Existing hydrogels pose challenges for the applications in transitional medication within narrow cavities due to the inherent conflict between fluidity and shape retention. Herein, inspired by mucus, a bottlebrush polymer hydrogel (denoted as PH-PG-T hydrogel) with excellent injectability, stable antibacterial property, and easy removability is developed by grafting poly(ethylene glycol) methyl ether methacrylate side-chains from polyhydroxyethyl methacrylate backbones and incorporating triple antibiotics. As a transitional disinfectant in tooth root canal treatment, PH-PG-T hydrogel fulfills requirements for an ideal disinfectant thorough treatment process. The polymer side-chains of PH-PG-T hydrogel disentangle during injection to facilitate canal filling and rapidly re-entangle after injection to enable in situ gelation and sustained antibiotics release. After medication, PH-PG-T hydrogel quickly dissolves in water during irrigation, facilitating its complete removal from the root canal. These findings highlight a promising avenue for the novel application of bottlebrush polymers as a high-performance disinfectant to control persistent root canal infections.

Terahertz (THz) holography demonstrates immense potential in biomedical detection, virtual reality, and information encryption security. Janus metasurfaces, which can independently control the amplitude and phase of THz waves from opposite directions, offer a promising platform for directional multiplexing in THz holography. However, most existing designs rely on conventional forward design methods, limiting the system to just two degrees of freedom (DOFs) and significantly restricting both channel count and holography imaging quality. In this study, we introduce a bidirectional deep neural network (Bi-DNN) inverse design method, combined with thermally responsive phase-change material vanadium dioxide (VO₂), for the development of Janus metasurfaces. The proposed design enables independent control of incident direction, frequency, and temperature as three distinct DOFs. The meta-atoms selected through the Bi-DNN exhibit a transmittance exceeding 90%, effectively generating low-crosstalk, eight-channel THz holographic images with an imaging efficiency of 78%. Furthermore, by combining the metasurface with the inherent superposition properties of Chinese characters to achieve high-intensity and high-density holographic encryption in the THz band. This design offers a novel strategy and technical foundation for applications in high-capacity data storage, holographic encryption, and secure wireless communication.

Covalent organic frameworks (COFs) have long been regarded as promising porous materials for applications ranging from gas storage to catalysis. However, their practical deployment has been hindered by tedious synthesis protocols, toxic solvents, and poor processability. In a recent study published in Nature Chemical Engineering, Jin et al. report a solid-phase hot-pressing (HP) method that enables rapid synthesis of highly crystalline COF platelets within minutes. This solvent-free, scalable approach not only preserves crystallinity and porosity but also facilitates the fabrication of mechanically robust COF monoliths, paving the way for their real-world implementation.

Soft magnetoresponsive materials (SMRMs) with programmable magnetization profiles can exhibit a wide range of motion postures under dynamically controllable magnetic fields. Although the complex magnetization distribution provides a vast design space, the intricate nonlinear deformation and magneto-mechanical coupling mechanisms make the design process highly challenging. The prevailing design methodologies are limited by their inefficiency and a lack of precision, which constrain the application potential of SMRMs. Here, we propose a method that combines machine learning and evolutionary algorithms to achieve forward prediction of deformations of SMRMs and inverse design of the magnetization profiles required for target deformations. A machine learning model based on a fully connected neural network (FCNN) is trained to predict deformations under external magnetic fields from six directions with high accuracy, within 0.3 ms. Based on this, an evolutionary algorithm is introduced to search for the inverse design solutions that achieve the target deformations from a pool of millions of candidates within minutes. By incorporating two symmetries into the physical model, the required amount of training data is reduced by 75%, enabling the design of controllable dynamic fluctuations and multi-modal arrays. An image recognition method has been developed to transform natural curves into deformation targets, demonstrating the static reconstruction of leaf contours and the dynamic replication of four distinct fish swimming modes. This approach provides an efficient design tool and expands the application scope of the material.

Electrochemical nitrate reduction reaction (NO₃RR) refers to the route of converting nitrate (NO₃⁻) to ammonia (NH₃). However, NH₃ dissolved in the post-reaction solution is limited for direct reuse toward downstream purposes, thereby undermining the application potentials and economic viability of NO₃RR. Instantaneous NH₃ recovery after NO₃⁻ conversion reduces the entropy of the system, thermodynamically favoring the forward reaction. NH₃ separation is necessary for both resource recovery and environmental safety considering its toxicity. Coupling NO₃RR with NH₃ recovery presents a promising solution for decentralized nitrogen management, particularly in areas with limited access to the power grid and commercial fertilizers. In this perspective, principles and engineering design of NH₃ recovery are introduced, with a highlight on heat as a suitable driving force. Looking forward, enhancing the reactant and product selectivity by smart materials design will facilitate the treatment of different nitrogen sources, and incorporating current energy infrastructures leads a way of enhancing access to electricity, sanitation and fertilizers.

The interdisciplinary integration of medicine and engineering serves as a central technological driver for advancing the regulation of stem cell differentiation toward specific lineages in tissue repair. In contrast to biological and chemical signals, physical signals (light, acoustic, magnetic, mechanical, and electrical) offer superior tunability and spatiotemporal precision. However, macroscopic external physical fields alone are generally ineffective for modulating stem cell fate. Nanostructure-mediated physical signals allow the generation of localized, quantifiable, and dynamically controllable cues that can be specifically recognized by cell surface receptors, ultimately regulating stem cell differentiation through nanoscale extracellular matrix components such as fibrin and polysaccharide fibers. The core mechanisms governing this process include field-induced material polarization, nanostructure-mediated signal amplification, energy conversion effects, and synergistic activation of downstream gene expression. A novel concept-termed “regulation of stem cell fate via nanostructure-mediated physical signals” has emerged as a critical frontier across biomaterials, cell biology, and tissue engineering. Building on recent advances in external-field-responsive functional nanomaterials, this perspective not only synthesizes the mechanisms and effects of diverse nanostructure-mediated physical signals but also inspires further interdisciplinary collaboration in engineering-medicine applications.

Temperature (T) monitoring is essential across various domains, including biomedical applications, where respiratory rate (RR) can be estimated from the temperature difference between inhaled and exhaled air. Additive manufacturing, particularly fused deposition modeling (FDM), remains underexplored for this purpose despite the potential offered by conductive thermoplastic composites. Integrating conductive fillers into printable polymers provides an effective strategy to develop novel sensors. This study presents the fabrication and characterization of resistive sensors produced by FDM using thermoplastic polyurethane (TPU) filled with carbon black (CB). Three bio-inspired geometries—spider (SP), honeycomb (HC), and flap (FL)—are fabricated with three thicknesses (1, 2, and 3 layers). The electrical responses to temperature and relative humidity (RH) are analyzed to evaluate the influence of design on metrological performance. All sensors exhibit a synergistic behavior, with resistance increasing as T and RH rise. A parabolic response to T variations is observed, with resistance changes up to 125%. T sensitivity increases with thickness, with the HC geometry showing the highest values. Response times range from 15 to 23 s, with 8% hysteresis. In the 30%–70% RH range, sensors provide linear responses. Integration into a commercial face mask enables reliable RR detection during bradypnea, eupnea, and tachypnea.

Metal-organic framework (MOF)-based nanomaterials have emerged as a transformative platform for biomedical applications due to their high surface area, tunable porosity, and modular composition. This review highlights recent advances in the design and synthesis of MOF-based nanomaterials for potential applications in cancer therapy, antimicrobial treatments, and anti-inflammatory therapy. We categorize these nanomaterials into three key classes: (1) intrinsic theranostic MOFs, which utilize their metal nodes or organic ligands for inherent therapeutic or diagnostic functions; (2) carrier-type MOFs, designed for high-capacity drug delivery with stimuli-responsive release; and (3) hybrid theranostic composites, formed by integrating MOFs with functional components for multimodal imaging and synergistic therapies. Despite promising demonstrations, the clinical translation of MOF-based nanomaterials still faces challenges in biosafety, scalability, and targeting efficiency. Future research must prioritize biodegradable designs, green synthesis, and multifunctional platforms. Interdisciplinary collaboration is essential to bridge the gap between innovation and clinical implementation, unlocking the full potential of MOF-based nanomaterials in precision medicine.

The conversion of carbon dioxide (CO₂) into value-added chemicals and renewable fuels is a promising approach to mitigate climate change and promote the development of sustainable energy systems. However, despite the broad range of products, including CO, formic acid and multi-carbon hydrocarbons, the large-scale implementation of CO₂ conversion technologies is still hindered by low catalytic efficiency and high energy consumption. This review introduces recent advances in catalytic materials design, emphasizing the structure–property relationships that govern the performance of highly efficient catalysts across various CO₂ conversion processes, including photocatalysis, electrocatalysis, CO₂ hydrogenation, photothermal conversion, non-thermal plasma techniques, and biological methodologies. By examining the synergies among catalyst architectures, key intermediates, catalytic mechanisms and reactor designs, this review explores the potential for tailored CO₂ conversion processes with optimized reaction pathways to achieve specific catalytic products, and also provides a roadmap for the development of efficient, scalable CO₂ conversion technologies to facilitate the transition to a circular carbon economy.