8 research outputs found
Kinematic-based locomotion mode recognition for power augmentation exoskeleton
No full text This article presents a kinematic-based method for locomotion mode recognition, for use in the control of an exoskeleton for power augmentation, to implement natural and smooth locomotion transition. The difference in vertical foot position between a foot already in contact with ground and a foot newly in contact with the ground was calculated via kinematics for the entire exoskeleton and used to identify the locomotion mode with other sensor data including data on the knee joint angle and inclination of the thigh, shank, and foot. Locomotion on five different types of terrain—level-ground walking, stair ascent, stair descent, ramp ascent, and ramp descent—were identified using two-layer decision tree classes. An updating process is proposed to improve identification of the transition and accuracy using the foot inclination at the mid-stance. An average identification accuracy of more than 99% was achieved in experiments with eight subjects for single terrains (no terrain transitions) and hybrid terrains. The experimental results show that the proposed method can achieve high accuracy without significant misrecognition and minimize the delay in locomotion mode recognition of the exoskeleton. </jats:p
Design and locomotion control of a hydraulic lower extremity exoskeleton for mobility augmentation
No full textAI and Quantum Computing for Advanced Materials Design
No full textThe AI-driven inverse design paradigm is fundamentally transforming materials discovery research by enabling the computational exploration of novel materials with predefined target properties. This review comprehensively synthesizes recent progress in applying AI methodologies, such as generative models, reinforcement learning, and diffusion models, to diverse material classes including metals, polymers, and proteins. It particularly highlights key advancements, such as the AI-guided discovery of high-entropy alloys with superior mechanical properties and the de novo design of functional polymers and protein-based biomaterials. Furthermore, major remaining challenges are discussed, including the computational-to-experimental validation gap, data scarcity, and the need for physically constrained models. Furthermore, this review explores the emerging frontier of Quantum Machine Learning (QML), which holds the promise of overcoming the limitations of classical computing for particularly complex problems in materials simulation. Finally, the integration of these methodologies into fully autonomous laboratories for closed-loop design, synthesis, and characterization is presented as a transformative route to accelerate the materials discovery cycle.
Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study
No full textThis
study investigated the impact of electric fields on Nephila
clavipes spider silk using molecular dynamics modeling.
Electric fields with varying amplitudes and directions were observed
to disrupt the β sheet structure of spider silk and reduce its
mechanical properties. However, a notable exception was observed when
a 0.1 V/nm electric field was applied in the antiparallel direction,
resulting in improvements in Young’s modulus and ultimate tensile
strength. The antiparallel direction was observed to be particularly
sensitive to electric fields, causing disruptions in beta sheets and
hydrogen bonds, which significantly influence the mechanical properties.
This study demonstrates that spider silk maintains its structural
integrity at 0.1 V/nm. Possibly, lowering the power levels of typical
electrospinning machines can prevent secondary structural disruption.
These findings provide valuable insights for enhancing silk fiber
production and applications using natural silk proteins while shedding
light on the impact of electric fields on other silk proteins. Finally,
this study opens up possibilities for optimizing electrospinning processes
to enhance performance in various silk electrospinning applications
Polyphenols Coordinated with Cu (II) in an Aqueous System Build Ion-Channel Coatings on Hair Surfaces
No full textRecently, developments in the field of cosmetics have led to a renewed interest in hair dyeing. However, damage to the hair during the dyeing process has increased hesitation in attempting hair dyeing. As a result, hair dyes with minimal side effects have been in constant demand, and are being developed. In this study, natural-extract polyphenols, pyrogallol, and gallic acid are coordinated by CuCl2 in a NaCl aqueous solution to form an oligomer, which creates an ion-channel coating on the hair surface to protect it. This work attempts to develop fast, simple, and damage-free hair-dye ingredients based on pyrogallol and gallic acid. The morphology and elements of polyphenols coated on hair are characterized. The results reveal that the hair is dyed with the polyphenol-based dye reagent successfully. Moreover, the thickness of the dyed hair continuously rises ten times after dyeing. The tensile strength of the dyed hair is also measured, showing an upward and downward trend. These results reflect the fact that pyrogallol and gallic acid are considered to be the essential and functional polyphenols, and can build ion blocks on hair, which can create new multifunctional coating materials
