Browse Articles

Bionic Flexible Wrinkled Strain Sensors With Water‑Accelerated Self‐healing Capability for Underwater Detection and Motion Interaction

ABSTRACT Developing flexible sensors capable of long‐term, high‐sensitivity monitoring for flow fields and motion interactions is crucial for advancing unmanned underwater operations. However, flexible sensors always face challenges in achieving underwater superior self‐healing performance and high sensitivity simultaneously. Here, inspired by human skin, we present a method combining force‐driven wool spiral regulation with high‐temperature to fabricate a flexible sensor with random wrinkled microstructures. Importantly, the sensor resumed usable output within just 8 min of incurring damage because dynamic borate ester bonds were applied to accelerate its underwater self‐healing rate via hydrolysis‐re esterification reactions. This bionic self‐healing wrinkled flexible sensor achieves high underwater sensitivity, with average response and recovery time of only 124 ms and 112 ms, respectively, compared with most reported underwater flexible sensors with the same type of material of exceeding 200 ms. Notably, it maintains signal stability and functional continuity over 5,000 cycles following underwater self‐healing. We further demonstrated its use in underwater vehicle model and human motion for confirming its underwater continuously outputting stable and sensitive signals. These findings advance the underwater self‐healing flexible sensing units, with the potential to solve long‐term and high‐sensitivity condition monitoring towards underwater robotics, and water‐resistant human‐machine interfaces.

3D‐Printed Architected Cholesteric Liquid Crystal Displays With Spatiotemporal Color Modulation

ABSTRACT Cholesteric liquid crystals (CLCs) can exhibit reversible structural colors through selective reflection from their helical superstructures when well‐defined planar alignment and a visible‐range helical pitch are established. Although CLC photonic systems have been extended beyond planar cells to 3D geometries such as droplets, shells, and fibers, the integrated fabrication of complex architected CLC display structures with programmable spatiotemporal color control remains underexplored. Here, we introduce a multi‐material 3D printing approach that advances 3D‐printed CLCs from static photonic patterns to architected display systems with spatiotemporally controllable color output. Shear‐thinning CLC composite inks enable high‐fidelity extrusion while retaining reversible structural coloration, supporting patterned films and freestanding 3D photonic architectures. Co‐printed conductive Joule‐heating circuits form monolithic electrothermal devices, where local temperature and reflection wavelength are precisely encoded via circuit geometry. This strategy achieves spatiotemporally programmable multicolor outputs within a single device. Integrated into a soft robotic gripper, the system provides real‐time visual temperature feedback and enables adaptive actuation, establishing a scalable materials‐to‐device framework for programmable, interactive photonic architectures.

Lysosome‐Targeted Self‐Adjuvanting Ammonia Nanogenerator Potentiates Hepatocellular Carcinoma Immunotherapy via Ammonia Death

ABSTRACT Ammonia death is a recently identified form of regulated cell death with unique molecular mechanisms and prominent anticancer activity. Nevertheless, its efficacy is severely restricted by the absence of tumor‐targeted ammonia delivery vehicles and poorly defined immunogenic properties. Herein, we develop a lysosome‐targeted ammonia nanogenerator (denoted AlN@HA) to induce ammonia death in hepatocellular carcinoma (HCC) cells and boost HCC immunotherapy. Following CD44 receptor‐mediated endocytosis, AlN@HA preferentially accumulates within lysosomes and undergoes in situ hydrolysis to produce excessive ammonia and nanoscopic aluminum hydroxide (Al(OH) 3 ). Intralysosomal ammonia overload further drives lysosomal alkalinization and membrane permeabilization, autophagic flux blockade, and mitochondrial dysfunction. This sequential signaling cascade elicits tumor cell ammonia death and triggers robust immunogenic cell death. Meanwhile, the hydrolytic byproduct Al(OH) 3 functions as an intrinsic adjuvant to facilitate dendritic cell maturation. Additionally, ammonia‐mediated neutralization of intratumor lactic acid reverses the immunosuppressive tumor microenvironment. In vivo results verify that AlN@HA‐initiated ammonia death markedly suppresses local tumor proliferation and activates systemic antitumor immune responses, thereby sensitizing HCC to antiprogrammed cell death 1 immunotherapy. This study clarifies the immunological features of tumor ammonia death, establishes a lysosome‐targeted ammonia delivery strategy, and highlights ammonia death as a viable synergistic modality for HCC combination immunotherapy.

Liquid Crystal Elastomer‐Based Haptic Pixel Arrays at Your Fingertips for Advanced Human–Machine Interfaces

ABSTRACT The sense of touch is underutilized in our digital lives, especially when compared to the prevalence and complexity of information transfer through our visual and auditory interactions with devices; the impact of this is profound as touch plays a major role in our perception of our world, and its omission in our commonly used devices limits our accessibility to the digital space, particularly for blind and visually impaired users. Fortunately, the stimuli‐responsive shape‐morphing properties of liquid crystal elastomers (LCEs) promise a solution: in our work, we mechanically program an LCE bilayer coating to achieve durable, high‐amplitude switchable surface protrusions, or tactile pixels, which we assemble into a digital platform for generating tactile images and haptic feedback. We further characterize and describe our tactile pixel design by microscopy techniques and simulations that use the neo‐classical theory of rubber elasticity. We demonstrate safe and effective electronically addressable activation of individual LCE pixels within seconds, accompanied by a psychophysical evaluation of tactile line orientation discrimination in which the performance of our LCE tactile pixels is comparable to swell paper, a standard tactile graphic medium. Thereby, we establish LCEs as a contender for the next‐generation dynamic tactile displays.

Antiphase Boundaries Regulate Phase Stability and Performance in DMA ⁺ ‐Assisted CsPbI ₃ ‐Based Perovskites

ABSTRACT CsPbI 3 ‐based perovskites are promising absorbers for tandem solar cells owing to their optimal bandgap (∼1.7 eV). However, the phase transition from photoactive γ‐CsPbI 3 to non‐photoactive δ‐CsPbI 3 remains a major obstacle and is strongly governed by microstructural defects formed during film growth. Among these, Ruddlesden–Popper antiphase boundaries (RP‐APBs) are particularly prevalent and exhibit competing effects, relieving lattice strain while simultaneously facilitating moisture penetration, ion migration, and nonradiative recombination. Here, we systematically regulate RP‐APB defects in γ‐phase CsPbI 3 thin films and elucidate their decisive influence on both phase stability and optoelectronic performance. A compositional strategy based on PbI 2 excess effectively reduces RP‐APB density but induces edge‐sharing [PbI 6 ] 4− motifs that nucleate the δ phase. In contrast, a dimethylammonium (DMA + )‐assisted phase‐engineering strategy forms β‐(DMA,Cs)PbI 3 , which intrinsically suppresses RP‐APB formation while preserving the photoactive perovskite framework. As a result, RP‐APB‐free β‐phase films exhibit prolonged carrier lifetimes, strongly suppressed nonradiative recombination, and the lowest apparent trap densities, enabling a champion power conversion efficiency of 20.23% together with markedly enhanced operational, thermal, and ambient‐air stability. This work demonstrates that regulating crystalline defects, exemplified by RP‐APBs, plays a critical role in achieving both stable and efficient perovskite solar cells.

Machine Learning‐Guided Additive Manufacturing of Multilayer Aerogels for Ultrabroadband, Ultralow‐Reflection Electromagnetic Shielding

ABSTRACT The development of broadband, low‐reflection electromagnetic interference (EMI) shielding materials is critically needed to suppress secondary electromagnetic pollution. Here, we report a machine learning (ML)‐guided additive manufacturing strategy for precisely fabricating multilayer gradient transition metal carbides/nitrides (MXene)‐based aerogels with spatially programmed electrical conductivity. Our approach synergistically integrates sustainable cellulose nanofibers with a utilization MXene dispersion, genetic algorithm‐enabled structural optimization, and direct‐ink writing for precise fabrication. The resulting aerogels achieve benchmark EMI shielding performance, characterized by an ultralow average reflectivity (R) of 0.045 and sustained absorptivity (A) above 0.9 over an ultrabroad bandwidth of 30.3 GHz (9.7–40.0 GHz), remarkably surpassing existing materials. This success, validated by the close agreement among ML predictions, simulations, and experiments, demonstrates a powerful data‐driven paradigm. Consequently, this study establishes a comprehensive blueprint for the ML‐accelerated development of next‐generation, intelligent electromagnetic protection systems centered on lightweight, absorption‐dominant aerogels.

Synthesis of adaptive 15N-nitrosyl-Co7 nanocluster for electrocatalytic C–H functionalization

Pan-Ebolavirus nanoparticle vaccine provides protection in rodents from lethal infection by Zaire and Sudan viruses

Abstract Both Zaire ebolavirus (EBOV) and Sudan ebolavirus (SUDV) are members of the genus Ebolavirus and cause outbreaks marked by high fatality rates and repeated spillover from animal reservoirs. Filoviral glycoproteins (GPs) are the primary targets of neutralizing antibodies and form the basis of current vaccines. Here we describe the design, structural characterization, and evaluation of two-component self-assembling icosahedral I53-50 nanoparticles displaying prefusion EBOV or SUDV GP antigens, either individually or in cocktail and mosaic multivalent formats. EBOV-GP-I53-50 and SUDV-GP-I53-50 nanoparticles elicited strong homologous protection in mice and guinea pigs. In the mouse-adapted EBOV model (maEBOV), mosaic and cocktail formulations produced weak survival below that of the matched EBOV-GP-I53-50 GP immunogen. In contrast, in the gpaSUDV guinea pig model (gpSUDV), cocktail and mosaic nanoparticles elicited robust protection against gpSUDV, and detectable antibody responses to both SUDV and EBOV GPs. These findings demonstrate that multivalent GP-I53-50 nanoparticle immunogens can protect rodents from death, severe clinical signs of disease, and weight loss in relevant models. Together with the established clinical safety of the I53-50 platform, these results support continued efforts toward the development of a pan-ebolavirus vaccine.

Multi-population GWAS meta-analysis identifies bladder cancer susceptibility loci and highlights genetic regulation of smoking-related risk

Genome-wide annotation of human multi-nucleotide variants reveals widespread functional differences from single nucleotide variants

Allele-specific chromatin architecture shapes imprinted domains and coordinates a distal enhancer and antisense transcription at the mouse Mest-Copg2 domain

Entropy plateaus, combinatorial degeneracy, local moments, and heavy-fermion mass renormalization in magic-angle graphene

Abstract Heavy-fermion behavior, driven by the interaction-induced enhancement of electronic mass, underpins exotic states ranging from unconventional superconductivity to quantum criticality. Long restricted to complex three-dimensional f- electron compounds, these phenomena are now predicted to emerge within the flat bands of magic-angle twisted bilayer graphene. Here, we use high-resolution planar tunneling spectroscopy to perform inverse-compressibility and entropy measurements, providing direct evidence for topological heavy-fermion behavior in magic-angle graphene. Our inverse-compressibility data reveal strong, filling-dependent mass renormalization, consistent with the hybridization between localized and itinerant electrons within a Kondo-lattice framework. Furthermore, entropy spectroscopy reveals two plateau structures whose degeneracies directly encode the combinatorial structure of local moment states. This 8-to-4-fold degeneracy reduction is an  entropic signature of strain-mediated symmetry breaking, consistent with the topological heavy-fermion model.

Quantitative RNA modification mapping by mass spectrometry with isobaric tags and nucleobase fragment analysis

Stochastic thermodynamics of social imitation beyond energetics

Macrophages tune NK cell IFNγ production through direct lipid transfer

Abstract Natural killer (NK) cells are critical effectors of innate immunity, but their activity is strongly influenced by metabolic state. While intrinsic NK metabolism has been studied extensively, less is known about how surrounding immune cells shape NK cell function. Here, we identify a direct metabolic communication axis between macrophages and NK cells. Using co-culture and in vivo models, we show that lipopolysaccharide-stimulated macrophages induce lipid accumulation in NK cells that suppresses mTORC1 activity and the production of IFNγ. This lipid accumulation is visualised as increased lipid droplets content in NK cells, generated using fatty acids synthesised within the macrophages. Genetic and pharmacological approaches show that fatty acid transfer from macrophages to NK cells requires cell-cell contact and is associated with CD36 protein transfer via trogocytosis. Blocking fatty acid synthesis specifically in macrophages prevents lipid accumulation in NK cells and restores both mTORC1 activity and IFNγ production. These findings define a previously unrecognized mechanism of macrophage-NK cell cross-regulation, revealing how metabolic exchange constrains NK effector function and establishing a feedback circuit with implications for hyperinflammation and immunotherapy.

Benchmarking RNA-seq with the Quartet and MAQC reference materials to establish best practices for accurate alternative splicing analysis

TiO2-facet-dependent reconstruction of Pt nanoparticles during CO oxidation

Spatially harnessing oxygenase enables paired and ultraselective electrooxidative waste depolymerization

Self-Assembled Multilayers Reduce Interfacial Energy Loss in Perovskite Solar Cells

Abstract The use of self-assembled multilayer (SAM) layers as hole transport layers (HTLs) represents a major advance for high-efficiency perovskite solar cells (PSCs). However, many SAMs materials suffer from aggregation, poor wettability, and weak interactions with the perovskite, which hinder charge transfer and cause energy losses that limit both power conversion efficiency (PCE) and long-term stability. In this study, we synthesized two SAMs, namely 2-(10-(3,5-dimethoxyphenyl)−7H-benzo[c]carbazol-7-yl)ethyl)phosphonic acid (denoted as DMPA) and 2-(7H-benzo[c]carbazol-7-yl)ethyl)phosphonic acid (denoted as BCPA). DMPA SAM effectively suppresses self‑aggregation and enhances substrate coverage. The methoxy groups in DMPA interact with the perovskite, thereby enabling DMPA to passivate defects at the buried interface and optimize perovskite crystallization. These interfacial improvements facilitate more efficient charge extraction and enhance interfacial stability. As a result, DMPA-based PSCs achieve a PCE of 27.59% (certified PCE of 27.2%) and show remarkable photothermal stability, retaining 94.5% of their initial efficiency after 1600 hours of continuous illumination under 1 Sun at 65 °C.

Human learning of probability distributions is biased toward moderate structural complexity

Abstract Inferring hidden environmental structures, which commonly involves learning arbitrary probability distributions from limited samples, is essential to optimal and adaptive behaviors across various cognitive domains. However, it remains largely unknown how the internal representations constructed by humans may deviate from actual probabilistic structures, and what computational processes, operated under inherent cognitive limitations, give rise to these representations. We first develop a structured distribution report task to reveal human participants’ internal representations, with findings verified in a further distribution recognition task. Across eight behavioral experiments (including one pre-registered study) in two modalities, participants estimate the overall probability density reasonably well, but exhibit a systematic bias toward moderate structural complexity, reporting too many clusters when the true distribution is a single Gaussian and too few when it contains many clusters. We then build a series of learning models in the framework of approximate Bayesian inference fit to our behavioral data. Through model comparisons, we reconstruct the prior beliefs guiding the evolution of participants’ internal representations. The best-fitting model for human reports reduces structure growth rate as complexity increases, effectively constraining the complexity of internal representations within memory limitations.