Jiangnan University

Ti6Al4V alloy is widely used in aircraft engine blades. However, its poor wear resistance and fatigue performance necessitate surface strengthening treatments. Abrasive water jet peening (AWJP) is an efficient surface enhancement technique that combines the advantages of both shot peening and water jet peening. This study comprehensively investigates the process of abrasive water jet peening on the curved surfaces of Ti6Al4V alloy in terms of surface roughness, hardness, and residual stress. The results indicate that, under the same processing parameters, the surface roughness of the concave surface is significantly higher, while the convex surface exhibits higher hardness and a notable difference in residual stress distribution. At higher pressures, material removal is more likely to occur on the convex surface, leading to the formation of pits in the jet core region. The optimized process results in a 38.5% reduction in roughness, a 12.8% increase in hardness, and a 286.2 MPa increase in residual compressive stress for the concave surface. For the convex surface, roughness decreased by 66.8%, hardness increased by 24.3%, and residual compressive stress increased by 314.3 MPa. This study provides valuable guidance for the strengthening of Ti6Al4V alloy blades.

Aerogels have been considered as super thermal insulators. However, they often exhibit less satisfactory mechanical properties for textile application due to their fragility and poor processability. These problems are overcome through a twisting‐induced fibrillation method, using 2D nanofiber membranes as mediators, to process aerogel particles into continuous macroscopic yarns based on a roll‐to‐roll format. A low concentration of strong nanofiber host in twisted form provides structural support to the aerogel particles and preserves their porous architecture. Despite the high‐volume ratio (≈78%) of aerogel particles, the aerogel yarns show high strength, ideal flexibility, and impressive thermal insulation properties. Textiles woven with the aerogel yarns are warmer than cashmere. The aerogel textile is extremely stable under temperatures of −196 and 100 °C, mechanical deformation, and even washing. This technology provides a flexible platform to extend the function (e.g., dyeability, hydrophobicity, and Joule heating) of aerogel yarns and textiles to suit various use scenarios.

This paper presents a novel Recurrent Neural Network (RNN) controller for redundancy resolution and orientation control of the Stewart platform. The Stewart platform features six prismatic actuators, making it a six‐degrees‐of‐freedom (6‐DOF) system. When imposing three‐dimensional orientation control, the platform retains a redundancy of 3‐DOF, which can be utilized to achieve secondary goals. The key novelty of this study lies in the formulation of a Jacobian‐free, gradient‐free control strategy that directly solves a constrained nonlinear optimization problem at the angular level, thereby significantly improving computational efficiency and robustness compared with conventional controllers. Specifically, we propose the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm, a biologically inspired metaheuristic framework that bypasses the computationally intensive Jacobian inversion typically required in redundancy resolution. The orientation control problem is formulated as a constrained optimization task, incorporating an energy‐efficient actuator usage objective and mechanical constraints modeled as inequalities. Theoretical stability and convergence guarantees are established for the proposed BAORNN framework, ensuring reliable operation across a wide range of configurations. To validate the approach, we developed a high‐fidelity simulation environment using the Simscape Multibody library in Simulink and conducted extensive experiments across multiple time‐varying reference trajectories. Quantitative performance comparisons against a state‐of‐the‐art inverse kinematics controller demonstrate the superior accuracy, convergence speed, and constraint‐handling capabilities of our method. Furthermore, we showcase a realistic application scenario by integrating the controller with a chair‐mounted Stewart platform for immersive driving and flight simulations, demonstrating the potential for real‐world deployment in motion simulation and training systems. In summary, this paper introduces a computationally lightweight, robust, and highly accurate RNN‐based controller tailored for redundant Stewart platforms, with proven advantages over traditional Jacobian–based methods.

Autonomous human-following remains one of the core challenges in mobile robotics and human-robot collaboration, playing a pivotal role in enhancing robotic intelligence. This paper presents a two-stage system integrating sensor fusion and trajectory optimization to address human-following challenges in unknown environments. The first stage introduces a robust person tracking method: leveraging LiDAR detection data with a motion filtering mechanism, we improve detection accuracy through camera association, while Ultra-Wideband devices mitigate target ambiguity in multi-person scenarios and fuse with LiDAR data to generate smooth and continuous trajectories. Crucially, the system maintains operational reliability in complete darkness through graceful degradation to a Camera-Free Mode. The second stage employs a local dynamic map to search for collision-free and dynamically feasible trajectories, enhanced through B-spline optimization for improved smoothness and safety. We introduce a novel adaptive strategy that dynamically adjusts trajectory temporal parameters based on target distance to ensure stable tracking. The system can be deployed on resource-constrained platforms. Experimental results show that the proposed human-following system can accurately identify target individuals among multiple pedestrians, handle occlusions effectively, and follow the target robustly while avoiding obstacles in unknown environments. The complete experimental video is available at https://youtu.be/6a3i7ua_14k.

Atomic-level crack propagation, interfacial delamination, as well as macroscopic network failure restrict huge applications of low-dimensional nanomaterials. It is significant from a fundamental standpoint to explore how mechanical properties of low-dimensional nanomaterials are influenced by these failures. In this review, recent progresses of the state-of-the-art experiments on failure behaviors of low-dimensional materials and their assemblies were summarized. Based on these experimental results, particular attention was paid to multiscale simulations revealing the microstructure failure mechanism hidden in experiments. First, various simulation methods, such as density functional theory, molecular dynamics simulation, as well as some continuum-based methods like atomic finite element simulation and peridynamics theory, for analyzing fractures of atomic-level monolayer low-dimensional nanomaterials were introduced. In addition, the modulation of fracture behaviors of these nanomaterials by defects was also discussed. Second, the interfacial strength and interfacial debonding of low-dimensional nanomaterials by theoretical models and simulations were described. Different interfacial optimization strategies for weakening or enhancing interfacial adhesion were addressed. Finally, various failure modes of these nanomaterial assemblies were addressed, in which the roles of internal factors and external factors were highlighted. This review will be useful for understanding the mechanism behind their failures and providing insight into atomic cracking, interfacial fracture, and macroscopic failure of van der Waals heterostructures.

The integration of plant proteins with bioactive compounds offers a promising strategy to enhance their environmental stability. This study explored the complexation of mung bean protein (MBP) with epigallocatechin gallate...

Phthalate esters (PAEs), widely employed as plasticizers, have garnered significant attention due to their multiple entry pathways into the environment, posing substantial threats to ecosystems. While current reviews predominantly focus on acute or high-dose toxicity in isolated environments or organisms, the present review addresses the critical knowledge gap. The present review encompasses peer-reviewed studies listed in the Web of Science from January 2017 to December 2024, excluding repetitive, irrelevant studies and those with invalid or incomplete data. Six common PAEs (BBP: butyl benzyl phthalate; DBP: di-n-butyl phthalate; DEHP: di(2-ethylhexyl) phthalate; DEP: diethyl phthalate; DMP: dimethyl phthalate; DOP: di-n-octyl phthalate) were listed as priority control contaminants by the US EPA. We comprehensively examine the environmental distribution and ecotoxicological impacts of these six PAEs. The toxicity differences among six PAEs were evaluated by integrating several indicators, such as oxidative stress, developmental disruption, endocrine dysfunction, metabolic alterations, reproductive impairment, and neurotoxicity. The mixture interactions were also examined because environmental exposure typically involves multiple PAEs and co-contaminants rather than single compounds. The PAE concentrations range from 0.00220 to 25.1 mg kg−1 and vary significantly with geographic location and soil cultivation type. Both aquatic organisms (e.g., fish and invertebrates) and soil organisms (e.g., earthworms and nematodes) exhibit pronounced toxic responses to various PAEs. The combined toxicity of PAEs with other environmental contaminants revealed synergistic/antagonistic effects—a critical consideration that is frequently overlooked in ecological risk assessments. The evidence presented provides a robust scientific foundation for updating current PAE regulations to address real-world exposure scenarios involving complex mixtures and long-term effects.

This paper focuses on programing mechanics of patterned spacer materials with double inlay-jacquard systems. Because of quite complex warp knitting paths and jacquard principles, an in-depth research on this double-jacquard techniques is conducted and then this paper proposes separate programing models of jacquard loop and jacquard underlap for the first time. Also, it studies a mapping method between the jacquard patterns design and colored loops formation process to automatically transfer what you design and see into what you knit. For the deviation between the design and the knitting result generated during the mapping process, an automatic detection method and an automatic correction algorithm are designed to ensure the accuracy of the mapping model. To comprehensively verify the programing model, an experimental design is exampled and then transferred into knitting parameters based on the programing system to fabricate a corresponding patterned material. This researched programing method shows great potential in increasing design efficiency and decreasing chemicals consumption in printing.

Hydrogel‐based wearable biosensors have revolutionized personal health monitoring due to their exceptional biocompatibility, flexibility, and adaptive functionality. These devices offer a significant advancement in healthcare by enabling personalized monitoring and diagnostics directly interfaced with the human body. To date, various hydrogel formulations have been developed using different fabrication techniques. However, they often face limitations such as low mechanical strength and susceptibility to permanent breakage in such monitoring systems. Further, the lack of dynamic cues and structural complexity within the hydrogels limit their range of functions. Recent developments have focused on overcoming these challenges by engineering hydrogels with enhanced physicochemical properties, ranging from advanced chemical compositions to integrating dynamic modulation and high‐tech architectures. Herein, the major advancements in designing and engineering hydrogels are reviewed and strategies targeting precise manipulation for their application in wearable biosensors.

This study employed a multidimensional approach combining clinical and animal experiments to elucidate the lipid‐modulating mechanisms of diacylglycerol (DAG). In a 12‐week intervention involving obese individuals, fasting serum triglyceride levels were significantly reduced in the DAG group compared to baseline. Within‐group reductions in triglycerides and low‐density lipoprotein (LDL) cholesterol were more pronounced in the DAG group than in the triacylglycerol (TAG) control group (p < 0.05). In a high‐fat diet‐induced obese mouse model, DAG significantly lowered serum total cholesterol, LDL levels, visceral fat weight (p < 0.05), attenuated hepatic steatosis, and altered hepatic lipid distribution. Lipidomic profiling revealed that DAG markedly downregulated hepatic triglycerides, ceramides, and monoacylglycerols, while normalizing sterol lipid levels. Pathway analyses based on differential lipids showed that DAG affected hepatic lipid composition mainly by intervening in the glycerophospholipid metabolism pathway. Mechanistically, DAG suppressed the expression of stearoyl‐CoA desaturase 1 and fatty acid synthase, while upregulating carnitine palmitoyltransferase 1, thereby enhancing hepatic lipid metabolism through dual regulation: inhibition of synthesis and promotion of catabolism and oxidation. These findings reveal DAG's structure‐dependent role in restoring lipid homeostasis and provide a theoretical foundation for functional lipid‐based strategies targeting metabolic disorders.

This paper introduces a novel hierarchical self-triggered control strategy for unknown dynamics based on transfer learning. The introduced methodology stratifies the self-triggered control strategy into upper and lower hierarchical layers which the upper-layer policy exercises oversight over the decision-making process of the lower-layer policy. Consequently, this hierarchical structure diminishes the search range for the lower-tier policy and enhances the efficacy of the learning procedure. Additionally, a parameter transfer fine-tuning method is developed to address initial value sensitivity in the policy network parameters of the learning process. Based on this setting, the shallow parameters are first frozen to achieve efficient reuse of prior knowledge of the pre-trained hierarchical network. Subsequently, the unfrozen deep parameters are fine-tuned to avoid policy failure caused by system parameter changes during the self-triggered control strategy learning for a new scenario. This proposed method eliminates the need to train the hierarchical Actor-Critic network from scratch, further reducing the time and computational resources required. Applied the developed method to a motor system demonstrates that 30% network training efficiency is improved.

Nowadays, plastic wastes have seriously endangered human health and ecological safety. Recycling plastics is a promising approach to achieve multiple uses of carbon resources. In this review, photocatalysis is introduced for the conversion of plastics into various valuable chemicals. The state-of-the-art photocatalytic techniques for plastic conversion are divided into two categories: direct and indirect photoconversion. We summarize in detail the photocatalytic small organic molecules conversion from polyethylene terephthalate, polylactic acid and polyethylene through the alkaline-assistant and hydrothermal pretreatments. Then, we overview the effective strategies of direct photoconverting polyethylene, polylactic acid and polyvinyl chloride into chemicals via the two-step process, amination strategy, and single reactive oxygen species-assistant strategy. Finally, we present some outlook on the current challenges and propose some potential solutions in the future.

Achieving effective exciton dissociation and charge transport in linear polymer photocatalysts for H2O2 photosynthesis remains a formidable challenge. Herein, we fabricated three‐motif cross‐linked polymers by rationally introducing a third functional component into a two‐motif linear polymer, which were employed for circulation‐flow photocatalytic H2O2 production. By strategically modulating the third component, we precisely tuned the electronic structure, significantly lowering exciton binding energy and enlarging the molecular dipole moment. Compared to the original linear configuration, the resulting cross‐linked structure creates multidirectional electron transport channels. Combined experimental and calculation investigations demonstrate that these synergistic effects collectively promote exciton dissociation and intramolecular electron transfer. PAQ‐TABPB photocatalyst with optimized third‐motif accelerates oxygen‐to‐superoxide radical transformation by lowering the *OOH binding energy, thereby facilitating the two‐step single‐electron oxygen reduction pathway, attaining an exceptional H2O2 production rate of 3351 µmol g⁻¹ h⁻¹. Notably, we constructed a circulation‐flow reactor for the photocatalytic synthesis of H2O2. Benefiting from improved gas‐liquid mass transfer and efficient light irradiation, this high‐speed flow system achieved a 5.2‐fold increase in H2O2 production compared to a conventional batch reactor under the light intensity of 27 mW cm⁻², reaching an accumulated yield of 3125 µmol g⁻¹ with stable recyclability. This work highlights the potential of multi‐component polymeric photocatalysts and circulation‐flow reactors for H2O2 photosynthesis.

Rheumatoid arthritis (RA) is a systemic autoimmune disease in which synovial fibroblasts (SFs) maintain chronic inflammation by secreting proinflammatory mediators, leading to joint destruction. While the role of proinflammatory mediators in this process is well-established, the contribution of non-inflammatory regulators in SFs to joint pathology remains poorly understood. In this study, we investigated the non-inflammatory role of SFs in RA using a co-culture model, and found that SFs from RA patients promote apoptosis of human chondrocytes. Mechanistic investigations reveal that SFs can secrete small extracellular vesicles (sEVs), which are taken up by chondrocytes and induce chondrocyte apoptosis in both normal chondrocytes and chondrocytes from patients with RA. sEV-derived miRNA 15-29148 are identified as key signaling molecules mediating the apoptosis effects of chondrocytes. Further studies reveal that SF-derived miRNA 15-29148 targeting CIAPIN1 results in increased chondrocyte apoptosis. We further demonstrate that SF-derived miRNA 15-29148 is transferred to chondrocytes, exacerbating cartilage damage in vivo. Moreover, chondrocyte-specific aptamer-modified polyamidoamine nanoparticles not only ameliorated RA but also prevented its onset. This study suggests that, in RA, the secretion of specific sEV-miRNAs from SFs plays a crucial role in promoting chondrocyte apoptosis, potentially through non-inflammatory regulation, and that sEV-miRNA inhibition in SFs may represent an early preventive treatment strategy for cartilage degradation in RA.