To date, there have been a plethora of reports on different means to fabricate organic–inorganic metal halide perovskite thin films; however, the inorganic starting materials have been limited to halide-based anions. Here we study the role of the anions in the perovskite solution and their influence upon perovskite crystal growth, film formation and device performance. We find that by using a non-halide lead source (lead acetate) instead of lead chloride or iodide, the perovskite crystal growth is much faster, which allows us to obtain ultrasmooth and almost pinhole-free perovskite films by a simple one-step solution coating with only a few minutes annealing. This synthesis leads to improved device performance in planar heterojunction architectures and answers a critical question as to the role of the anion and excess organic component during crystallization. Our work paves the way to tune the crystal growth kinetics by simple chemistry. Organic-inorganic metal halide perovskites are of considerable promise for efficient, easy to manufacture solar cells. Here, the authors show that the choice of anions in the perovskite solution can considerably affect the crystal growth and performance of these solar cells.
During the last century, inorganic oxide compounds laid foundations for materials synthesis, characterization, and technology translation by adding new functions into devices previously dominated by main-group element semiconductor compounds. Today, compounds with multiple anions beyond the single-oxide ion, such as oxyhalides and oxyhydrides, offer a new materials platform from which superior functionality may arise. Here we review the recent progress, status, and future prospects and challenges facing the development and deployment of mixed-anion compounds, focusing mainly on oxide-derived materials. We devote attention to the crucial roles that multiple anions play during synthesis, characterization, and in the physical properties of these materials. We discuss the opportunities enabled by recent advances in synthetic approaches for design of both local and overall structure, state-of-the-art characterization techniques to distinguish unique structural and chemical states, and chemical/physical properties emerging from the synergy of multiple anions for catalysis, energy conversion, and electronic materials. Inorganic compounds with multiple anions have added a new dimension to the discovery of materials. Here the authors review the recent progress in the development of mixed-anion compounds, focusing on the roles of multiple anions in synthesis, characterization and properties.
At its core, reticular chemistry has translated the precision and expertise of organic and inorganic synthesis to the solid state. While initial excitement over metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) was undoubtedly fueled by their unprecedented porosity and surface areas, the most profound scientific innovation of the field has been the elaboration of design strategies for the synthesis of extended crystalline solids through strong directional bonds. In this contribution we highlight the different classes of reticular materials that have been developed, how these frameworks can be functionalized and how complexity can be introduced into their backbones. Finally, we show how the structural control over these materials is being extended from the molecular scale to their crystal morphology and shape on the nanoscale, all the way to their shaping on the bulk scale.
We introduce ChemPro, a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice Questions and Numerical Questions spread across fine-grained information recall, long-horizon reasoning, multi-concept questions, problem-solving with nuanced articulation, and straightforward questions in a balanced ratio, effectively covering Bio-Chemistry, Inorganic-Chemistry, Organic-Chemistry and Physical-Chemistry. ChemPro is carefully designed analogous to a student's academic evaluation for basic to high-school chemistry. A gradual increase in the question difficulty rigorously tests the ability of LLMs to progress from solving basic problems to solving more sophisticated challenges. We evaluate 45+7 state-of-the-art LLMs, spanning both open-source and proprietary variants, and our analysis reveals that while LLMs perform well on basic chemistry questions, their accuracy declines with different types and levels of complexity. These findings highlight the critical limitations of LLMs in general scientific reasoning and understanding and point towards understudied dimensions of difficulty, emphasizing the need for more robust methodologies to improve LLMs.
We present QDK/Chemistry, a software toolkit for quantum chemistry workflows targeting quantum computers. The toolkit addresses a key challenge in the field: while quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted algorithms in the quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source quantum chemistry packages and quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible quantum chemistry experiments.
Photodynamic antibacterial therapy presents a promising strategy for combating bacterial infections due to its non-invasive nature and low potential for inducing resistance. In this work, we developed a series of electron beam-modified graphitic carbon nitride (g-C<sub>3</sub>N<sub>4</sub>, CN) and titanium carbide (Ti<sub>3</sub>C<sub>2</sub>, TC) nanocomposites, which were subsequently incorporated into polyvinyl alcohol/sodium alginate (PVA/SA) hydrogels through physical cross-linking. The optimized 200CN/1TC composite hydrogel (where 200CN denotes 200 kGy irradiation dose, and 1TC represents 1 wt% TC content) maintained excellent biocompatibility with cell viability exceeding 80% even at the highest nanomaterial loading (8% 200CN/1TC). Notably, the 8% 200CN/1TC composite hydrogel displayed substantial antibacterial activity, forming inhibition zones of 12.3 mm and 10.8 mm against <i>Staphylococcus aureus</i> and <i>Escherichia coli</i>, respectively. The improved performance may be explained by the combined effects of enhanced electron transfer between the component materials and the unique two-dimensional structure of the nanocomposites, though further investigation is required to fully elucidate the underlying mechanisms. This study provides a feasible approach for developing efficient antibacterial hydrogel systems and offers valuable perspectives on the design of nanomaterial-based biomedical materials for wound healing and infection control applications.
This review presents various hole transport layers (HTLs) employed in perovskite solar cells (PSCs) in pursuing high power conversion efficiency (PCE) and functional stability. The PSCs have achieved high PCE (over 23%, certified by NREL) and more efforts have been devoted into research for stability enhancement. Inorganic HTLs become a popular choice as selective contact materials because of their intrinsic chemical stability and low cost. HTLs and electron transport layers (ETLs) are critical components of PSCs due to the requirement to create charge collection selectivity. Herein the authors provide an overview on inorganic HTLs synthesis, properties, and their application in various PSCs for both mesoporous and planar architectures. Inorganic HTLs with appropriate properties, such as proper energy level and high carrier mobility, can not only assist with charge transport, but also improve the stability of PSCs under ambient conditions. The importance of interfacial chemistry and interfacial charge transport is further addressed to understand the underlying mechanism of related degradation and carrier dynamic. It is expected that the success of the inorganic HTL in PSCs can stimulate further research and bring real impact for future photovoltaic technologies.
This paper reports the first gas sensor based on the plastic inorganic semiconductor GaPS4, pioneering the application of plastic inorganic semiconductors in the field of gas sensing. Unlike traditional rigid sensors, this device leverages the unique layered structure and ultra-wide bandgap of GaPS4 to achieve high sensitivity and selectivity in detecting NO2. The intrinsic plastic deformability of the material enables it to conform tightly to complex curved pipelines like an "electronic bandage," completely eliminating monitoring blind spots. Nanoindentation tests reveal that its extremely low hardness (0.20 GPa) confers exceptional flexibility while maintaining stable electrical characteristics even under bent states. The device exhibits a linear response to NO2 concentrations ranging from 1 to 10 ppm at room temperature. Although the limited defects in the single-crystal material result in pA-level response currents, defect engineering offers a viable pathway for performance enhancement. This study breaks through the conventional boundaries of plastic inorganic semiconductors confined to photoelectric and thermoelectric applications, opening new avenues for their use in gas sensing and advancing gas monitoring technology toward "conformal integration."
Mathematical crystal chemistry views crystal structures as the optimal solutions of mathematical optimization problem formalizing inorganic structural chemistry. This paper introduces the minimum and maximum atomic radii depending on the types of geometrical constraints, extending the concept of effective atomic sizes. These radii define permissible interatomic distances instead of interatomic forces, constraining feasible types and connections of coordination polyhedra. The definition shows the aspect that crystal structures are packings of atomic spheres. Additionally, creatability functions for geometrical constraints, which give a choice of creatable types of geometrical constraints depending on the spatial order of atoms, are implemented to guide randomly generated structures toward optimal solutions. The framework identifies unique optimal solutions corresponding to the structures of spinel, pyrochlore ($α$ and $β$), pyroxene, quadruple perovskite, cuprate superconductor $\mathrm{YBa}_2 \mathrm{Cu}_3 \mathrm{O}_{7-x}$, and iron-based superconductor $\mathrm{LaFeAsO}$. Notably, up to $95\%$ of oxide crystal structure types in Inorganic Crystal Structure Database align with the optimal solutions preserving experimental structures despite the discretized feasible atomic radii. These findings highlight the role of mathematical optimization problem as a theoretical foundation for mathematical crystal chemistry, enabling efficient structure prediction.
Creating a single unified interatomic potential capable of attaining ab initio accuracy across all chemistry remains a long-standing challenge in computational chemistry and materials science. This work introduces a training protocol for foundation machine-learning interatomic potentials (MLIPs) that bridge molecular, surface, and materials chemistry through cross-domain learning. First, we introduce enhancements to the MACE architecture that improve its performance on chemically diverse databases by increasing weight sharing across chemical elements and introducing non-linear factors into the tensor decomposition of the product basis. Second, we develop a multi-head replay post-training methodology that enables efficient knowledge transfer across diverse chemical domains. By fine-tuning on datasets at different levels of electronic structure theory, including inorganic crystals, molecular systems, surface chemistry, and reactive organic chemistry, we demonstrate that a single unified model achieves state-of-the-art performance across several chemical domains. Comprehensive benchmarking reveals superior cross-domain transferability compared with existing specialised and multi-task models, with notable improvements in molecular and surface properties while maintaining state-of-the-art performance in materials-property prediction.