Soft robots have the appealing advantages of being highly flexible and adaptive to complex environments. However, the low‐stiffness nature of the constituent materials makes soft robotic systems ...incompetent in tasks requiring relatively high load capacity. Despite recent attempts to develop stiffness‐tunable soft actuators by employing variable stiffness materials and structures, the reported stiffness‐tunable actuators generally suffer from limitations including slow responses, small deformations, and difficulties in fabrication with microfeatures. This work presents a paradigm to design and manufacture fast‐response, stiffness‐tunable (FRST) soft actuators via hybrid multimaterial 3D printing. The integration of a shape memory polymer layer into the fully printed actuator body enhances its stiffness by up to 120 times without sacrificing flexibility and adaptivity. The printed Joule‐heating circuit and fluidic cooling microchannel enable fast heating and cooling rates and allow the FRST actuator to complete a softening–stiffening cycle within 32 s. Numerical simulations are used to optimize the load capacity and thermal rates. The high load capacity and shape adaptivity of the FRST actuator are finally demonstrated by a robotic gripper with three FRST actuators that can grasp and lift objects with arbitrary shapes and various weights spanning from less than 10 g to up to 1.5 kg.
A fast‐response, stiffness‐tunable (FRST) soft actuator is fabricated by hybrid multimaterial 3D printing. Owing to the thermomechanical properties of an embedded shape memory polymer layer, the actuator exhibits flexibility when heated and high stiffness (120 times stiffer than its purely elastomeric counterpart) when cooled. Assisted by Joule‐heating and fluidic cooling, the heating–cooling cycle is completed within 32 s.
Metallic glasses (MGs), which are also known as amorphous metals, are formed by quenching the melts at a super high cooling rate (e. g. 106 K/s) to avoid crystallization. Compared with ordinary ...metals, there is no long‐range translational order and crystalline defects in MGs. Benefitting from this unique structural characteristic, MGs show many superior mechanical, physical and chemical properties and thus have been attracting intensive attentions in applications as structural materials. The investigations on the functional properties of MGs are still at the early stage, but immense potential in electrochemically functional applications has been demonstrated in recent years. In this Minireview, we aim to present an overview of the functional properties of this class of novel metallic glassy catalysts and some achievements obtained so far. We mainly focus on the applications of MGs in various electrochemical applications, like hydrogen evolution reaction and oxygen evolution reaction as well as fuel cells. The strategies for optimizing the performance of these amorphous catalysts are discussed as well, including high‐throughput screening and nano‐engineering. In the absence of crystalline ordering, the atomic‐scale structural mechanism of MGs in electrocatalysis may be unique and will be explored. Finally, we also give perspectives on how to design novel and superior electrocatalysts for future electrocatalysis applications.
Metal or Glass? Metallic glasses are novel alloy materials with disordered atomic arrays. Due to the unique amorphous structure, they have attractive mechanical, physical and chemical properties which are promising for various functional applications. Recently, more and more researches demonstrated the superior activity and durability of metallic glassy catalysts in electrocatalysis. These innovative outcomes indicate the possibility of implementing metallic glasses as functional materials for various electrocatalytic applications, such as hydrogen evolution reaction, oxygen evolution reaction and fuel cells.
Abstract
The recent discovery of non-classical crystal nucleation pathways has revealed the role of fluctuations in the liquid structural order, not considered in classical nucleation theory. On the ...other hand, classical crystal growth theory states that crystal growth is independent of interfacial energy, but this is questionable. Here we elucidate the role of liquid structural ordering in crystal nucleation and growth using computer simulations of supercooled liquids. We find that suppressing the crystal-like structural order in the supercooled liquid through a new order-killing strategy can reduce the crystallisation rate by several orders of magnitude. This indicates that crystal-like liquid preordering and the associated interfacial energy reduction play an essential role in nucleation and growth processes, forcing critical modifications of the classical crystal growth theory. Furthermore, we evaluate the importance of this additional factor for different types of liquids. These findings shed new light on the fundamental understanding of crystal growth kinetics.
Abstract This study highlights the need for climate-sensitive urban planning and design in the face of climate change, with a specific focus on Singapore. Rapid urbanization has led to significant ...warming trends, increased heat stress, and heightened electricity demand for cooling. The Urban Climate Design Lab (UCDL) at the National University of Singapore employs a multidisciplinary approach, merging urban planning, architecture, and urban climate science. The research introduces GIS-based tools to evaluate the microclimate impact of new developments, replacing time-consuming simulations and wind tunnel experiments. These tools encompass: 1) The Urban Wind Environment Model, assessing urban permeability for natural ventilation; 2) The Fine-Scale Wind Environment Model, providing high-resolution pedestrian-level wind speed data. 3) The Urban Tree-Airflow Model, aiding tree placement and species selection for optimal cooling. 4) The Anthropogenic Heat Dispersion Model, estimating the impact of human-generated heat emissions. These GIS tools are integrated into the open-access UCDL Microclimate Digital Platform, facilitating knowledge transfer and empowering stakeholders in climate-sensitive urban planning. The platform offers various climate models and visualization capabilities to enhance evidence-based decision-making for urban climate sustainability and resilience. In the future, the platform will expand its offerings, becoming a valuable resource for urban planners, engineers, health practitioners, environmental experts, and residents adapting to a changing climate.
The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global crisis. Replication of SARS-CoV-2 requires the viral ...RNA-dependent RNA polymerase (RdRp) enzyme, a target of the antiviral drug remdesivir. Here we report the cryo-electron microscopy structure of the SARS-CoV-2 RdRp, both in the apo form at 2.8-angstrom resolution and in complex with a 50-base template-primer RNA and remdesivir at 2.5-angstrom resolution. The complex structure reveals that the partial double-stranded RNA template is inserted into the central channel of the RdRp, where remdesivir is covalently incorporated into the primer strand at the first replicated base pair, and terminates chain elongation. Our structures provide insights into the mechanism of viral RNA replication and a rational template for drug design to combat the viral infection.
The present study aims to assess the superiority of the metaheuristic evolutionary when compared to the conventional machine learning classification techniques for landslide occurrence estimation. To ...evaluate and compare the applicability of these metaheuristic algorithms, a real-world problem of landslide assessment (i.e., including 266 records and fifteen landslide conditioning factors) is selected. In the first step, seven of the most common traditional classification techniques are applied. Then, after introducing the elite model, it is optimized using six state-of-the-art metaheuristic evolutionary techniques. The results show that applying the proposed evolutionary algorithms effectively increases the prediction accuracy from 81.6 to the range (87.8–98.3%) and the classification ratio from 58.3% to the range (60.1–85.0%).
Six population-based hybrid algorithms are applied to train the multilayer perceptron (MLP) to improve the classification accuracy, in the stability assessment. A complex problem of slope stability ...against failure is designed in Optum G2 software. Considering four key factors of shear strength of clayey soil, slope angle, the ratio of foundation distance from the slope to the foundation length, and the applied surcharge, the stability or failure of the proposed slope are anticipated. The provided data are used to develop the MLP combined with biogeography-based optimization (BBO), ant colony optimization (ACO), genetic algorithm (GA), evolutionary strategy (ES), particle swarm optimization (PSO) and probability-based incremental learning (PBIL). The results revealed that the BBO-MLP with the obtained area under the receiving operating characteristic curve (AUROC) of 0.995 and the classification ratio (CR) of 92.4% is the most accurate model followed by GA-MLP (AUROC = 0.960 and CR = 84.3%), PBIL-MLP (AUROC = 0.948 and CR = 79.3%), ES-MLP (AUROC = 0.879 and CR = 65.7%), PSO-MLP (AUROC = 0.878 and CR = 71.3%), and ACO-MLP (AUROC = 0.798 and CR = 60.7%).
PDF
