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At ambient conditions, the high-entropy alloy superconductor R⁢e0.6⁢(NbTiZrHf)0.4 exhibits exceptional mechanical properties among high-entropy alloys, with its hexagonal phase achieving nanoindentation hardness of 18.5 GPa. We report on a unique pressure-induced structural transformation from a hexagonal phase to a body-centered cubic (BCC) phase, revealed by synchrotron x-ray diffraction measurements up to 70 GPa. This first-order transition, accompanied by a 6.1% volume collapse, occurs at 44 GPa and results in a BCC structure with random site occupancy by the five constituent elements, which is remarkably retained upon decompression to ambient conditions. The transformation proceeds via a martensiticlike, diffusionless mechanism without elemental segregation, enabled by pressure-induced electronic redistribution and atomic-scale disorder. These findings demonstrate a rare case of metastable phase retention in a chemically complex alloy and offer new insights into structure-stability relationships under pressure. 

Soft robots, constructed from deformable materials, offer significant advantages over rigid robots by mimicking biological tissues and providing enhanced adaptability, safety, and functionality across various applications. Central to these robots are electroactive polymer (EAP) actuators, which allow large deformations in response to external stimuli. This review examines various EAP actuators, including dielectric elastomers, liquid crystal elastomers (LCEs), and ionic polymers, focusing on their potential as artificial muscles. EAPs, particularly ionic and electronic varieties, are noted for their high actuation strain, flexibility, lightweight nature, and energy efficiency, making them ideal for applications in mechatronics, robotics, and biomedical engineering. This review also highlights piezoelectric polymers like polyvinylidene fluoride (PVDF), known for their flexibility, biocompatibility, and ease of fabrication, contributing to tactile and pressure sensing in robotic systems. Additionally, conducting polymers, with their fast actuation speeds and high strain capabilities, are explored, alongside magnetic polymer composites (MPCs) with applications in biomedicine and electronics. The integration of machine learning (ML) and the Internet of Things (IoT) is transforming soft robotics, enhancing actuation, control, and design. Finally, the paper discusses future directions in soft robotics, focusing on self-healing composites, bio-inspired designs, sustainability, and the continued integration of IoT and ML for intelligent, adaptive, and responsive robotic systems. 

Abstract Analysis of ion-kinetic instabilities in solar wind plasmas is crucial for understanding energetics and dynamics throughout the heliosphere, as evident from spacecraft observations of complex ion velocity distribution functions (VDFs) and ubiquitous ion-scale kinetic waves. In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. Using 34 hybrid particle-in-cell simulations of kinetic protons andα-particles initialized using plasma parameters derived from solar wind (SW) observations, we prepare a data set of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. We compare feature-based classifiers applied to VDF moments, such as support vector machine and random forest (RF), with DL convolutional neural networks (CNNs) applied directly to VDFs as images in the gyrotropic velocity plane. The best-performing classifier, RF, has an accuracy of 0.96 ± 0.01, and a true skill score of 0.89 ± 0.03, with the majority of missed predictions made near stability thresholds. We study how the variations of the temporal derivative thresholds of anisotropies and magnetic energies, and sampling strategies for simulation runs, affect classification. CNN-based models have the highest accuracy of 0.88 ± 0.18 among all considered if evaluated on the runs entirely not used during the model training. The addition of theE_⊥power spectrum as an input for the ML models leads to the improvement of instability analysis for some cases. The results demonstrate the potential of ML and DL for the detection of ion-scale kinetic instabilities using spacecraft observations of SW and magnetospheric plasmas. 

Pooled single-cell perturbation screens represent powerful experimental platforms for functional genomics, yet interpreting these rich datasets for meaningful biological conclusions remains challenging. Most current methods fall at one of two extremes: either opaque deep learning models that obscure biological meaning, or simplified frameworks that treat genes as isolated units. As such, these approaches overlook a crucial insight: gene co-fluctuations in unperturbed cellular states can be harnessed to model perturbation responses. Here we present CIPHER (Covariance Inference for Perturbation and High-dimensional Expression Response), a framework leveraging linear response theory from statistical physics to predict transcriptome-wide perturbation outcomes using gene co-fluctuations in unperturbed cells. We validated CIPHER on synthetic regulatory networks before applying it to 11 large-scale single-cell perturbation datasets covering 4,234 perturbations and over 1.36M cells. CIPHER robustly recapitulated genome-wide responses to single and double perturbations by exploiting baseline gene covariance structure. Importantly, eliminating gene-gene covariances, while retaining gene-intrinsic variances, reduced model performance by 11-fold, demonstrating the rich information stored within baseline fluctuation structures. Moreover, gene-gene correlations transferred successfully across independent experiments of the same cell type, revealing stereotypic fluctuation structures. Furthermore, CIPHER outperformed conventional differential expression metrics in identifying true perturbations while providing uncertainty-aware effect size estimates through Bayesian inference. Finally, most genome-wide responses propagated through the covariance matrix along approximately three independent and global gene modules. CIPHER underscores the importance of theoretically-grounded models in capturing complex biological responses, highlighting fundamental design principles encoded in cellular fluctuation patterns. 

Abstract Using genetic code expansion (GCE) to encode bioorthogonal chemistry has emerged as a promising method for protein labeling, both in vitro and within cells. Here, we demonstrate that tetrazine amino acids incorporated into proteins are highly tunable and have extraordinary potential for fast and quantitative bioorthogonal ligations. We describe the synthesis and characterize reaction rates of 29 tetrazine amino acids (20 of which are new) and compare their encoding ability into proteins using evolved Tet ncAA encoding tRNA/RS pairs. For these systems, we characterized on-protein Tet stability, reaction rates, and ligation extents, as the utility of a bioorthogonal labeling group depends on its stability and reactivity when encoded into proteins. By integrating data on encoding efficiency, selectivity, on-protein stability, and in-cell labeling for Tet tRNA/RS pairs, we developed the smallest, fastest, and most stable Tet system to date. This was achieved by introducing fluorine substituents to Tet4, resulting in reaction rates at the 10⁶ M⁻¹s⁻¹ level while minimizing degradation. This study expands the toolbox of bioorthogonal reagents for Tet-sTCO-based, site-specific protein labeling and demonstrates that the Tet-ncAA is a uniquely tunable, highly reactive, and encodable bioorthogonal functional group. These findings provide a foundation to further explore Tet-ncAA encoding and reactivity.