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The flexible robot is widely used in a variety of fields such as medical treatment, rescue and disaster relief, industry, and agriculture. Using elastic materials to prepare flexible robot body structures is the core of the study of flexible robots. Due to the small selection of materials, single preparation method, and long fabrication time, in this study, a new method of gas-assisted extrusion (GAE) of elastic material round-tube for flexible robot body was proposed, and the numerical simulation of GAE was carried out with nonsilicone elastic material round-tube under different viscosities. The results showed that with the change of viscosity, the velocity, pressure drop, and shear rate of melt in all directions change accordingly. When the viscosity is too small or too large, it is easy to bring negative effects on the GAE process of elastic materials. TPE and TPU were completely plasticized in the GAE, and the surface of the extruded elastic products was smooth and straight, with full gloss. Therefore, in the preparation of the flexible robot body, nonsilicone elastic materials and GAE forming methods can be considered.
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http://dx.doi.org/10.1021/acsomega.4c00967 | DOI Listing |
ACS Nano
September 2025
State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China.
Bimorph soft actuators, traditionally composed of two materials with distinct responses to external stimuli, often face durability challenges due to structural incompatibility. Here, we propose an alternative design employing free-standing, isostructural heterogeneous Janus (IHJ) films that harmonize stability with high actuation efficiency. These IHJ films were fabricated through a vacuum self-assembly process, consisting of TiCT MXene nanosheets and hybrid graphene oxide (GO)-biomass bacterial cellulose (BC), with a well-matched two-dimensional lattice structure.
View Article and Find Full Text PDFFront Hum Neurosci
August 2025
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Cocaine use disorder (CUD) is characterized by cortico-striatal circuit dysregulation and high relapse rates, with repetitive transcranial magnetic stimulation (rTMS) emerging as a potential neuromodulatory intervention. This study investigates rTMS-induced dynamic brain network reconfigurations in 30 CUD patients using longitudinal resting-state fMRI from the SUDMEX-TMS cohort. Applying Leading Eigenvector Dynamics Analysis (LEiDA) to phase-locking states, we identified four metastable network configurations mapped to canonical resting-state networks.
View Article and Find Full Text PDFThis paper presents a systematic literature review (SLR) on integration of robotics in hospitals and home-based educational settings. These schools provide essential educational environments that uphold children's right to education during prolonged illness. The review explores flexible didactic design, time adaptation, and personalized teaching approaches that are crucial in these contexts.
View Article and Find Full Text PDFNan Fang Yi Ke Da Xue Xue Bao
August 2025
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Objectives: To synthesize a temperature-responsive multimodal motion microrobot (MMMR) using temperature and magnetic field-assisted microfluidic droplet technology to achieve targeted drug delivery and controlled drug release.
Methods: Microfluidic droplet technology was utilized to synthesize the MMMR by mixing gelatin with magnetic microparticles. The microrobot possessed a magnetic anisotropy structure to allow its navigation and targeted drug release by controlling the temperature field and magnetic field.
Sci Rep
September 2025
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China.
The processing-transportation composite robots, with their dual functions of processing and transportation, as well as comprehensive robot-machine interactions, have been widely and efficiently applied in the manufacturing industry, leading to a continuous increase in energy consumption. Hence, this work focuses on investigating robot-machine integrated energy-efficient scheduling in flexible job shop environments. To address the new problem, an innovative mixed-integer linear programming model and a novel dual-self-learning co-evolutionary algorithm are proposed, aimed at minimizing the total energy consumption and makespan.
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