ACS Materials Au最新文献

High-entropy materials, first demonstrated in metallic alloys and later extended to oxides and other systems, unlock a vast compositional space with properties suited for catalysis, energy, and structural materials. However, the high compositional complexity makes systematic exploration challenging, and only a small portion of the design space has been studied. To address this, we introduce an active learning strategy that integrates predictive modeling, uncertainty estimation, and iterative sampling to efficiently navigate embedded compositional material spaces. This approach continuously learns from previous evaluations, focusing subsequent searches on the most promising regions while reducing both time and data requirements. We demonstrate this methodology in the search for high-entropy oxygen carriers for chemical looping, where it rapidly accelerates discovery and identifies promising candidates more effectively than conventional trial-and-error or grid-search approaches. Importantly, this strategy is general and well-suited to exploring the vast space of multicomponent materials.

Mixtures of nanocrystals organized into dense monolayers can feature optical properties unattainable in films of homogeneous materials or in one-component nanocrystal assemblies. Doped metal oxide nanocrystals, which display metallic behavior with localized surface plasmon resonances that are tunable by dopant concentration, offer versatility as colloidal building blocks for such metasurfaces. By selecting nanocrystal components and mixing proportions, monolayers can be formed with multiple in- and out-of-plane collective plasmon resonances and broad spectral windows of near-zero permittivity. Here, we present a computational study using a mutual polarization method to assess how compositional correlations within a mixed binary monolayer of tin-doped indium oxide nanocrystals shift the balance between homo- and heterocoupling at a fixed nanocrystal mixing ratio, impacting optical properties from effective permittivity to near-field intensity spectra. The results highlight how the ability to control compositional ordering upon nanocrystal assembly would expand the design space available for creating metasurfaces with targeted optical responses.

The development of high-performance electrode and solid electrolyte materials is crucial for the advancement of next-generation electrochemical energy storage systems. Among emerging synthesis strategies, ionic liquids (ILs) and deep eutectic solvents (DESs) have gained increasing attention as alternative reaction media due to their unique physicochemical properties, including high thermal stability, a wide electrochemical stability window, low vapor pressure, and tunable composition and polarity. These features offer unprecedented control over particle morphology, composition, and surface chemistry, enabling the formation of novel or metastable phases, as well as in situ surface functionalization or generation of homogeneous carbon coatings through postannealing treatments. Despite these promising attributes, the implementation of ILs and DESs at an industrial scale remains to date limited. Major challenges include high viscosity, recycling difficulties, high costs, and a lack of large-scale proofs of concept. After introducing ILs and DESs, and their specific properties, this review critically evaluates the potential and limitations of IL- and DES-based synthesis methods in comparison to conventional techniques such as solid-state and hydrothermal approaches. The benefits and impacts of these uncommon solvents on material morphology and functional properties are discussed along with a systematic comparison with the electrochemical performance of similar materials synthesized via classical methods. This review further discusses the prospects for industrial integration and highlights key areas where further research is essential. Finally, this review provides some perspectives that would allow for mastering these synthesis approaches and developing optimized materials for electrochemical energy storage.

Zeolites with core-shell morphologies represent a captivating class of materials that exhibit distinctive catalytic properties in comparison with conventional zeolite catalysts. Due to the ordered organization of each zeolite component in a core-shell configuration, the stability, mass transport characteristics, and product distribution of these catalysts can significantly differ from those of single-component zeolites. Consequently, the precise construction and structural characterization of core-shell zeolite materials present considerable challenges relative to traditional single-component zeolites. In this review, we provide a comprehensive overview of the synthesis methodologies for core-shell zeolite materials, along with a detailed comparison of the advantages and limitations associated with each approach. Additionally, the characterization techniques currently employed to elucidate the structural features of core-shell structures will be summarized. Finally, we will explore the opportunities and challenges presented by core-shell zeolite catalysts in the development of advanced characterization techniques and the exploration of emerging applications, contrasting them with conventional zeolite materials.

Copper is a highly promising catalyst for the electrochemical CO₂ reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as ethylene and ethanol. Since the CO2RR takes place in aqueous solution, the detailed atomic structure of the water-copper interface is essential for unraveling the key reaction mechanisms. In this study, we investigate copper-water interfaces exhibiting nanometer-scale roughnesses. We introduce two molecular dynamics protocols to create rough copper surfaces, which are subsequently brought into contact with water. From these interfaces, we sample additional training configurations from machine-learning-interatomic-potential-driven molecular dynamics simulations containing hundreds of thousands of atoms. An active learning workflow is developed to identify regions with high spatially resolved uncertainty and convert them into DFT-feasible cells through a modified amorphous matrix embedding approach. Finally, we analyze the local environments at the interface using unsupervised machine-learning techniques. Unique environments emerge on the rough copper surfaces absent from model systems, including stacking-fault-induced configurations and undercoordinated corner atoms. Notably, corner atoms consistently feature chemisorbed water molecules in our simulations, indicating their potential importance in catalytic processes.

Unraveling the atomic structures of chemically exfoliated precious metal dioxide (PMD) nanosheets is the key to understanding their diverse properties and realizing their potential in applications like catalysis. Using pair distribution function (PDF) analysis, we have solved the structures of platinate and iridate nanosheets, revealing they both adopt a T-MoS₂-type crystal structure. This discovery not only establishes a crucial structural analogy to well-understood transition metal dichalcogenides (TMDs) but, more importantly, allows us to explain the origins of their distinct properties. Our calculations based on these structures correctly predict that the platinate nanosheet is a yellow semiconductor, while the iridate nanosheet is a blue semimetal. Having established this powerful structure-property relationship, we further probed the unique chemical nature of these materials. We found that the structural polymorphism (T- vs T'-type) is governed by intrinsic elemental characteristics, rather than simple redox states as explored by in situ experiments. Instead of large-scale distortions, these nanosheets exhibit subtle short-range order (SRO) in their metal atom positions. This work provides a robust methodology for PMD research and highlights that chemically imparted features like SRO are key to designing the next generation of 2D materials.

Thermal expansion (TE) describes the behavior of a solid material as it responds to a change in temperature. The behavior is affected by the components of the material, and the strength of the bonds used to construct it. For materials that are held together by strong covalent bonds, TE is often reduced as the dimensionality increases. For example, diamond, which is covalently bonded in three dimensions, undergoes less expansion than fullerene, which is a discrete molecule. In molecular solid materials assembled through noncovalent bonds, TE is generally larger because the bonds are weaker. Here, we demonstrate synthesis of a series of hydrogen-bonded solids with differing dimensionality of the hydrogen-bonding network. Notably, the same molecular building blocks were used to construct all solids and dimensionality differences were achieved by modifying the stoichiometric ratio of the starting materials. All solids exhibit anisotropic TE behavior, and systematically increasing the dimensionality affords corresponding control over TE. Moreover, based on unexpected hydrogen-bonding behavior in one solid, a shape-size mimicry approach was successfully used to prepare a ternary molecular solid. Lastly, one of the two-dimensional (2D) hydrogen-bonded networks described here exhibits TE behavior that is similar to graphite and black phosphorus, classic 2D covalent-bond-based materials. The strategy of using identical molecular building blocks to construct multicomponent solids with differing dimensionalities is uncommon and offers a way to control TE in solid-state materials.

Biohybrid polyethylene (PE) and polycaprolactone (PCL) were used to regenerate cartilage and bone tissues for repair of knee joint defects. Biohybrid polyethylene was synthesized using ring-opening metathesis polymerization methods with polyethylene-containing macromonomer and arginylglycylaspartic acid (RGD), laminin-derived peptide A5G81 (AGQWHRVSVRWG), or hyaluronic acid-containing macromonomer. The resultant brush copolymers were formulated with ultrahigh molecular weight polyethylene to create formulations for testing with human chondrocyte assays. PE-RGD formulation was determined to be the most effective in causing statistically significant chondrocyte proliferation consistently. PCL-RGD was used to create the stem of the plug for osteogenesis. The materials were 3D printed using dual nozzle fused filament fabrication method to create a biphasic interlocking plug for in vivo testing of cartilage and bone regeneration in porcine knee joint defect models. The studies provide a guide for fabrication of cartilage repair implants using biohybrid polymers.

The convergence of materials science and biology has reshaped the design of biomaterials, exposing both new opportunities and unresolved challenges. Among natural polymers, collagen remains a cornerstone due to its biocompatibility and structural affinity with the extracellular matrix. However, its intrinsic mechanical weakness, rapid degradation, and limited bioactivity restrict its clinical potential. The incorporation of inorganic phasescarbon nanostructures, metallic nanoparticles, or functional oxideshas emerged as a route to overcome these limitations and introduce new functionalities such as antimicrobial protection, osteoconductivity, electrical responsiveness, and stimuli sensitivity. Yet, this hybridization introduces complex interfacial phenomena that demand careful architectural and chemical control. The spatial organization of pores, fibers, and surface topographies governs nutrient diffusion and cell alignment, while interface chemistry dictates stability, degradation, and biological signaling. Despite significant progress, reproducibility and long-term safety remain inconsistent across studies, hindered by variations in collagen source, particle distribution, and cross-linking strategies. Beyond empirical formulation, future progress requires mechanism-guided design frameworks that link composition, structure, and function to predictable biological outcomes. This review critically examines advances in collagen-inorganic composites, highlighting key structure-property-function relationships, manufacturing strategies, and translational barriers. By mapping trends through bibliometric analysis and synthesizing evidence from recent studies, it outlines a roadmap toward reproducible, multifunctional, and clinically relevant collagen-based biomaterials.