Vanadium–titanium (V–Ti) catalysts, featuring high activity, selectivity, thermal stability, and sulfur resistance, play a pivotal role in typical oxidation reactions, including aromatic selective oxidation, selective catalytic reduction, volatile organic compounds abatement, and oxidation of sulfur-containing species. This review focuses on the fundamental structures and chemical properties of V–Ti catalysts, highlighting the regulatory roles of catalyst composition, interfacial microstructure, and preparation methods in governing catalytic performance. Modification strategies, including crystal facet/phase engineering, morphology control, elemental doping, and defect construction, are systematically discussed with respect to their underlying mechanisms. Furthermore, the recent advances in the application of V–Ti catalysts in selective and deep oxidation reactions relevant to industrial oxidation processes are summarized, with emphasis on the corresponding catalytic mechanisms. Finally, future perspectives are proposed, emphasizing the design of efficient, stable, and environmentally benign catalysts, coupled with advanced characterization techniques to deepen mechanistic understanding and promote their industrial applications in industrial catalysis, fine chemicals, and environmental remediation.

Multi-scale superquadric discrete element method-computational fluid dynamics (DEM-CFD) coupling provides a powerful tool to resolve cross-scale behaviors inside fluidized bed reactors, yet several critical obstacles restrict its industrial application, including anisotropic contact hydrodynamics, incomplete heat transfer frameworks, immature non-spherical reaction models, and high computational cost for large-scale particle systems. This view systematically dissects the above bottlenecks, summarizes unresolved research gaps, and proposes targeted remedies: reduced-space Newton iteration for contact singularity, generalized shape-factor Nusselt correlations, superquadric coordinate-based shrinking core reaction models, and the synergy of coarse-grained particle models with graphics processing unit acceleration. Furthermore, this work elaborates on the transformative potential of artificial intelligence: deep learning can construct universal drag/heat transfer correlations from direct numerical simulation data, generate interpretable chemical reaction kinetics, and accelerate stiff solvers to cut simulation overhead. This view delivers clear development roadmaps for the next-generation multi-scale SuperDEM-CFD platform, supporting predictive design and real-time digital twin construction of gas-solid reactors.

The catalytic conversion of CO₂ into methanol represents a promising route for reducing anthropogenic carbon emissions. Although Pt/In₂O₃ is an effective catalyst for this reaction, it suffers from severe sintering of both the Pt nanoparticles and the In₂O₃ support at high temperatures, leading to particle growth and deactivation. Herein, we report an in situ growth and embedding strategy using MIL-68(In) nanorods as a precursor to confine Pt nanoparticles within the derived In₂O₃ matrix. The resulting Pt/In₂O₃ catalyst exhibits ultrasmall In₂O₃ crystallites (11.8 nm) and a high surface area (48.3 m²∙g⁻¹). Comprehensive spectroscopic and microscopic analyses reveal strong Pt-O-In electronic interactions, which generate a high density of surface oxygen vacancies (41%) while minimizing the number of exposed Pt sites. These structural and electronic features synergistically enhance CO₂ activation and H₂ dissociation, reducing the apparent activation energy required for methanol formation to 62.2 kJ∙mol⁻¹. Under industrially relevant conditions (3 MPa, 300 °C, 12000 mL∙g_cat⁻¹∙h⁻¹), the optimized catalyst achieves a CO₂ conversion of 10.6% and a methanol production rate of 0.22 g_MeOH∙g_cat⁻¹∙h⁻¹, with exceptional stability over 180 h, outperforming previously reported Pt/In₂O₃ catalysts. This study offers a versatile metal-organic framework-mediated confinement strategy for designing durable high-performance metal/oxide catalysts for CO₂ hydrogenation.

Coal gasification fine slag (CGFS) exhibits poor briquetting characteristics and cannot be directly reused in furnaces. To facilitate the beneficial reuse of this byproduct, we systematically investigate using carbon-rich residue (CR), a CGFS product, coupled with a corn stalk-based biomass binder and hot-pressing to produce briquetted carbon-rich residue (BCR). The effects of the briquetting conditions and shape on the mechanical strength and gasification reactivity of the BCR were investigated. The optimal briquetting parameters were identified as an alkali-urea-treated biomass binder content of 25%, a hot-pressing temperature of 175 °C, and a pressure of 7.5 MPa. Under these conditions, the drop strength of all BCR samples exceeded 99.7%. The BCR sample with a height-to-diameter ratio of 0.8 exhibited the best mechanical strength, with a compressive strength of 620.8 N, which is more than double the industrial standard of 300 N. Moreover, the gasification reactivity and carbon conversion rates were superior to those of raw CR. Mechanistic analysis indicated that lignin and amide components generated during binder preparation act as active viscous precursors. During hot-pressing, the lignin softened and coated the CR particles, and the hydrogen bond network was reconstructed at the molecular level to enhance interfacial bonding. Additionally, the amide components bonded tightly with the CR particles, further strengthening interfacial bonding and forming a dense BCR structure. This study provides a technical reference for the synergistic beneficial reuse of CGFS and biomass solid wastes.

Reliable acquisition of arterial pulse waves is important for wearable cardiovascular monitoring; however, weak wrist-surface vibrations are susceptible to variations in contact pressure, motion artifacts, and physiological conditions. Herein, we report an iontronic pressure sensor that integrates magnetron-sputtered Pt interdigitated electrodes with a microdome-structured thermoplastic polyurethane/spherical boron nitride/ionic liquid composite layer. The device operates over a pressure range of 0–1500 kPa, with a maximum sensitivity of 67.89 kPa⁻¹, response/recovery times of 136/30 ms, resolved response at the minimum demonstrated pressure of 173 Pa, and repeated operation over 7000 loading-unloading cycles with a maximum signal deviation of 3.72%. Hierarchical spherical boron nitride interfaces are expected to influence local ion organization and interfacial polarization, whereas mechanical tests indicate higher dynamic stiffness and lower cyclic peak force variation following the incorporation of spherical boron nitride under the tested conditions. As a proof-of-concept, 14 standardized pulse patterns were classified using multiple feature-based strategies, with the best-performing classifier achieving a five-fold mean accuracy of 98.23%. A wireless system recorded radial artery pulse waves before and after exercise and enabled Fourier-transform analysis of heart rate variation. These pulse patterns and single-participant on-body demonstrations support sensor-based pulse acquisition and analysis but do not constitute a clinical validation.

Hierarchical yolk–shell structured TS-1-SiO₂ micro-mesoporous composites (TY_xS_y) with different mass transfer properties were fabricated as supports for the fabrication of high-efficiency NiMo/TY_xS_y. The shell thickness and yolk size of such yolk–shell composites were precisely controlled by introducing the nonpolar additive 1,3,5-trimethylbenzene and modulating its incorporation dosage. An appropriate yolk–shell thickness facilitated the hydrodesulfurization (HDS) of macromolecular sulfur-containing compounds, and the yolk–shell structured NiMo/TY_xS_y micro-mesoporous catalysts exhibited superior HDS catalytic performance. Among all as-prepared catalysts, NiMo/TY₁₀S₁₃ achieved the highest turnover frequency (7.5 × 10^–4 s^–1 for dibenzothiophene (DBT) and 6.1 × 10^–4 s^–1 for 4,6-dimethyldibenzothiophene (4,6-DMDBT)), apparent HDS rate constants (2.6 × 10^–7 mol·g^–1·s^–1 for DBT and 2.1 × 10^–7 mol·g^–1·s^–1 for 4,6-DMDBT), and optimal desulfurization efficiencies of DBT (98%) and 4,6-DMDBT (93.3%). Additionally, this catalyst favored the direct desulfurization pathway for DBT HDS and the isomerization pathway for 4,6-DMDBT HDS.

Selective CO₂ hydrogenation to ethanol is fundamentally challenged by the difficulty of coordinating CO₂ activation, hydrogenation, and asymmetric C–C coupling within a single catalytic system. Typical catalytic sites guided by previous strategies, including atomically dispersed sites and conventional interfacial sites, suffer from inherent limitations such as low site density and poor functional differentiation versus inadequate structural precision and stability, respectively. To address this, we propose atomic-scale interface engineering to construct chemically differentiated, spatially adjacent and kinetically matched sites that selectively stabilize and couple C1 intermediates. Realizing this concept will require coordinated control over site density, adsorption strength, and hydrogenation kinetics, together with the establishment of robust relationships among catalyst structure, reaction intermediates, and kinetics through integrated operando characterization, density functional theory calculations, and machine learning assisted screening. Such a framework could provide a rational basis for simultaneously improving CO₂ conversion, ethanol selectivity, and catalyst stability, and ultimately facilitate the development of practical catalytic systems for CO₂ conversion to ethanol.