The removal of hexavalent chromium (Cr(VI)) from industrial effluents remains challenging for conventional solvent extraction systems due to sluggish mass transfer, limited extraction capacity, and the inherent toxicity of volatile organic compounds. Here, we report a wettability-modulated strategy to overcome these limitations using hydrophobic deep eutectic solvents (HDESs) based on quaternary ammonium salts and fatty acids. By correlating the contact angle, viscosity, and hydrogen-bonding network with extraction performance, we identify the optimized HDES, [N8881][Br]-OA(1:2), which exhibits a remarkably low viscosity (147.6 cP at 298.2 K) and a favorable contact angle (34.4°), enabling rapid interfacial anion exchange. This extractant achieves a single-stage Cr(VI) recovery exceeding 99% within only 10 min at an initial concentration of 500 mg·L–1 under an oil-to-water ratio of 1:50. Mechanistic investigations confirm a reversible Br–/HCrO4– anion-exchange pathway, while thermodynamic analysis reveals an endothermic and entropy-driven process. Notably, the HDES maintains over 95% extraction efficiency after 12 successive regeneration cycles without structural degradation and outperforms the benchmark solvent methyl isobutyl ketone (MIBK) by a factor of 12. This work demonstrates that tailoring the wettability of HDESs provides a green, sustainable, and industrially viable platform for the ultrafast decontamination of Cr(VI)-laden wastewater, with significant potential for scalable water treatment applications.
Accurate prediction of hydrogen sorption in fine-grained geological materials is essential for subsurface energy systems. While classical isotherm models describe individual samples effectively, they fail to generalize across heterogeneous populations, with R2 dropping from 0.80–0.90 for single-sample fits to 0.09–0.38 for aggregated data sets. This limitation demonstrates that predictive models must explicitly account for intrinsic material properties and fundamental physical constraints. We present a thermodynamics-informed, physics-regularized neural network framework designed to improve predictive accuracy and cross-lithology generalization. The framework integrates three core components: a seven-category physics-informed feature engineering scheme generating 62 thermodynamically meaningful descriptors, a multiterm loss function enforcing saturation limits and monotonic pressure responses through penalty-weighted terms, and an architecture-diverse ten-member ensemble with post hoc temperature-scaling calibration to provide reliable uncertainty estimates. Van’t Hoff temperature dependence is incorporated through thermodynamic feature engineering, with all model components integrated within a three-phase curriculum training strategy that progressively introduces physical regularization. On a random-split holdout set, the framework achieves R2 = 0.9544, RMSE = 0.0484 mmol/g, and MAE = 0.0231 mmol/g, with a 98.6% monotonicity satisfaction rate and zero nonphysical negative predictions. Because random splits primarily evaluate interpolation within the observed distribution, we prioritize leave-one-lithology-out (LOLO) validation to assess physical generalization under distribution shift. When entire geological classes are withheld from training, the framework retains R2 = 0.723 for coal and 0.794 for shale, outperforming a well-tuned random forest baseline (R2 = 0.631 and 0.712, respectively) by 14.6% and 11.5%, respectively. These results support the effectiveness of thermodynamic constraints as a generalization scaffold across geological boundaries, providing a practical foundation for evaluating formations whose lithological classes are absent from the training data.
Material recovery facility (MRF) residues are heterogeneous mixtures of plastics, paper/cardboard, and mineral matter that are difficult to separate and are commonly landfilled. This study evaluated whether torrefaction-induced changes in grindability can enable size-based enrichment of plastic-, biogenic-, and mineral-rich components from MRF-derived municipal solid waste (MRF-MSW). Approximately 150 g batches were torrefied at 300–400 °C for 5–150 min, producing 0–55% mass loss, and then ground and vibro-sifted into >850, 74–850, and <74 μm fractions. Across the investigated torrefaction severities, 60–80% of the recovered ash was concentrated in the <74 μm fraction, whereas the >850 μm fraction at low mass loss had the highest higher heating value (HHV) and was enriched in plastics. Microscopy and elemental analysis revealed that the materials were concentrated into plastic, biogenic, and mineral-rich fractions. Above approximately 20% mass loss, all retained material passed the 850 μm screen. The chlorine contribution from the coarse plastic-rich fraction declined to essentially zero above approximately 25% mass loss, while most residual chlorine was associated with the mineral-rich fine fraction. These results show that torrefaction severity can be adjusted to control grindability and facilitate physical fractionation of heterogeneous MRF-MSW.
Accurate prediction of acidity in terminal alkynes is critical for understanding their chemical behavior in isotope labeling, synthesis design, and drug development. In this study, we evaluate multiple quantum-chemical strategies to identify a reliable and simple computational approach for estimating alkyne acidities. A range of descriptors, including thermodynamic proton and hydrogen atom transfers, reaction energy profiles, vibrational and magnetic properties, and atomic charge distributions, were assessed across a representative set of alkynes. Only two approaches demonstrated predictive performance aligned with experimental data. The first involves calculation of energy barriers for hydrogen exchange, which shows a linear correlation with known pKa values and offers applicability across alkyl-, aryl-, and fluoro-substituted alkynes. The second leverages computed acetylenic proton chemical shifts, which provide valid predictions for a limited subset of alkynes. In contrast, descriptors such as IR frequencies, 13C NMR shifts, and atomic charges failed to yield accurate estimates. We extended our predictions to pharmaceuticals with terminal alkyne groups─such as ethynylestradiol, deprenyl, and 5-ethynyluracil─for which experimental acidity data are currently unavailable. The proposed workflow combines computational efficiency with mechanistic relevance and may serve as a practical tool for chemists seeking to assess the acid–base properties of terminal alkynes in silico.
Bubbles exhibit nonlinear motion characteristics in liquids, with their interfaces undergoing significant deformation during movement, resulting in complex and diverse situations. In this paper, the lattice Boltzmann method is employed to numerically simulate the deformation behavior of a freely rising single bubble. The focus is on examining the effects of bubble size, surface tension, liquid viscosity, and liquid density on bubble deformation and obtaining the variation of the Weber number of bubble deformation. The results indicate that the essence of bubble deformation lies in the dynamic interplay between inertial forces, viscous forces, and surface tension. The Weber number serves as an effective comprehensive indicator of the relative importance of these three factors. Notably, surface tension emerges as the pivotal factor inhibiting deformation, whereas liquid density and bubble size facilitate deformation by amplifying inertial forces. The role of liquid viscosity exhibits nonmonotonicity, stemming from its dual function as both a damping dissipator and a shear deformation transmitter.
