Chunshan Lu

Co–Ni alloy nanoparticles encapsulated in carbon are prepared via pyrolysis of metal–organic coordination polymer. The optimized catalyst delivers outstanding output performance in hydroxide exchange membrane fuel cells.

The breakthrough in the performance of carbon-supported non-noble-metal catalysts lies in the synergistic optimization of geometric size and electronic structure; however, the high surface energy of metals tends to induce agglomeration, and the weak interfacial electronic coupling between metals and carbon makes precise regulation of electronic structure challenging. Herein, an incomplete encapsulation of Ni nanoparticles (Ni NPs) -embedded structure is achieved after the self-assembly of Ni species and halogen-doped carbon dots and the subsequent pyrolysis process. The F atoms, endowed with the strong electron-withdrawing capability, abstract electrons from the outer shell of Ni species, which not only accelerates interfacial electron transfer of the catalyst, thus modulating the d-band center of Ni, but also suppresses the formation of Ni−Ni bonds, thereby weakening the intrinsic driving force for metal agglomeration. Among them, the catalyst Ni@FCDs doped with F atoms exhibits the highest adsorption and dissociation ability for reactant molecules, demonstrated by the extended X-ray absorption fine-structure and density functional theory calculations, and the smallest metal particle size (5.54 nm), thus expressing high catalytic hydrogenation activity. Furthermore, the embedded structure effectively inhibits the sintering of Ni NPs during the reaction, and the metal particle size remains stable after 9 cycles of reaction. The synergistic optimization of geometric size and electronic structure of the metal species induced by the “halogen doping dual regulation” strategy provides an innovative approach for the design of high-performance nickel-based catalysts and deepens the understanding of the structure−activity relationship in carbon-supported metal catalysts.

N , N , N ′, N ′‐tetraglycidyl‐4,4′‐diaminodiphenylmethane (TGDDM) is a glycidylamine epoxy resin with excellent heat resistance and rigidity. However, its inherent high brittleness restricts its application scope. The use of thermoplastic polyimide to toughen EP has several drawbacks, such as increased viscosity and deteriorated dispersibility of composites. To address this issue, an amino‐terminated polyimide oligomer (DBD) was synthesized to toughen TGDDM due to a minimal impact on the viscosity during processing. In addition, the reactive terminal amino groups of DBD can participate in the epoxy curing reaction to form a chemically bonded interface between the oligomer and the matrix and dissipate fracture energy via mechanisms such as crack deflection, thereby enhancing the fracture toughness of the resulting composites. The results demonstrated that, compared with neat EP, the modified epoxy resin TGDDM with 5 wt% of DBD exhibited an increase in impact strength to 7.0 kJ/m ² and fracture toughness to 0.77 MPa· m ^1/2 (representing increases of 40% and 22%, respectively), without sacrificing its thermal and tensile properties. Furthermore, the results of mechanical and thermal characterization were further validated by phase morphology analysis results. This research provides novel insights for the practical application of oligomer‐modified epoxy resins in the electronic and aerospace industries.

Thermocatalysis, the mainstream approach relative to photocatalysis and electrocatalysis, drives reactions by employing catalysts to lower activation energies with thermal energy. However, thermocatalysis presents issues of inefficient heat transfer and limited reaction performance. Magnetic induction heating (MIH) is a promising thermocatalytic strategy. This study employs a carbon-encapsulated Fe–Ni alloy (Ni–Fe@C) for the catalytic hydrogenation of p‑chloronitrobenzene (p‑CNB) under MIH. The results show that magnetic induction heating generates thermal effects under alternating magnetic field (AMF) conditions, whereas thermal-control experiments demonstrate that thermal effects alone cannot fully account for the observed catalytic enhancement. Experimental characterizations further reveal a close association between spin-related electronic characteristics and the AMF-induced catalytic enhancement, while spin-constrained DFT calculations show that changing the spin configuration affects p-CNB adsorption and reaction barriers. Under mild MIH conditions (35 °C, 20 min), the catalyst achieved 99.9% p-CNB conversion, significantly outperforming conventional heating at 101.1 °C (60.1%). This work demonstrates a “magnetic catalysis” strategy for efficient hydrogenation under mild conditions.

Nitrogen doping modulates the geometric and electronic structures of the carbon layer, thereby enabling the efficient hydrogenation–rearrangement of nitrobenzene to p -aminophenol.

Triglyceride‐based oils undergo concurrent chain scission and intermolecular coupling during high‐temperature exposure, resulting in time‐dependent redistribution of molecular weights and the formation of volatile/gaseous products. Here, refined soybean oil was thermally aged at 150°C for 0–96 h, and its degradation behavior was characterized using physicochemical indices (acid value, viscosity, and moisture), dissolved gas analysis (DGA) as scission‐related gaseous markers, and gel permeation chromatography (GPC) to track the evolution of the high‐molecular‐weight (high‐MW) tail. Over the studied window, DGA‐measured gases exhibited rapid accumulation (e.g., ethylene reached ~65 ppm at 96 h), whereas the GPC high‐MW tail reflected a slower, lagging oligomerization/coupling contribution. To enable integrated interpretation of these heterogeneous observables, we propose a cross‐metric mapping N ( M ) that links ethylene concentration ( N ) to the high‐MW tail area increment ( M ), using M as a time‐proxy coordinate ( R ² = 0.968). The derived time‐scale parameter λ ≈107 quantifies the pronounced curvature mismatch between cracking and growth, confirming that the two processes evolve on distinctly different apparent time scales. This mapping provides a practical, time‐scale‐free yardstick for comparing the relative cracking‐versus‐growth tendency of different oil systems or under different contaminant conditions (e.g., copper catalysis), offering a chemically grounded, application‐oriented approach for short‐term thermal stability assessment of triglyceride‐based liquids under controlled heating conditions.

The hydroxide exchange membrane fuel cell (HEMFC) is one of the most advanced fuel cells, showing high potential in fuel cell vehicles. In a HEMFC, the gas flow pattern and distribution are significant as air is introduced into flow field initially. However, few studies have focused on gas flow and no effective approach could predict and evaluate flow fields before they are manufactured. In this work, a novel approach based on a multi‐parameter function including pressure drop, velocity, and maldistribution factor was proposed to design and evaluate the hydrodynamics of flow fields. Models of nickel foam, single serpentine, and parallel flow fields were built via X‐ray computed tomography (CT) and direct fluid domain reconstruction. Numerical simulation with single‐phase flow was implemented to predict the pressure drop, velocity, and distribution. The pressure drop was validated experimentally with 3D printed plates and the relative error was less than 20%. The maldistribution factors (M _f ) were proposed and calculated to estimate gas distribution. The M _f of nickel foam flow field was 0.90–1.02, which was much lower than the M _f of the parallel flow field (2.85–3.01) and the M _f of the single serpentine (1.36–1.54). Combined with M _f , average z velocity ( v _z ), and pressure drop (P), the multi‐parameter‐function was proposed. The nickel foam flow field performed the best under single‐phase conditions.

Chlorinated volatile organic compounds (CVOCs) hydrodechlorination is hindered by chlorine poisoning and the instability of monometallic catalysts, limiting their practical application. In this work, long-range ordered PdCu intermetallic compounds were synthesized via a microwave-assisted method. Characterization confirmed that microwave treatment induced strong Pd–Cu electronic interactions and lattice ordering, thereby facilitating the formation of intermetallic phases. Catalytic evaluations demonstrated that the PdCu/C-Ordered catalyst achieved an ethane production rate of 4.47 mmol·gcat–1·h–1 at 300 °C with an apparent activation energy of 31.28 kJ/mol, while maintaining exceptional stability over a 300 h continuous operation. Density functional theory (DFT) calculations further elucidated that the intermetallic phase, characterized by a d-band center positioned closer to the Fermi level, exhibited enhanced reactant adsorption and activation capabilities. Concurrently, it significantly lowered the HCl desorption energy barrier, thereby effectively mitigating chlorine poisoning effects. This research provides a novel pathway for the design of high-efficiency hydrodechlorination catalysts based on the precise regulation of electronic structures.

Carbon monoxide (CO) catalytic oxidation, as one of the most extensively studied model reactions, serves as a critical platform for understanding how electronic structures determine catalytic performance. Although descriptors such as CO adsorption energy are widely used to correlate catalytic activity, they are often closely tied to specific surface structures or material types, making it difficult to establish a universal framework. In contrast, electronic structure descriptors, derived directly from the intrinsic electronic properties of atoms, offer the potential for constructing a unified understanding. This review systematically categorizes four key electronic structure descriptor systems: energy‐level structure, d‐band structure, charge and valence state distribution, and spin structure – progressively revealing their inherent hierarchical relationships across energy‐level distribution, orbital hybridization, charge population, and electron spin, as well as their decisive roles in determining the reaction pathways of CO oxidation. In addition, this paper expands the scope of descriptors and explores the construction pathways for dynamic electronic structure descriptors as well as multiscale descriptors. By employing physics‐based descriptors and interpretable machine learning models, it is possible to overcome the limitations of traditional performance prediction and lay the foundation for establishing causal models that encompass dynamic evolution and cross‐scale coupling processes.