Interdisciplinary collaboration is essential for developing advanced smart materials, yet coordinating diverse expertise remains a significant bottleneck. In a recent study published in Nature, a Virtual Lab where autonomous AI agents collaborate to design functional nanobodies with minimal human intervention was introduced. This work exemplifies a transformative paradigm in AI for Science, demonstrating a versatile multi-agent architecture that is agnostic to specific domains. Here, we discuss how this framework can be adapted to materials science, offering a blueprint for autonomous discovery in next-generation smart materials.
Mesoporous zinc-based nanomaterials exhibit considerable potential across a spectrum of applications, yet their controlled synthesis remains challenging due to the rapid hydrolysis and condensation kinetics of Zn2+ ions. Herein, we report a facile surfactant-directed synthesis strategy for the controllable synthesis of monodisperse mesoporous Zn(OH)2 nanospheres and they can be readily converted into semiconducting mesoporous ZnO (denoted mZnO) nanospheres via calcination. In this synthesis, by employing sodium salicylate (NaSal) as a multifunctional modulator, we regulate micelle organization and zinc precursor diffusion, overcoming the inherent kinetic limitations of Zn2+. The resulting crystalline mZnO can serve as an ideal host for loading ultrasmall Pt nanoclusters to produce Pt/mZnO composite nanospheres with abundant metal–metal oxide interfaces. Using Pt/mZnO nanospheres as the sensitive materials, gas sensors were fabricated on micro-electromechanical systems (MEMS) devices, which display an exceptional performance in the detection of low-concentration acetone vapor. The gas sensor exhibited high response (4.9 toward 1 ppm acetone), low detection limit (170 ppb), excellent selectivity, and good long-term stability. In situ spectroscopy characterization results reveal that, during the gas-sensing process, the reaction pathway involves catalytic oxidation of acetone over Pt/mZnO nanospheres via intermediate carboxylates. This work offers a generalized approach for designing functional mesoporous metal oxides with enhanced interfacial activity for advanced sensing and catalytic applications.
The electrocatalytic nitrate reduction reaction (NO3RR) provides a sustainable route for nitrogen pollution remediation and ammonia production under ambient conditions. However, the process involves sluggish multi-electron/proton transfer and suffers from competing hydrogen evolution (HER) which severely limits efficiency and selectivity. Herein, we construct a Pdx-CeO2/C composite catalyst, in which strong interactions between Pd and CeO2 induce interfacial electronic redistribution, thereby effectively modulating the Pd electronic structure. This interfacial coupling enhances water dissociation, accelerates the generation of reactive *H, and promotes its spillover from Pd to CeO2. In-situ attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) confirms that hydrogen spillovers facilitate stepwise hydrogenation of intermediates while suppressing HER. Density functional theory calculations further reveal that Pd-Ce interactions inhibit HER and enable N─O side-on adsorption, shifting the potential-determining step and lowering the reaction barrier, thus boosting electrocatalytic activity. As a result, the optimized 5 wt.% Pd-CeO2/C delivers a Faradaic efficiency of 98.0% for ammonia at −0.4 V (vs. RHE), and achieves an ammonia yield rate of 4.17 mmol/(mgPd∙h) at −0.6 V. This work offers new insights into the application of interfacial engineering for efficient electrocatalytic ammonia synthesis.
The slow kinetics and high overpotentials of the oxygen evolution reaction (OER) remains a major challenge for electrocatalytic energy conversion. Spin manipulation has emerged as a promising strategy for enhancing OER performance by modulating the adsorption of intermediates, thereby reducing the reaction energy barrier and accelerating OER kinetics. Herein, we demonstrate that chiral nanostructured Pd films (CNPFs) can enhance OER performance. CNPFs were electrodeposited on a nickel foam substrate using methionine as a symmetry-breaking agent. The optimal CNPFs catalyst achieves a current density of 50 mA∙cm−2 at 1.57 V versus RHE, representing a 180 mV reduction in overpotential compared to achiral Pd films. By varying the electrodeposition time, a series of CNPFs with tunable spin polarization were synthesized. The enhanced OER performance in CNPFs exhibits a positive correlation with spin polarization, primarily due to the spin-polarized electron transfer promoting the formation of triplet oxygen. This work elucidates the fundamental relationship between spin polarization and OER performance, highlighting the critical role of chiral structure in the design of efficient spin-polarized electrocatalysts.
Stretchable organic light-emitting diodes (OLEDs) represent a transformative platform for human–machine interfaces and wearable technologies but have long been constrained by inefficient electron injection. Wang Sihong and de Pablo Juan J.'s team addressed this challenge through the rational design of stretchable electron-transport polymers and liquid-metal-induced embrittled aluminum cathodes, achieving synergistic and highly efficient electron injection. This strategy overcomes the traditional trade-off between stretchability and electronic performance, achieving the first fully stretchable OLED with performance comparable to that of rigid OLED devices. Beyond its immediate applications in high-performance stretchable displays and electronic skins, this work establishes a generalizable design paradigm based on molecular-scale “function–mechanics decoupling” and interface engineering strategies that impart flexibility to conventional materials. These innovations provide a robust framework for advancing next-generation skin-like optoelectronic systems.
Solar-driven interfacial evaporation has evolved into a sustainable technology with the potential to alleviate the shortage of freshwater resources. Despite remarkable progress in developing photothermal materials, substrates and hybrid configurations, these approaches aimed at improving the evaporation performance appear to have hit a plateau. This limitation can be attributed to the neglect of thermal convection, a critical factor that significantly impacts evaporator performance. Herein, a wood-based evaporator featuring a dual convection structure was developed by creating a central cavity in a wood block and integrating MXene photothermal materials. It is worth highlighting that the evaporator delivers an exceptional evaporation rate of 2.16 kg/(m2·h) and a photothermal conversion efficiency of 118% without necessitating the use of special materials or intricate architectures. Simulations revealed that the purposely designed dual-convection structure effectively regulates the heat distribution and reduces the humidity above the evaporation surface, which proved to greatly enhance both the evaporation rate and the overall photothermal conversion efficiency. Furthermore, the device demonstrates good resistance to saline conditions and long-term operational stability, even under extreme pH conditions. This work provides novel insights into the design of high-efficiency, structurally simplified solar interfacial evaporators and broadens the potential applications of wood-based materials in solar-driven evaporation systems.
Magnetic field regulation has become a unique non-contact strategy to enhance the performance of a variety of electrochemical reactions, such as hydrogen evolution reaction (HER), oxygen evolution reaction (OER), carbon dioxide reduction reaction (CO2RR) and nitrogen reduction reaction (NRR). Magnetic field regulation provides a unique path for efficiency improvement by selectively manipulating the reaction kinetics, changing the spin state, and optimizing the mass transfer process. This article comprehensively addresses the influence of the magnetic field on the electrocatalytic process at a fundamental level, emphasizing the operational principles of core mechanisms, including spin polarization, electronic structure modulation, and improved mass transfer. Additionally, it systematically differentiates the response laws of various catalyst types, including ferromagnetic, paramagnetic, and antimagnetic, to magnetic stimulation. It establishes the correlation mechanism between their distinctive characteristics and catalytic performance, integrating experimental observations with theoretical simulations to emphasize recent advancements in the application of magnetic field regulation within specific reaction systems. In conclusion, considering the prevailing core challenges and developmental prospects in magnetoelectric catalysis, advancing in situ characterization technology, establishing a theoretical framework, and formulating a cooperative strategy involving magnetic fields and other external stimuli are crucial for the advancement of sustainable and efficient energy conversion systems.
Effective monitoring of food spoilage is crucial for global food security, as nearly one-third of the food produced worldwide each year is wasted and 29% of the population faces nutritional risk. However, existing gas sensing technologies suffer from high material costs, limited sensitivity, and inefficient data transmission. Here, we report a low-cost electrochemical H2S sensor based on a metal-organic framework-derived ZrO2@C-900 composite. This active material costs only US$0.09 g−1, has high sensitivity, and retains 97% of its initial performance after 120 days of continuous operation. This device integrates Bluetooth transmission functionality to monitor H2S release in real time as a biomarker of food spoilage. This sensor operates stably under high humidity and low temperature conditions, making it suitable for use in intelligent cold chain systems.
Despite advances in computational catalysis, the complexity of theoretical calculations and specialised expertise requirements limit the broader adoption of catalyst design tools. This work introduces CatPath-GPT, a mixture-of-experts framework that democratizes computational catalyst design by integrating three AI specialists: product prediction (77.2% accuracy), computational planning, and automated code generation through a unified BERT-based router. Experimental validation through two case studies demonstrates practical impact: systematic screening of CuxZn1−x catalysts identifies optimal compositions for selective CO2RR (Cu75Zn25 for ethanol), while high-throughput metal oxide screening reproduces Nørskov's classical scaling relationships and identifies high-activity materials. Benchmarking against GPT-4 and Mistral-7B demonstrates superior performance across catalyst-related tasks, particularly in modeling complex surface reactions. The open-source framework enables researchers without computational expertise to perform advanced catalyst design, potentially transforming how catalytic materials are discovered and optimized.