Furthermore, CQPES provides direct interfaces with established dynamics software such as Gaussian 16, Polyrate 2017-C, VENUS96C, RPMDRate v2.0, and Caracal v1.1, enabling immediate application in chemical kinetics and dynamics simulations.