Developing sustainable energy technologies is among the foremost challenges of this century and spawns the emergence of materials for ambient energy harvesting, such as photovoltaics, triboelectrics, and pyroelectrics. The conversion of ambient thermal energy (temperature fluctuations) into electricity through pyroelectric materials offers a promising route toward sustainable energy technologies. However, there is a critical limitation: The conventional pyroelectric effect is inherently restricted to noncentrosymmetric crystals, but excludes the centrosymmetric materials, even though they exhibit otherwise favorable properties. This symmetry dependence severely constrains the development of pyroelectric energy technology. Here, we demonstrate that the pyroelectric effect, typically observed only in noncentrosymmetric materials, can be induced in centrosymmetric materials via the flexoelectric effect. By introducing strain gradients using an atomic force microscope, we generated a giant pyroelectric coefficient of up to 1.25 × 10⁶ μC·m⁻²·K⁻¹ in SrTiO₃. This strain gradient-induced pyroelectric effect, termed as the flexo-pyroelectric effect, decouples pyroelectric functionality from intrinsic material polarity. Our findings exceed the long-standing symmetry limitation in pyroelectric energy technology by proving that centrosymmetric materials can also exhibit robust pyroelectricity through strain engineering. The flexo-pyroelectric effect redefines the understanding of pyroelectricity and unlocks the vast library of centrosymmetric materials for designing next-generation energy harvesters, advancing sustainable technology development.

Addressing the critical demand for next-generation lightweight, high-efficiency microwave absorbers, this paper proposes a “micro-meso-macro” multi-scale synergistic regulation strategy. Specifically, core@shell mesoporous carbon hollow nanospheres (HNSs)@carbon bean-shaped nanofibers (BNFs) are designed and fabricated efficiently using SiO₂/carbon solid nanospheres as precursor through a continuous electrostatic spinning, heat treatment, carbonization, and hydrofluoric (HF) etching. The acquired results suggest that the regulation of carbonization temperature greatly improves the graphitized degree of mesoporous carbon HNSs@carbon BNFs, which significantly enhances the values of complex permittivity. Furthermore, the introduction of a controllable number of mesoporous carbon HNSs at the mesoscale significantly increases the specific surface area and promotes the interfacial polarization effects. The macroscopic 3-dimensional continuous conductive network constructed via electrospinning further enhances electron transport capability and conductive loss efficiency. Benefiting from the excellent collaborative design between multi-scale structure and composition, the optimized mesoporous carbon HNSs@carbon BNFs display excellent microwave absorption properties with a minimum reflection loss (RL_min) of −61.03 dB at 2.42 mm and an effective absorption bandwidth (EAB) of 6.2 GHz at 2.18 mm. Meanwhile, the acquired mesoporous carbon HNSs@carbon BNFs also present excellent corrosion resistance, hydrophobicity, flexibility, and lightweightness. Generally, the finding proposes a simple route for the production of novel core@shell C@C nanocomposites, which makes the best of multi-scale construction strategy to develop lightweight multifunctional microwave absorbers.

Engineering protein thermostability is a key aspect of rational protein design, aiming to broaden the applicability of enzymes and enhance their industrial utility. In this study, we introduce a strategy for identifying and reinforcing dynamic stability centers of local structure (DSCLSs) to improve protein thermostability. A DSCLS comprises key structural residues and their interactions, representing the structural basis of protein stability. Molecular dynamics, cross-correlation amino acid networks, and other analytical techniques were integrated into the method. This approach was initially inspired by thermostability engineering of exodiol dioxygenases (EDOs). The method was validated through mutational analyses of mesophilic EDO MT-2, CpKR (ketoreductase from Candida parapsilosis), and CaPETase (polyethylene terephthalate hydrolase from Cryptosporangium aurantiacum). Subsequently, we applied the approach to engineer thermostability in xp-EctC (ectoine synthase) from Rhodococcus and mesophilic EDO L1 from Bacillus, with the best-performing mutants showing T_m increases of ~15 °C. Notably, the catalytic efficiency of the optimal mesophilic EDO L1 mutant (T70Y) was 1.6-fold higher than that of the wild type at 60 °C, while the xp-EctC mutant (I2R) exhibited a 2.1-fold increase over the wild type. By characterizing and enhancing DSCLSs, this work presents a practical and generalizable strategy for thermostability engineering that also reduces mutational screening efforts, offering important potential for industrial applications.

The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-based antennas like Taiji and Laser Interferometer Space Antenna promises deep insights into strong-field gravity and black hole physics. However, the complex, highly degenerate, and nonconvex likelihood landscapes characteristic of EMRI parameter spaces pose severe challenges for conventional Markov chain Monte Carlo (MCMC) methods. Under realistic instrumental noise and broad priors, these methods demand impractical computational costs but are prone to becoming trapped in local maxima, leading to biased and unreliable parameter estimates. To address these challenges, we introduce flow-matching MCMC (FM-MCMC), a novel Bayesian framework that integrates continuous normalizing flows (CNFs) with parallel tempering MCMC (PTMCMC). By generating high-likelihood regions via CNFs and refining them through PTMCMC, FM-MCMC enables robust exploration of the nontrivial parameter spaces, achieves orders-of-magnitude improvement in computational efficiency, and, more importantly, ensures statistically unbiased inference. By enabling real-time, unbiased parameter inference, FM-MCMC could unlock the full scientific potential of EMRI observations, and would serve as a scalable pipeline for precision gravitational-wave astronomy.

Meniscal injuries are common in the knee joint. Minor meniscal injuries usually respond well to conservative treatment, while severe cases often require complete meniscal replacement. Meniscal injuries cause inflammatory responses that importantly hinder meniscal tissue regeneration. Despite ongoing advances in research, considerable breakthroughs in meniscal regeneration remain out of reach. This study introduces programmable macrophage mimics (PMMs), which enable sequential regulation from anti-inflammatory responses to meniscal fibrocartilage regeneration. PMMs were prepared by encapsulating the transforming growth factor-β3 and insulin-like growth factor-1 growth factors within mesoporous silica nanoparticles modified with branched polyethyleneimine via disulfide bonding. This design allows the initial adsorption of proinflammatory cytokines followed by the controlled release of growth factors that promote adipose-derived stem cell (ADSC) differentiation into fibrochondrocytes. The PMMs were integrated into meniscus-specific acellular matrix hydrogels (mGC), which provided suitable mechanical properties critical for effective regeneration. In rabbit osteoarthritis models, ADSC-loaded PMMs@mGC hydrogels showed marked fibrocartilage regeneration. Additionally, the team developed an advanced biofabrication approach that combines a 3-dimensionally printed polycaprolactone framework designed for total meniscus replacement. This research suggests that PMMs act as a bifunctional “core–shell” nano-delivery system, offering a promising therapeutic strategy for managing inflammatory meniscal conditions.

Aerosol viral transmission monitoring enables early, noninvasive detection of infectious disease spread by identifying airborne viral particles in shared environments. Integrating sampling, sensing, and data analysis, this perspective outlines an integrated framework for transforming epidemic responses from reactive testing to proactive surveillance. It synthesizes current technological advances and deployment experiences to identify key challenges in detection efficiency, data management, and societal trust. This marks a shift from clinical diagnosis to environmental surveillance, transforming epidemic responses from testing individuals to monitoring shared air. By enabling earlier interventions, these systems help reduce transmission, protect vulnerable groups, and limit disruptions to daily life and the economy.

The growing imperative to ensure food safety, preserve ecological integrity, and mitigate public health risks associated with pesticide residues has driven a critical demand for highly sensitive optical sensors. In this regard, optical biosensors, including fluorescence (FL), colorimetry (CL), surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and chemiluminescence strategies, have been developed for pesticide detection. This review aims to provide a comprehensive summary of both fundamental knowledge and advancements in the field of optical biosensors for pesticide detection. The advantages of these biosensors are highlighted, such as excellent sensitivity, high specificity, and on-site application. Subsequently, a detailed overview of the sensing mechanism of optical biosensors based on different molecular recognition elements [e.g., enzymes, antibodies, aptamers, molecularly imprinted polymers (MIPs), and supramolecular host–guest complexes] is provided. Finally, perspectives are offered on the current challenges and future directions of pesticide biosensors. This review is expected to serve as a fundamental guide for researchers in the field of optical biosensors for pesticide detection and to provide insights and avenues to enhance the performance of existing sensing mechanisms in applications across diverse fields.

Precise diagnosis and management of lower extremity dysfunction disorders hinge on continuous gait monitoring. Nevertheless, the existing wearable devices fall short as they grapple with insufficient sensing precision, inadequate energy endurance, and ineffective intelligent data analysis. Here, we report a fully integrated, biomimetic smart insole that incorporates 3 synergistic innovations to overcome these challenges. First, inspired by the hierarchical mechanosensory apparatus of mantis legs, we design dual-microstructure capacitive sensors with a detection limit of 0.10 Pa and a maximum detection range of 1.4 MPa. This sensor can distinguish pressures across a wide range from subtle to substantial and exhibits robust mechanical stability over 12,000 cycles, making it highly suitable for insole applications and outperforming current flexible pressure sensors. Second, we realize energy-autonomous operation by integrating nano-perovskite solar cells with high-capacity lithium–sulfur nanobatteries, achieving an average photocharging efficiency of 11.21% and energy storage efficiency of 72.15%. Third, embedded artificial intelligence algorithms interpret the spatiotemporal pressure data transmitted via a 16-channel wireless module. These models achieve 96.0% accuracy in detecting foot arch abnormalities and 97.6% accuracy in classifying 12 pathological gait patterns. Collectively, these 3 advances, including bioinspired high-resolution sensing, sustainable energy interfacing, and intelligent mechanodiagnosis, establish a closed-loop wearable platform validated in clinical studies. This system offers promising applications in early disease screening, personalized rehabilitation, and remote healthcare.