ACS Materials Letters最新文献

Electroreduction of nitrate to ammonia (NO₃RR) is a potential route for ambient ammonia synthesis. However, the complex eight-electron transfer process makes it a great challenge to achieve high-efficiency ammonia production. Herein, a kind of Cu-based oxide with a design of high-entropy doping is presented as an efficient NO₃RR catalyst. Such a strategy is able to not only accelerate the reaction kinetics but also induce a self-healing feature toward the catalyst. During NO₃RR, its phase is in situ reconstructed from CuO to Cu/Cu₂O, which quickly restores to CuO reversibly after electrolysis. As expected, ampere-level ammonia production was achieved on the proof-of-concept catalyst, with a maximized NH₃ yield rate of 105.66 mg h^–1 cm^–2 and Faradaic efficiency of 96.7%, along with excellent long-term stability at a NH₃ partial current density over 1.2 A cm^–2. We believe that the high-entropy doping strategy offers an efficient approach for the future design of NO₃RR catalysts.

Type-II two-dimensional (2D) heterostructures are promising for photocatalytic water splitting but face exploration challenges due to high experimental/computational costs. Here, we propose an efficient data-driven approach for the rapid discovery of type-II van der Waals heterostructures (vdWHs) without the need for preoptimization of structures or precise stacking information. To meet this end, a specially designed matrix descriptor is developed to capture the important interlayer interactions. Coupled with a one-dimensional convolutional neural network, this descriptor can well describe weak interlayer interactions in heterostructures, allowing direct prediction of bandgap and band edge positions of arbitrary 2D heterostructures. 800 potential candidates are successfully screened out of nearly 10⁵ heterostructures for type-II vdWHs, and further comprehensive band structure and optical absorption spectra calculations reveal the potential of WS₂/Rh₂Br₆ and Al₂S₂/PtS₂ as water splitting photocatalysts. This work provides a data-driven approach to energy materials discovery and offers a cost-effective alternative to traditional methods.

In-situ characterization techniques, although complex, can provide a wealth of insight into material chemistry and evolution dynamics. Grasping the fundamental kinetics of material synthesis is essential to enhance and streamline these processes and facilitate easier scaleup. Metal–organic frameworks (MOFs), a class of porous crystalline materials discovered three decades ago, have been developed and implemented in various applications at the laboratory scale. However, only a few studies have explored the fundamental mechanisms of their formation that determine their physical structure and chemical properties. Independent experimental and theoretical investigations focusing on chemical kinetics have provided some understanding of the mechanisms governing MOF formation. However, more effort is needed to fully control their formation pathways and properties to enhance stability, optimize performance, and design strategies for scalable production. This Perspective highlights current techniques for studying MOF kinetics, discusses their limitations, and proposes multimodal experimental and theoretical protocols, emphasizing how improved data acquisition and multiscale approaches can advance scalable applications.

The current lubricating artificial tear ingredient for dry eye disease (DED) therapy, i.e., sodium hyaluronate (SH), is ineffective because it is difficult to stay on the ocular surface long term. To address this problem, we developed an injectable in situ lubricative copolymer that can firmly adhere to the ocular surface for long-lasting in situ lubrication. Lubrication was achieved by the zwitterionic monomer [2-((methacryloyl)oxy)ethyl]phosphorylcholine (MPC) with hydration lubrication property. Meanwhile, in situ adhesion performance was achieved by the reactive ester monomer N-hydroxysuccinimidyl acrylate (AA-NHS), which is capable of amide bonding reactions to the tissue surface. The optimum feeding ratio of MPC versus AA-NHS for free radical polymerization was confirmed by friction tests, and this group was named MPC-co-AA-NHS (MAN). We established a BALB/c mice DED model to verify its biofunctionality in vivo. The results showed that MAN is a promising in situ lubrication material that effectively relieves DED.

Elucidating the atomic arrangement of dopant states at high doping concentrations is crucial for understanding structure–property relationships in materials. On the atomic scale, closely connected interfaces, particularly coherent interfaces, can effectively suppress interface-induced trapping processes. Although not yet experimentally verified, heavy doping holds promise for generating heterojunctions within host materials. This study combines spherical aberration-corrected electron microscopy and first-principles calculations to reveal that, at low doping concentrations (1%), Bi primarily occupies W sites, resulting in substitutional doping. However, at high doping concentrations (>10%), we have identified the formation of a β-Bi₂O₃ phase within the WO₃ host. The formation of these heterojunctions can effectively facilitate electron transfer due to favorable band alignment and potential energy differences between Bi₂O₃ and WO₃. The findings of this study are crucial for rethinking the atomic structures of dopant states at high doping concentrations and their potential application in the development of heterojunctions.

If rubber-toughened plastics can effectively stabilize organic triplet exciton radiation, then a new class of room temperature phosphorescent (RTP) polymers can be developed. In the current work, the outstanding triplet generating and radiating coronene (Cor) is doped into HIPS, ABS, and MBS, and the thermoplastic processed sheets emit bright and ultralong RTP with lifetimes of 3.200–3.700 s after being excited by 365 nm light. Under the impact of mechanical forces, the stress whitening region no longer emits RTP afterglow, whereas heat healing can recover afterglow, implying the potential application in detecting material damage and repair and indicating that polymer cohesion and density remarkably affect triplet thermal and oxygen stability. We further reveal that rubber-plastic secondary “core-shell” structures can synergistically inhibit triplet thermal deactivation and oxygen quenching, and we also confirm that these Cor/polymers show visible light excitable RTP properties. This work represents a breakthrough advancement in RTP materials and concepts.

Efforts have been relentlessly pursued to develop high-quality and uniform graphitic carbon nitride (g-C₃N₄) films. In this work, a thermal chemical vapor deposition (CVD) method was developed for the synthesis of homogeneous g-C₃N₄ films on various substrates using melamine powder as a precursor. The film produced on a silicon wafer is ultrathin, down to 10 nm, with good crystallinity. By changing the precursor and extending the polymerization time, it is also possible to deposit a homogeneous free-standing film on top of anodic aluminum oxide (AAO). The film can be peeled off after the sample is immersed in distilled water for 10 min. Notably, upon characterization, the chemical features and composition were found to closely resemble those of the ideal g-C₃N₄. This research offers a method for growing g-C₃N₄ films, which is crucial for broadening their utility beyond catalysis and potentially paving the way for future applications in optoelectronic devices and beyond.

Electronic skin (E-skin), mimicking the multisensory response of human skin, is increasingly utilized in diverse applications such as health monitoring and sensory skins. Here, inspired by the diverse adhesion and responsive structural color phenomena in biological interfaces, we present a cellulose-based, skin-adherent photonic E-skin (CSPE) for dual-mode visual and electrical strain sensing. The CSPE combines a cellulose nanocrystal (CNC)-based conductive, tough photonic hydrogel with a bridging chitosan interlayer that binds the photonic gel and skin together. Endowed with the ionically crosslinked double-network hydrogel as the mechanochromic dissipative matrix and pH-responsive chitosan for topological interpenetration, the CSPE exhibits anisotropic adhesion, ensuring good skin adhesion and preventing unwanted attachments on the opposite surface. Additionally, the photonic hydrogel with vivid structural colors provides quantitative feedback of mechanical stimulation via color mapping and electromechanical changes, enabling precise tracking of human movements. This proposed skin-adherent photonic skin can widen the practical value of bionic electronic skins.

Inspired by biological sensors that characteristically adapt to varying stimulus ranges, efficiently detecting stimulus changes sooner than the absolute stimulus values, we propose a mechanosensing concept in which the resolution can be adapted by magnetic field (H) gating to detect small pressure-changes under a wide range of compressive stimuli. This is realized with resistive sensing by pillared H-driven assemblies of soft ferromagnetic electrically conducting particles between planar electrodes under a voltage bias. By modulation of H, the pillars respond with mechanically adaptable sensitivity. Higher H enhances current resolution, while it increases scatter among repeating measurements due to increased magnetic structural jamming between colloids in their assembly. To manage the trade-off between electrical resolution and scatter, machine learning is introduced for searching optimum H gatings, thus facilitating efficient pressure prediction. This approach suggests bioinspired pathways for developing adaptive stimulus-responsive mechanosensors, detecting subtle changes across varying stimuli levels with enhanced effectiveness through machine learning.

Inspired by biological sensors that characteristically adapt to varying stimulus ranges, efficiently detecting stimulus changes sooner than the absolute stimulus values, we propose a mechanosensing concept in which the resolution can be adapted by magnetic field (H) gating to detect small pressure-changes under a wide range of compressive stimuli. This is realized with resistive sensing by pillared H-driven assemblies of soft ferromagnetic electrically conducting particles between planar electrodes under a voltage bias. By modulation of H, the pillars respond with mechanically adaptable sensitivity. Higher H enhances current resolution, while it increases scatter among repeating measurements due to increased magnetic structural jamming between colloids in their assembly. To manage the trade-off between electrical resolution and scatter, machine learning is introduced for searching optimum H gatings, thus facilitating efficient pressure prediction. This approach suggests bioinspired pathways for developing adaptive stimulus-responsive mechanosensors, detecting subtle changes across varying stimuli levels with enhanced effectiveness through machine learning.