Science China Materials最新文献

Quasi-two dimensional (2D) perovskites have emerged as a promising class of materials due to their remarkable photoluminescence efficiency, which stems from their exceptionally high exciton binding energies. The spatial confinement of excitons within smaller grain sizes could enhance the formation of biexcitons leading to higher radiative recombination efficiency. However, the synthesis of high-quality quasi-2D perovskite thin films with controllable grain sizes remains a challenging task. In this study, we present a facile method for achieving quasi-2D perovskite thin films with controllable grain sizes ranging from 500 to 900 nm. This is accomplished by intermediate phase engineering during the film fabrication process. Our results demonstrate that quasi-2D perovskite films with smaller grain sizes exhibit more efficient bound exciton generation and a reduced stimulated emission threshold down to 15.89 µJ cm⁻². Furthermore, femtosecond transient absorption measurements reveal that the decay time of bound excitons is shorter in quasi-2D perovskites with smaller grain sizes compared to that of those with larger grains at the same pump density, which is 230.5 ps. This observation suggests a more efficient exciton recombination process in the smaller grain size regime. Our findings would offer a promising approach for the development of efficient bound exciton lasers.

The development of highly efficient and low-cost photocatalysts for degradation of organic pollutants become an effective approach for environmental remediation. However, the practical application of traditional powder catalyst in photocatalytic technology is limited due to its low recycling capacity, agglomeration and secondary pollution risk. Herein, a floating Fe-doped TiO₂ and hydrogel (FTH) composite was synthesized for the photodegradation of Rhodamine B via a facile impregnation method. The photodegradation results show that the FTH composite exhibits a higher photocatalytic efficiency with degradation percentage (95.6%) compared with pure TiO₂ (41.2%). The enhanced photocatalytic performance is attributed to its excellent flotation performance, providing a large number of active sites for pollutant degradation, contact with O₂ and photons at the air/water interface. Remarkably, the adsorbed Rhodamine B in FTH can still be removed by exposing to light in the air alone, demonstrating strong recovery ability of the FIH composite catalyst. The floatable hydrogel nanocomposites offer a promising solution for scalable solar-drive degradation of water pollutants, paving the way for sustainable water treatment technologies.

Sulfur and nitrogen dual-doped graphdiyne (NSGD) has been found to be a promising catalyst for oxygen reduction reaction (ORR) through a combination of density functional theory (DFT) calculation and the application of oxygen evolution reaction (OER) experiments. The DFT analysis suggests that adsorption characteristics are significantly altered by resulting nitrogen and sulfur doping, which in turn affect the ORR activity. In particular, the NSGD-800 catalyst exhibits an increased ORR half-wave potential of 0.754 V, with enhanced stability due to the synergy effect of N and S. Meanwhile, thanks to the unique acetylene-rich structure of graphdiyne to anchor metal oxides with strong d-π interactions, the activity and stability of com-RuO₂ for OER were significantly enhanced by mixing with NSGD-800. The zinc-air battery (ZAB) with NSGD shows a much higher peak power density (87.3 mW cm⁻²) and longer charge-discharge cycle stability compared with the ZAB with Pt/C, making it an excellent candidate air electrode for ZAB and other energy storage and conversion devices.

Dental hard tissues, primarily enamel and dentin, serving essential functions such as cutting, chewing, speaking, and maintaining facial aesthetics, mainly composed well-aligned hydroxyapatite (HAp) nanocrystals interlaced with a protein matrix. These tissues exhibit remarkable mechanical and aesthetic behaviors. However, once damaged, its ability to self-repair is extremely limited, often accompanied by dentin hypersensitivity (DH). Currently, although dental restorations using synthetic materials and remineralization techniques have made clinical progress, these methods still have limitations that affect their widespread use in clinical applications. Therefore, understanding the formation mechanisms of dental hard tissues and developing high-performance restorative technologies that can mimic natural teeth and meet clinical needs are crucial. This review focuses on the current strategies and research advancements in enamel regeneration and dentin desensitization, and challenges of clinical translation. We emphasize that scientific research should start with clinical needs, and these studies, through translation, ultimately serve the clinic to form a mutually reinforcing virtuous cycle. This review aims to provide a new perspective on the prevention and treatment of dental hard tissues, promote innovation in restorative materials and techniques, and bring better clinical translation products and services to patients.

Carbon molecular sieve membranes (CMSMs) are a class of porous membranes inherited with excellent thermal stability, high tolerance and superior mechanical strength. Owing to their nanoporous structures, CMSMs usually hold significant potential for gas separation applications. Specifically, hyper-crosslinked ionic polymer (HIP) membranes possess a highly crosslinked nitrogen-rich framework, high thermal stability together with exceptional mechanical strength, making them excellent precursors for the CMSMs fabrication. Upon pyrolysis of HIP membranes, the resulting CMSMs featured with nitrogen functional sites exhibit strong interactions with CO₂, which significantly reduces the CO₂ permeability while other gas molecules continue to flow through the nanoporous membrane. The resultant CMSMs exhibited excellent H₂/CO₂ selectivity with values of 10.75 and 7.09, together with ultra-high H₂ permeability of 3052 and 9181 barrer, respectively, surpassing the Robeson upper bound. The preparation route towards CMSMs with high nitrogen content from HIP can significantly enrich the rational design and synthesis strategies of high-performance gas separation CMSM materials.

Zero-dimensional perovskite materials, characterized by broadband emission caused by self-trapped excitons, are promising materials for stimuli-responsive and photo-writeable encryption. However, existing research is focused on the effects of structural phase transitions on photophysical properties, and lacks in-depth understanding of the mechanisms of self-trapped excitons emission. Here, we demonstrate that the dehydration reaction in zero-dimensional antimony halide clusters significantly enhances the self-trapped excitons emission without inducing structural phase transition, resulting in a substantial increase in photoluminescence (PL) quantum yield from 3.5% to 91.4%. In-situ X-ray diffraction and PL techniques were employed to shed light on the relationship between the crystal structure and radiative recombination, demonstrating the introduction of rich lattice distortion during the dehydration process. Temperature-dependent PL spectra and transient absorption spectra suggest that the lattice distortion causes the moderate electron-phonon coupling strength and high exciton binding energy, facilitating self-trapped excitons to relax from the non-radiative recombination singlet state to the radiative recombination triplet state, corresponding to the enhanced emission intensity. As a proof of concept, several switchable PL applications have been established in scenarios such as anti-counterfeiting, rewritable luminescent paper, and humidity sensing. This finding elucidates the emission mechanism of self-trapped excitons and provides a novel avenue for designing switchable luminescent materials.

Signal processing has entered the era of big data, and improving processing efficiency becomes crucial. Traditional computing architectures face computational efficiency limitations due to the separation of storage and computation. Array circuits based on multi-conductor devices enable full hardware convolutional neural networks (CNNs), which hold great potential to improve computational efficiency. However, when processing large-scale convolutional computations, there is still a significant amount of device redundancy, resulting in low computational power consumption and high computational costs. Here, we innovatively propose a memristor-based in-situ convolutional strategy, which uses the dynamic changes in the conductive wire, doping area, and polarization area of memristors as the process of convolutional operations, and uses the time required for conductance switching of a single device as the computation result, embodying convolutional computation through the unique spiked digital signal of the memristor. Our strategy reasonably encodes complex analog signals into simple digital signals through a memristor, completing the convolutional computation at the device level, which is essential for complex signal processing and computational efficiency improvement. Based on the implementation of device-level convolutional computing, we have achieved feature recognition and noise filtering for braille signals. We believe that our successful implementation of convolutional computing at the device level will promote the construction of complex CNNs with large-scale convolutional computing capabilities, bringing innovation and development to the field of neuromorphic computing.

Finding crystals with high birefringence (Δn), especially in deep-ultraviolet (DUV) regions, is important for developing polarization devices such as optical fiber sensors. Such materials are usually discovered using experimental techniques, which are costly and inefficient for a large-scale screening. Herein, we collected a database of crystal structures and their optical properties and trained atomistic line graph neural network to predict their Δn. To estimate the level of confidence of the trained model on new data, D-optimality criterion was implemented. Using trained graph neural network, we searched for novel materials with high Δn in the Materials Project database and discovered two new DUV birefringent candidates: NaYCO₃F₂ and SClO₂F, with high Δn values of 0.202 and 0.101 at 1064 nm, respectively. Further analysis reveals that strongly anisotropic units with various anions and π-conjugated planar groups are beneficial for high Δn.