Ding-Ming Xue

Solar-driven CO₂ reduction requires a photocatalyst to integrate light harvesting, exciton dissociation, long-range charge transport, CO₂ adsorption and activation, and multielectron proton-coupled electron transfer within a single structural platform. Covalent organic frameworks (COFs) are attractive in this context because their π-conjugated backbones, periodic pores, linkage chemistry, stacking modes, and catalytic sites can be programmed with molecular precision. Nevertheless, the literature remains dominated by phenomenological correlations between optical response and product yield, while the causal relationships among exciton binding energy (E_b), free-carrier generation, interfacial charge transfer, intermediate adsorption, and product selectivity remain insufficiently resolved. This review develops a unified structure–activity framework in which intrinsic π-conjugation and donor–acceptor polarization regulate E_b and exciton diffusion; morphology and heterojunctions control the competition between interfacial transfer and recombination; and active-site coordination and pore microenvironments determine local electron density, proton delivery, and the adsorption energies of *COOH, *OCHO, *CO, *CHO, and *OCCO intermediates. Representative performance is benchmarked only when ¹³CO₂ isotope tracing and appropriate blank controls are available, with external homogeneous photosensitizer systems treated separately. Among the entries retained in the condition-aware benchmark, reported CO production rates span 17.75–20,110 μmol g⁻¹ h⁻¹ and wavelength-specific AQYs span 0.058–5.1%; these values are not directly comparable without the associated photon flux, reactor configuration, catalyst loading, and auxiliary components. Finally, operando spectroscopy, density functional theory, microkinetic modelling, stability analysis, standardized reporting, machine-learning-assisted discovery, and device-oriented scale-up are discussed as routes toward predictive and reproducible COF photocatalyst engineering.

Chlorophenylacetonitriles are known as one of the emerging nitrogenous disinfection byproducts (N-DBPs) in chlorinated drinking water due to their concerned cytotoxicity and genotoxicity compared to regulated carbonaceous DBPs. However, under low-dose exposure, the in vivo pathological consequences of chlorophenylacetonitriles remain largely unresolved. Here, C57BL/6J mice were exposed to 2-chlorophenylacetonitrile (2-CPAN) via drinking water (100 ug/L) for six months, and an integrated histopathological and genome-wide transcriptomic approach was employed to mechanistically characterize its multi-organ toxicological consequences. 2-CPAN ingestion significantly suppressed body weight (35.9 ± 2.0 g vs. 47.6 ± 11.6 g, p < 0.05) and induced severe gastroenteropathy—including gastric lamina propria inflammatory infiltration, intestinal villous blunting, crypt disorganization, transmural mononuclear infiltration, and abrogating epithelial barrier integrity. Intestinal barrier failure drove portal translocation of luminal PAMPs, potentially triggering splenic white pulp atrophy, red/white pulp boundary dissolution, and parenchymal changes consistent with fibrotic remodeling. Splenic RNA sequencing revealed a bipartite transcriptomic reprogramming: upregulated pathways were enriched in the ribosome, MAPK signaling, cytokine–cytokine receptor interaction, and chemokine signaling pathways. A proteotoxic stress module (Hspa1a, 9.4-fold; Hspa1b, 10.2-fold) and a chemokine effector hub (Ccl2, 3.43-fold; Ccl5, 2.9-fold; Ccl19, 2.58-fold; Ccl21a, 2.15-fold) were identified by the STRING network. Downregulated pathways converged on cell cycle suppression, with concurrent loss of Ccne1/Ccne2 and Cdc6 (G1/S block), Ccnb1 and Plk1 (G2/M arrest), and Rrm2/Tars3/TrnM (dNTP and aminoacyl-tRNA starvation), collectively forcing splenic lymphocytes into irreversible proliferative failure. These findings provide the first mechanistically resolved in vivo evidence that chronic 2-CPAN exposure drives a potential gut–spleen toxicological axis, underscoring the urgent need to incorporate organ endpoints by long-term exposure into N-DBP risk assessment.

6PPD is a widely used tire antioxidant that readily transforms into its more toxic ozonation product, 6PPD-quinone (6PPD-Q). Both compounds are emerging environmental contaminants with potential neurotoxic risks, yet their molecular mechanisms in Alzheimer’s disease (AD) and Parkinson’s disease (PD) remain unclear. This study integrated network toxicology, molecular docking, transcriptomic validation, and experimental models to investigate their neurotoxic effects. In silico analyses predicted significant neurotoxicity and blood–brain barrier permeability for both compounds. Target prediction and PPI network analysis identified 145/121 overlapping targets with AD/PD for 6PPD and 120/100 for 6PPD-Q. Functional enrichment analysis suggested that 6PPD-associated targets were mainly enriched in axon regeneration-, p75NTR-, and AGE-RAGE-related pathways, whereas 6PPD-Q-associated targets were enriched in MAPK cascade-, endosomal TLR signaling-, and amyloid-β formation-related pathways. Molecular docking suggested favorable binding affinities between these compounds and several core targets, including MAP2K1, EGFR, GSK3B, and CYCS. Transcriptomic validation in GEO datasets prioritized multiple hub genes. In vivo experiments showed activation of apoptosis-related signaling in the brain, while in vitro assays demonstrated ROS accumulation and neuroinflammatory activation (elevated TNF-α, IL-1β, IL-6, IFN-γ). CYCS and MAP2K1 emerged as key convergent nodes. Our findings reveal distinct yet synergistic neurotoxic mechanisms of 6PPD and 6PPD-Q in AD and PD, highlighting tire-derived pollutants as potential environmental risk factors for neurodegenerative diseases.

Fe–carbon catalysts (FCCs) are extensively used for persulfate activation in advanced oxidation processes (PS-AOPs), an approach regarded as an efficient and cost-effective strategy for removing emerging contaminants (ECs). However, the quantitative structure–activity relationship between the degradation efficiency of ECs with diverse molecular characteristics and the microstructure of FCCs has not been clearly elucidated. This hinders the widespread practical implementation of FCCs. Herein, density functional theory (DFT)-derived molecular-descriptor-assisted machine learning models were employed to accurately predict the reaction rate constants for EC degradation in FCC-PS AOPs, mainly focusing on three aspects: performance prediction, operating condition optimization and mechanism interpretation. Additionally, DFT-derived descriptors are integrated with fabrication and operational parameters to facilitate the generative design of FCCs. The excellent fitting performance of the overall XGB model in predicting the reaction constants for EC degradation (Test R² = 0.813) highlights the notable advantages of customized hyperparameter tuning for improving predictive accuracy. Subsequently, the submodels trained using different EC clusters (derived from t-SNE and K-means clustering methods) can offer specific strategies for selecting optimal parameters of FCC-PS AOPs that target ECs with distinct properties. The interpretability of the model was improved by using SHAP values and partial-dependence plots to clarify the internal relationships of the ML “black box”. Overall, a feasible and generalizable ML model is proposed to facilitate a paradigm shift in the inverse design of FCCs for the degradation of specific ECs.

Efficient and selective SO ₂ capture from flue gas, particularly under humid conditions, remains a significant challenge. Herein, we report the successful molecular design and construction of Pillar‐Layered Co ₆ Cluster MOF (PLC‐Co), an ultramicroporous columnar layered MOF featuring a unique tfz‐d topology. Comprehensive studies revealed remarkable performance: single‐component isotherms showed a record SO ₂ /CO ₂ selectivity ratio exceeding 22 (5.71 mmol/g SO ₂ vs. 0.36 mmol/g CO ₂ at 0.1 bar), the highest reported among crystalline porous materials. Its high performance was validated in multicomponent breakthrough simulations and experiments, demonstrating sustained high‐efficiency SO ₂ removal even under challenging 50% RH conditions. A combined experimental and computational study on the interaction between SO ₂ and PLC‐Co single crystals has revealed the true adsorption sites of SO ₂ within the material. This research elucidates the molecular adsorption mechanism and identifies specific interaction sites responsible for the observed unique selectivity. Furthermore, PLC‐CO exhibits excellent thermal and chemical stability, along with outstanding repeatability and regenerability. These findings highlight PLC‐CO as a highly promising material for practical flue gas desulfurization and showcase the effectiveness of topological design strategies for developing high‐performance porous materials for demanding gas separations.

Solid base catalysts hold significant promise for replacing traditional homogeneous bases with green chemical processes. However, the construction of their strong basic sites typically relies on high-temperature calcination, which often leads to the collapse of the carrier structure and high energy consumption. This study proposes a novel “carrier reducibility tuning” strategy, which involves endowing the carrier with intrinsic reducibility to induce the low-temperature decomposition of alkali precursors via a redox pathway, thereby enabling the mild construction of strong basic sites. Low-valence Cr³⁺ was doped into a mesoporous zirconia framework, successfully fabricating an MCZ carrier with a mesostructure and reducible characteristics. Characterization results indicate that a significant redox interaction between the Cr³⁺ in the carrier and the supported KNO₃ occurs at 500 °C. This interaction facilitates the complete conversion of KNO₃ into highly dispersed, strongly basic K₂O species, while Cr³⁺ is predominantly oxidized to Cr⁶⁺. This activation temperature is approximately 300 °C lower than that required for the conventional thermal decomposition pathway and effectively preserves the structural integrity of the material. In the transesterification reaction for synthesizing dimethyl carbonate, the prepared catalyst exhibits superior catalytic activity, significantly outperforming classic solid bases like MgO and other reference catalysts.

Red mud storage sites pose significant environmental and health risks due to the accumulation and dispersion of toxic heavy metals (HMs). This study presents a novel framework integrating Self-Organizing Maps (SOM) with the APCS–MLR model for source apportionment and health risk assessment of soil heavy metal contamination near a red mud site in Shandong, China. Unlike traditional receptor models or SOM alone, this approach combines SOM’s ability to reveal nonlinear spatial patterns with the quantitative strength of APCS–MLR to estimate source contributions. Additionally, we incorporate a source-oriented health risk assessment to directly link pollution sources with health impacts. These were further coupled with Monte Carlo simulation-based probabilistic health risk assessment and Getis–Ord Gi* spatial hotspot analysis to attribute health risks to specific sources and localize high-risk zones. Four major sources were identified: natural sources (46.34% ± 3.21%), red mud storage (20.08% ± 1.54%), coal combustion (2.84% ± 0.56%), and agricultural–industrial activities (2.81% ± 0.45%), along with 27.93% (± 4.12%) from unknown sources. Although red mud contributed less to total heavy metal loads, it dominated health risks, accounting for 63.84% of carcinogenic and 58.91% of non-carcinogenic risks, primarily due to its high arsenic content. This finding highlights the disproportionate health impact of red mud despite its lower contribution to the overall contamination. Spatial risk clusters corresponded strongly with red mud proximity and As concentrations. This study demonstrates the effectiveness of a multi-model integrative approach for resolving complex source–risk–space linkages and informing precision pollution control strategies.

Adsorption is one of the most promising technologies for desulfurization and denitrification.