材料科学最新文献

Crystal engineering focuses upon the design, properties, and applications of crystals, whereas reticular chemistry involves linking molecular building blocks to create network structures. The intersection of these areas is evident in the number of systematic studies of structure/function relationships concerning porous coordination networks (PCNs) and covalent organic frameworks (COFs). PCNs and COFs are inherently modular in nature and therefore amenable to systematic fine-tuning of both pore size and chemistry in a manner that is infeasible for other classes of porous solid. This review highlights how this exquisite control over pore size and chemistry has enabled the development of a new generation of physisorbents that are effective in the context of industrially relevant hydrocarbon (HC) separations. The motivation behind such reticular sorbents is the need to replace today's energy-intensive HC separation methods with more sustainable alternatives. Physisorbents are attractive in this context as they can offer the high selectivity needed for trace removal of impurities along with relatively low energy of recycling. This review details how crystal engineering strategies offer precise control of pore size and chemistry to enable HC selectivity to reach hitherto unprecedented levels. Nevertheless, despite these property advances, challenges remain to be addressed before commercial adoption becomes feasible.

Thermoreversible adhesives allow for on-demand bonding and debonding on diverse surfaces through thermal stimulation. They have great potential in applications such as temporary fixation, which helps lower product costs and meet the requirements for environmental protection. However, the existing thermoreversible adhesive materials face challenges such as imprecise thermal switching, excessively high detachment temperatures, or poor bonding stability. To address these issues, here, we innovatively introduce temperature-responsive crystalline domains with an appropriate melting temperature (T_m) into the adhesive. These crystalline domains stabilize the adhesive network, thereby enabling it to maintain the high lap shear strength of the adhesive across a wide range. However, above T_m, close to the glass transition temperature (T_g) of the amorphous domains, it causes rapid softening and bonding failure. Therefore, the prepared thermoreversible adhesive simultaneously achieves high lap shear strength (5.38 MPa), reliable bonding stability over the normal operating temperature range (>75% of initial value, 30-70 °C), suitable thermal detachment temperature (∼80 °C, 0.66 MPa), and high environmental reliability. This work provides a feasible strategy for designing precision thermoreversible adhesives, which is conducive to promoting the application of thermoreversible adhesives in scenarios such as temporary fixation.

Suppressing formamidinium (FA) loss and perovskite phase degradation is very crucial for achieving highly efficient and long-term stable perovskite solar cells (PSCs). Herein, we designed and synthesized a novel multifunctional additive (ZL1) to stabilize α-FAPbI₃ perovskite phase through synergistic multisite interactions: i its F atoms form F···H-N hydrogen bonds with FA⁺, (ii) its phenyl rings participate in cation-π interactions with FA⁺, (iii) the C=O and S groups coordinate Pb²⁺ through Lewis acid-base interactions, and (iv) the NH groups engage I^- anions through N-H···I hydrogen bonding. Consequently, ZL1 molecule can effectively suppress FA loss and optimizes perovskite crystallization kinetics, yielding high-quality and stable α-FAPbI₃ perovskite films with enlarged grain sizes and reduced defect density. Meanwhile, ZL1 treatment promotes exciton dissociation, facilitates hole extraction from the perovskite layer into the hole transport layer, and reduces charge carrier recombination in device. The ZL1-modified device achieves a power conversion efficiency of 26.13%, significantly outperforming the control device (24.20%). A similar improvement is observed in wide-bandgap PSCs, with efficiency increasing from 18.44% to 20.53% after ZL1 treatment. Notably, the unencapsulated ZL1-based devices exhibit exceptional operational stability under both illumination and thermal conditions.

Interindividual variability in metabolic responses to diets complicates the relationship between nutrition and metabolic health, which highlights the existence of metabolic heterogeneity across populations. This variability challenges the conventional "one-size-fits-all" approach to dietary recommendations and underscores the need for precision nutrition. In the current era, characterized by breakthroughs in sophisticated data collection technologies, the explosion of big data, and progress in artificial intelligence, the implementation of precision nutrition is becoming increasingly feasible. This review aims to summarize potential sources of metabolic heterogeneity from the angle of the host genome, gut microbiome, and brain connectome to explore the implications of their interactions with diet. Furthermore, we discuss the application of artificial intelligence in leveraging multimodal data for predicting individualized dietary responses. Aggregating data on host genetics, gut microbes, and brain activity profiling offers profound insights into the personalized response to diets. We also highlight the development of individual-specific predictive models that combine n-of-1 study designs with advanced wearable technologies and machine learning algorithms, thereby placing the individual at the center of nutritional decision-making. Finally, this review summarizes current challenges in the field and outlines potential directions for advancing precision nutrition.

There are two general approaches in guiding intelligent vision system development: one emphasizes ultra-flexibility (reconfigurability) for adapting to various scenarios, and the other emphasizes ultrahigh power efficiency tailored to specific applications. The pinnacle design is geared toward the biological vision system with concurrent high levels of on-demand intelligence, efficiency, and flexibility. However, current state-of-the-art intelligent vision systems are far behind, relying on heterogeneously integrated and limited-function single devices, alongside rigid sensing/computing architecture, thus preventing flexibility for low area and power efficiency toward dynamic and unpredictable scenarios. This work bridges the neuromorphic gap with an on-demand ultra-reconfigurable vision system, demonstrating true reconfigurability across device, cell, array and system levels. This is enabled by a multi-paradigm device array capable of seamless switching between spiking, non-spiking, neuromorphic imaging (NI), and artificial intelligence (AI) computing modes, as well as a reconfigurable circuit and architecture design. The system is capable of on-demand allocating resources between NI and AI functionalities for high-quality smart imaging and high-accuracy recognition tasks, and transitioning between spiking and non-spiking modes for frameless dynamic and frame-based static scenarios. Superior power efficiencies of up to 52.6 TOPS/W for NI-centric computing and 75.5 TOPS/W for NI/AI hybrid computing are achieved, which are up to two orders of magnitude larger than the state-of-the-art intelligent vision system.