In this study, HZSM-5 zeolite was modified with citric acid, and the effects of two calcination atmospheres, air and N2, on the pore structure and acid sites of the catalyst support were investigated. Transition metals Ni, Mn, and Cu were then loaded onto the modified supports, and their catalytic cracking performance for toluene, biomass tar model compound, was compared. The results show that citric acid modification combined with air calcination significantly increases the average mesopore diameter, reduces mass diffusion resistance, and enhances catalytic activity. Calcination in N2 atmosphere increases the acid sites of the catalyst, thereby promoting the catalytic activation of toluene. The catalytic performance is further improved after metal loading, with the Ni-modified catalyst exhibiting the best performance, achieving a toluene conversion of 75.50%. Under air atmosphere, the metals exist as oxides of NiO, Cu2O, and MnO2, whereas under N2 atmosphere, Ni and Cu are present in their elemental forms, while Mn remains as MnO2. Metallic Ni exhibits strong ability to cleave C-C and C-H bonds, thus increasing H2 yield. In contrast, Mn preferentially catalyzes the demethylation of toluene, promoting CH4 formation. Furthermore, a numerical model built using COMSOL Multiphysics accurately simulates the experimental process, revealing the effects of toluene mass flow rate and catalyst contact area conversion efficiency. Reducing the feed flow rate or increasing the contact area both effectively improve toluene conversion efficiency.
Dry reforming of methane (DRM) provides a sustainable route for converting CH4 and CO2 into syngas, yet the high-temperature reforming environment often accelerates Ni nanoparticle sintering and carbon accumulation, leading to rapid catalyst deactivation. Stabilizing highly dispersed Ni species within a robust interfacial environment is therefore essential for durable DRM catalysis. Herein, we report a silane-assisted confinement strategy using 3-aminopropyltrimethoxysilane to construct a thermally robust Ni/A-SiO2 catalyst. The silane coupling chemistry induces the in situ formation of Si-O-Ni interfacial linkages, which strengthen the metal-support interaction and confine ultrafine Ni nanoclusters within an amorphous SiO2 matrix. This confined interfacial architecture effectively suppresses Ni migration, coalescence, and carbon nucleation during DRM. Consequently, the optimized 4Ni/A-SiO2 catalyst delivers CH4 and CO2 conversions of 93.06 % and 95.19 %, respectively, at 700 degrees C and a GHSV of 10,600 h-1, and remains stable for 400 h with negligible coke deposition. These results demonstrate that silane-mediated interfacial confinement provides an effective strategy for stabilizing nonprecious metal catalysts under harsh DRM conditions.
Against the backdrop of consumer utility loss caused by information asymmetry in fresh products and the impact of freshness preservation investment on supply chain performance, this paper examines how fresh produce suppliers and retailers can enhance consumer cognition by adopting Digital Trust Technology, which integrates AI and blockchain. It further analyzes the interactive effects of different cooperation modes and pre-sale strategies. Based on Stackelberg game theory, a two-echelon supply chain consisting of one supplier and one retailer is constructed. Optimal decisions and profits are derived through backward induction, and a pre-sale profit-sharing contract is designed to coordinate the supply chain. The study finds that the Digital Trust Technology enhances the competitiveness of the reselling mode, making it feasible in regions where no consensus previously existed, while simultaneously narrowing the consensus region for the agency selling mode. Under the agency selling mode, the supplier, who holds pricing power, is always willing to adopt the technology and raise the pre-sale price, thereby achieving a win–win outcome with the retailer. In contrast, under the reselling mode, although the retailer actively adopts the technology, the supplier lacks sufficient motivation to cooperate. Without coordination, the strategic conflict region accounts for most of the parameter space, and consensus can be achieved only under specific commission rates and pre-sale ratios. By designing a profit-sharing contract, the consensus region for the reselling mode can be significantly expanded, thereby achieving Pareto improvement. Furthermore, under the reselling mode, when the cost of preservation is high, it is actually beneficial to retailers and the entire supply chain.
Geological tectonic processes lead to the widespread development of joints and fractures in deep rock masses. During service, such rock masses are affected not only by rainfall infiltration and groundwater seepage but also by dynamic disturbances such as blasting and earthquakes. The combined effects of seepage, static stress, and dynamic loading alter the stress and deformation states of fracture surfaces, promoting crack initiation, propagation, and coalescence and thereby inducing complex failure responses. As structural weaknesses in rock masses, joints and fractures substantially reduce the overall load-bearing capacity and constitute key factors governing the mechanical response and failure modes of rock masses subjected to seepage and dynamic disturbances. To investigate the dynamic mechanical response and energy-absorption characteristics of bolted fractured rock under a simulated deep hydro-mechanical environment, bolts were fabricated from high-strength, high-toughness, and high-ductility negative Poisson’s ratio (NPR) steel, 45# steel, and Q235 steel. Hydro-mechanically coupled split Hopkinson pressure bar (SHPB) tests were then conducted to comparatively examine the dynamic strength, secant modulus, failure modes, and energy-dissipation characteristics of fractured sandstone reinforced with the three bolt types at different fracture inclination angles. The tests were performed under a fixed hydro-mechanical environment comprising an axial static stress of 27 MPa, a confining pressure of 27 MPa, and a seepage water pressure of 6 MPa, with fracture inclination angles of 0°, 15°, 30°, 45°, and 60°. The results show that NPR bolts markedly improve the dynamic load-bearing and energy-absorption capacities of fractured sandstone. The dynamic peak stresses of the NPR-bolted specimens were 10.22%–39.44% and 19.85%–48.12% higher than those of the specimens reinforced with 45# steel and Q235 steel bolts, respectively. For all three bolted specimen groups, the peak stress first increased and then decreased with increasing fracture inclination, while the NPR-bolted specimen reached a maximum peak stress of 80.00 MPa at an inclination of 15°. Failure-mode analysis indicates that the bolts restrained relative fracture displacement by carrying axial and transverse loads and enhancing resistance to sliding along the fracture surface. At 15°, the NPR-bolted specimen was characterized primarily by tensile cracking and slight particle spalling, without evident block instability. Based on the experimental analysis, a theoretical model incorporating dynamic loading, axial static stress, confining pressure, and seepage water pressure was established. Parameter sensitivity analysis was further conducted to examine the potential effects of environmental parameters on the distribution of axial and shear forces in the bolts, thereby revealing the governing role of the angle between the bolt and fracture plane in the mechanical response stages and energy evolution. These findings provide a theoretical basis and parameter support for the design of rock-bolt reinforcement in deep engineering rock masses subjected to dynamic disturbances.
As artificial intelligence (AI) becomes increasingly autonomous, understanding how human–robot collaboration affects consumer trust has emerged as a critical challenge in human–AI interaction research. Drawing on service orientation theory and social class theory, this research investigates how different forms of human–robot collaboration affects consumer trust in AI service robots. With three scenario-based studies, we examine the contingent roles of service orientation and social class, as well as the mediating role of human-robot interaction anxiety. The results reveal that robot-assisting collaboration generates greater consumer trust than robot-leading collaboration under a relational service orientation, whereas this difference disappears under a transactional service orientation. Furthermore, consumers from lower social classes exhibit higher trust in robot-assisting than in robot-leading collaboration, while no significant difference is found among consumers from higher social classes. By integrating service orientation and social class into the human–AI collaboration literature, this study advances our understanding of the psychological mechanisms through which human–AI collaboration affects consumer trust and offers actionable insights for the effective deployment of AI service robots in organizations.
