ACS Materials Letters最新文献

Switchable divergent organic transformations represent a straightforward but challenging method to synthesize structurally varied compounds starting from the same set of raw materials. Herein, we report the divergent dehydrocoupling of thiols into tunable disulfides/thioethers and H₂ in response to the visible or ultraviolet (UV) light, over CdS quantum dots. Regulating the irradiation wavelength allows disulfides and thioethers to be synthesized in moderate to high yields with good functional group tolerance. Mechanistic studies reveal that thiols are oxidized to produce sulfur-centered radicals by photogenerated holes under visible light irradiation, which then undergo S–S coupling to form disulfides. While under UV light irradiation, the cleavage of C–S bonds in thiols occurs readily to afford aryl radicals, which interact with sulfur-centered radicals, undergoing C–S coupling to obtain thioethers. This work is expected to open an avenue of light-controlled switch to maneuver a radical conversion route for divergent synthesis of value-added fine chemicals.

Determining the critical concentrations of metal nodes and ligands required for metal–organic framework (MOF) nucleation on a substrate remains a fundamental challenge. Here we propose an electrochemical method to address this issue by integrating in situ surface mass change analysis with in situ surface pH measurement during a cathodic deposition process. Taking UTSA-280 ([Ca(C₄O₄)]_n) as an example, the critical metal ion (c(Ca²⁺)) and deprotonated ligand (c(C₄O₄^2–)) concentrations for UTSA-280 nucleation on a gold substrate are determined. It is found that the values of c(Ca²⁺) × c(C₄O₄^2–) in different electrolytes with varied bulk Ca²⁺ concentrations are similar, which can be seen as an analogue of the solubility product of UTSA-280 considering its chemical formula. Also, a simple yet previously overlooked strategy to shorten the incubation time for MOF nucleation in cathodic deposition is proposed and verified. The presented method can be applied to other MOFs.

Three different length ligands (3/6/11-aminopropylthiol hydrochloride (APT/AHT/AUT)) were modified onto the surface of ultrasmall (d < 3 nm) gold nanoparticles (AuNPs) to investigate the ligand length on their antibacterial performance. Compared with the medium-chain AHT-AuNPs, both the short-chain APT-AuNPs and the long-chain AUT-AuNPs exhibited better antibacterial activity against Escherichia coli and Staphylococcus aureus. Antibacterial mechanistic investigations revealed that the adsorption efficiency of AHT-AuNPs on bacterial membranes was significantly lower compared with APT-AuNPs and AUT-AuNPs. This resulted in reduced membrane disruption, decreased ATP depletion, and diminished ROS generation by AHT-AuNPs relative to APT-AuNPs and AUT-AuNPs, ultimately leading to a lower antibacterial efficacy of AHT-AuNPs compared with APT-AuNPs and AUT-AuNPs. This study offers novel insights into the correlation between the structural features of ultrasmall AuNPs and their antibacterial efficacy, serving as a valuable reference for optimizing the ligand chain length in the development of high-performance nanoantibacterial materials.

Amorphous ionic materials hold promise for advanced energy storage, electrocatalysis, and optical devices, yet systematically evaluating their propensity to form amorphous phases remains underdeveloped. Here, we present a machine learning framework for efficiently predicting amorphization enthalpy, a universal measure of the thermodynamic cost of converting crystalline ionic compounds to amorphous phases. By combining a standardized DFT-based melt-and-quench protocol with machine learning, we build a training set of 407 compounds and identify key descriptors linked to the amorphization enthalpy. A random forest regressor highlights relevant features but shows a limited predictive power. To overcome this, we implement e3nn, one of the state-of-art graph neural networks (GNN), and adopt a transfer-learning approach. Applying this GNN to screen 12,123 ionic compounds reveals that nitrides and sulfides typically resist amorphization, while alkali- or halogen-rich and multi-cation compositions favor it. Our data-driven approach offers a practical guide for discovering novel amorphous ionic materials, accelerating experimental development across diverse applications.

Composite materials offer versatile properties, but predicting their mechanical behavior remains challenging due to complex morphology-performance relationships. We address this challenge using convolutional neural networks (CNNs) to analyze X-ray computed tomography (CT) images of cold-sintered polymer-ceramic composites. Traditional machine learning models with morphological features as inputs yielded limited accuracy, while transfer learning from pretrained CNNs improved predictions. Bayesian hyperparameter optimization and ensemble learning further refined the model, achieving R² values of up to 0.94 on unseen data. Leveraging the z-stack nature of CT imaging, a meta-learning approach enhanced predictions, improving R² to 0.95. This study demonstrates alternative machine learning approaches using small datasets to uncover morphology–structure–property relationships in composites and highlights the potential of computer vision in materials development.

Adsorptive separation of Xe/Kr is challenging due to their similar properties. A notable difference between Xe and Kr lies in their polarizability, with Xe being much “softer” (having a higher polarizability). By taking advantage of this, in this work, nonpolar methyl groups were incorporated into metal–organic frameworks (MOFs) in a programmable manner to provide a synergistic effect derived from the pore confinement and nonpolar pore environment. The pillar-layered MOF, NNM-30, can capture Xe with an exceptionally high capacity of 3.07 mmol/g at 298 K and 20 kPa, a more than 7-fold increase compared to that achieved with pristine MOF (Co-DMOF). A high Xe/Kr selectivity (16.56) was also observed. The excellent separation capacity under dry or humid conditions for a 20:80 Xe/Kr mixture was confirmed by breakthrough experiments. Additionally, Co-DMOF-(CH₃)₄ showed efficient Xe capture at an ultralow concentration (400 ppm), which indicates it is promising for Xe removal from the used nuclear fuel reprocessing off-gas.

Chalcogenide supertetrahedral functional units play a vital role in both fundamental research and technological applications, but discovering new types of these units remains a significant challenge. This gap is largely due to the difficulty in inducing stable, novel supertetrahedral structures with unique properties. In this work, we successfully synthesize [Ba₂₂(SO₄)₅][Zn₁₄Ga₁₈S₅₈] (BZGSO), the first sulfate-incorporated salt-inclusion chalcogenide, using high-temperature solid-state methods. BZGSO features a unique, noncentrosymmetric FTW-type framework composed of unprecedented pseudosupertetrahedral [Zn₁₃Ga₁₆S₅₈] functional units. These units are formed by defective [Zn₃Ga₄S₁₆] clusters around a [ZnS₆] octahedron. Notably, BZGSO exhibits promising characteristics for infrared nonlinear optical applications. The theoretical analysis reveals key structure–property relationships, and a systematic study of X/Zn/Ga/S compounds suggests a trend in structural dimensionality based on [X/(Zn+Ga)] ratios. This work not only advances the understanding of supertetrahedral chemistry but also paves the way for the discovery of materials with potential technological applications.

The mechanism of electron transport in the polymer P(NDI2OD-T2) (poly(N,N′-bis-2-octyldodecylnaphthalene-1,4,5,8-bis-dicarboximide-2,6-diyl-alt-2,2′-bithiophene-5,5′-diyl), N2200) is investigated. We use spectroelectrochemical measurements on P(NDI2OD-T2), spectroscopic studies of a chemically reduced model compound, 2,2′-(2,2′-bithiophene-5,5′-diyl)-bis(N,N′-di-n-hexylnaphthalene-1,8:4,5-bis(dicarboximide)) (NDI-T2-NDI), and electronic structure calculations to evaluate the microscopic charge-transport parameters. Experimental and computational data suggest that NDI-T2-NDI^•– is a class-II mixed-valence compound, strongly supporting the small-polaron hopping model as a charge-transport mechanism for electrons along polymer chains in P(NDI2OD-T2). The electronic coupling between the NDI redox units is at least 21 meV, while the reorganization energy is between 0.45 and 0.56 eV. Using a hopping model, we estimated the mobility and activation energy for electron transport along P(NDI2OD-T2) polymer chains to be 0.15 cm² V^–1 s^–1 and 60 meV, respectively. Our study elucidates a long-standing issue of explaining the coexistence in P(NDI2OD-T2) of localized redox sites with relatively large electron mobilities more usually achieved only in highly conjugated polymers.

This review focuses on the use of microfluidic technology for the fabrication of heterogeneous micro–nano-structured materials, with an emphasis on anisotropic structures shaped by hydrodynamic conditions. Microfluidics allows precise control over fluid flow, enabling the creation of materials with tailored size, shape, and composition at the microscale. Through the manipulation of parameters such as flow rate, viscosity, and surface tension, it is possible to produce complex heterogeneous micro–nano structures with varying functional properties. These materials are particularly promising for biomedical applications including cell culture, drug delivery, tissue engineering, and in vitro models. The review also addresses the challenges and future directions in the field, highlighting the potential for microfluidic technology to advance biomedical research, personalized medicine, and regenerative therapies.

Confining water molecules within sub1 nm channels is recognized as an effective strategy for improving proton conductivity. Utilizing a hydrothermal synthesis approach, two new two-dimensional (2D) polyoxometalate-based metal–organic frameworks (POMOFs) were successfully synthesized, named [Ni(Bip)₂(H₂O)₂(γ-Mo₈O₂₆)]·3H₂O (CUST-862) and [Co₂(Bip)₂(H₂O)₄(γ-Mo₈O₂₆)]·2H₂O (CUST-863). Thermogravimetric analysis (TGA) and powder X-ray diffraction (PXRD) results demonstrated that both compounds exhibit excellent water stability and thermal stability. Alternating current (AC) impedance spectroscopy revealed that CUST-862 achieved a maximum proton conductivity of 1.97 × 10^–3 S cm^–1, which is an order of magnitude higher than that of CUST-863 under conditions of 90 °C and 98% relative humidity (RH). The space confined effect of sub-1-nm channels acting on free molecules in CUST-862 effectively enhances the proton conductivity. The results obtained from attenuated total reflection infrared (ATR-IR) and water vapor adsorption–desorption tests offered a comprehensive explanation for the confinement effect. This research gives a novel approach for enhancing proton conductivities in POMs-based materials.