Earth-abundant multinary inorganic chalcogenides, such as Cu2BaSnS4-xSex (CBTSSe), have emerged as promising absorbers for next-generation solar cells. However, accessing colloidal CBTSSe nanocrystals, including metastable polymorphs with more favorable optoelectronic properties, remains a critical synthetic challenge. Herein, we report the first use of topotactic anion exchange to access metastable crystal structures in soft chemistry syntheses. By varying the stoichiometry of selenium precursor, we directly synthesize Cu2BaSnS4-xSex (0 ≤ x ≤ 4) nanocrystals with a broad band gap tunability of 1.51-2.04 eV. At x = 4, a new metastable trigonal phase of Cu2BaSnSe4 is isolated. This phase arises via an in situ topotactic anion exchange from sulfur-rich Cu2BaSnS4-xSex intermediates, while retaining both the cation sublattice and overall nanocrystal morphology. The trigonal Cu2BaSnSe4 exhibits a nearly direct band gap that is 280 meV lower than its thermodynamically stable orthorhombic counterpart, aligning more closely with the optimal band gap for single-junction solar cells. In contrast, a postsynthetic anion exchange route leads to the thermodynamically preferred orthorhombic polymorph, pointing to the critical nature of the in situ transformation. Our findings open a versatile pathway for both polymorphic and morphological control in colloidal nanocrystals, expanding the synthetic design space for new optoelectronic materials.

While the polymorphism of chalcopyrite semiconductors has been widely studied, the wurtzite analogues of defect-chalcopyrite II-III2-VI4 compositions, such as Zn(In,Ga)2(S,Se)4, remain underexplored. Here, we report the synthesis of polytypic Zn-(In,Ga)-Se multipods via cation exchange using ZnSe as a template. With zinc-blende cores and wurtzite arms elongated along the hexagonal c-axis, the multipods retain the structure and morphology of the ZnSe template. Optical characterization reveals composition-dependent absorption and photoluminescence, tunable from the visible to the near-infrared region, with spectral features distinct from those of previously reported defect-chalcopyrite structures. Temperature-dependent measurements demonstrate strong emission at cryogenic temperatures, which is quenched near room temperature due to thermally activated nonradiative processes. We illustrate the use of ZnSe as a platform for cation exchange toward wurtzite multinary chalcogenides, unlocking access to novel structures with colorful optical properties.

Ternary I-III-VI2 semiconductors, such as CuInSe2, exhibit diverse polymorphs with unique structural characteristics and optoelectronic properties. This study investigates the pressure-induced phase transitions of metastable wurtzite-like CuInSe2 nanocrystals. Using a combination of synchrotron X-ray diffraction, pair distribution function analysis, and density functional theory calculations, we reveal a transition from cation-ordered wurtzite-like (Pmc21) to cation-disordered NaCl-like (Fm3̅m) structures at 7.7 GPa. The cation-disordered NaCl-like phase persists upon decompression. Bulk modulus calculations highlight size-dependent deviations from bulk material behavior. These findings deepen our understanding of phase stability in colloidal I-III-VI2 semiconductor nanocrystals, with implications for tailoring functional materials under extreme conditions.

High-throughput reaction discovery is necessary to understand complex reaction spaces for inorganic nanocrystal synthesis. Here, we implemented a high-throughput continuous flow millifluidic reactor to perform reaction discovery for Cs-Pb-Br nanocrystal synthesis using a ligand assisted reprecipitation (LARP)-type approach. 3D-printed flow resistors enable the screening of up to 16 different mixing ratios within a single 90 s run, allowing for >270 different precursor concentration ratios to be quickly tested to explore the phase space that results in CsPbBr3, Cs4PbBr6, a biphasic mixture, or no product. To construct a full phase map from these high-throughput experiments, a neural network was trained and validated to predict the product composition (similar to 500 000 points in precursor concentration space). The phase map predicts product composition/phase as a function of Cs-Pb-Br feed ratio. This approach demonstrates how high-throughput flow chemistry can be used in tandem with machine learning to rapidly explore nanocrystal reaction spaces in flow.

Soft-chemistry techniques provide a versatile approach to synthesizing inorganic materials under mild conditions, enabling access to compositions and structures that are challenging to achieve through traditional thermodynamically driven solid-state methods. However, these solution-based routes often result in phase competition, requiring precise control over reaction conditions to achieve selective product formation. While one-variable-at-a-time (OVAT) approaches have traditionally been used for phase selection, data-driven strategies are emerging as more efficient methods for navigating complex synthetic spaces. Ternary metal halides, such as cesium cadmium bromides (Cs-Cd-Br), are of growing interest due to their potential in wide and ultrawide band gap applications. Unlike the well-studied cesium lead halide phases, the compositional diversity and solution-based synthesis of ternary Cs-Cd-Br phases remain largely unexplored. This study systematically investigates the synthetic phase space of the Cs-Cd-Br system by constructing a data-driven phase map. Using a common set of precursors and a standardized experimental procedure, we successfully synthesize all four known Cs-Cd-Br phases─CsCdBr3, Cs2CdBr4, Cs3CdBr5, and Cs7Cd3Br13─each exhibiting distinct structures, morphologies, and optical properties. Our findings highlight the potential of soft-chemistry methods for expanding the library of ternary metal halides and provide key insights into the thermodynamic and kinetic factors governing phase formation.

Solution-processed AgBiS2 thin films were fabricated using novel thiol-amine precursor inks to investigate the stereochemical activity of Bi3+ 6s2 lone pairs and their impact on the structure. A dual-space analysis combining Bragg diffraction and hard X-ray photoelectron spectroscopy (HAXPES) revealed a rock salt-like average structure with local distortions linked to cation coloring. Density functional theory (DFT) and crystal orbital Hamilton population (COHP) analyses confirmed that local Bi-rich and Ag-rich nanodomains amplify stereochemical activity, whereas more mixed and cation-order nanodomains are less stereochemically active. This local, nanoscopic mixing of segregated and ordered domains would indeed explain an average Fm3̅m structure that is rock salt-like and that does not manifest the full anharmonicity and noncentrosymmetry evidenced in canonical structures with stereochemical expression. These findings provide insights into the local structural and electronic complexities governing the optoelectronic properties of AgBiS2 thin films.

Design of one-dimensional (1D) nanomaterials based on non-van der Waals (non-vdW) 1D chain structures is emerging as a new materials frontier, owing to their strong intrinsic anisotropy and broad compositional diversity. However, achieving ultrathin 1D morphology in such systems remains a significant challenge. In this work, we report the colloidal synthesis of ultrathin KFeS2 and RbFeS2 nanowires-representing the first fabrication of ultrathin 1D nanomaterials driven by non-vdW 1D crystal structures. The nanowires exhibit diameters of ∼5 nm and lengths of microns, with anisotropic growth directed by covalent [FeS2]- chains. Magnetic characterization reveals significantly reduced antiferromagnetic transition temperatures and suppressed interchain ferromagnetic interactions, demonstrating pronounced size and morphology effects. Control experiments on structurally related materials indicate that direct nucleation of the 1D phase is essential for achieving the nanowire morphology. These findings establish a new synthetic pathway to an understudied family of non-vdW 1D nanomaterials, enabling exploration of their emergent quantum and magnetic properties.

By driving the electrooxidation of small molecules instead of relying on sluggish oxygen evolution reaction (OER), low -input voltage is obtained for overall water splitting (OWS) and hydrogen generation, requiring active electrocatalysts. Using single-step pulsed laser irradiation, strong metal -support interaction is achieved on Pd/PdO-decorated Ni-3(PO4)(2)& sdot;8H(2)O (NiPh) microflowers, yielding an outstanding bifunctional electrocatalyst for hydrogen evolution (HER) and hydrazine oxidation (HzOR). When Pd/PdO-NiPh-3 serves as both anode and cathode in the OWS electrolyzer (OER||HER), a cell voltage of 2.098 V achieves 10 mA/cm(2) in 1.0 M KOH. When evaluated in the hydrazine -coupled electrolyzer (HzOR||HER), Pd/PdO-NiPh-3 exhibits remarkable stability with a low cell voltage of 0.538 V in 0.5 M-N2H4/1.0 M-KOH, which is approximately 1.56 V lower than that of the traditional water electrolyzers. In Pd/PdO-NiPh, the empty 4s and 5s orbitals of Ni2+ and Pd, respectively, serve as two absorption sites. These sites facilitate chemisorption on the electrocatalyst surface by forming a twoelectron dipolar bond between the lone -pair electrons of NH2 groups in N2H4 and Ni2+ as well as Pd. A feasible strategy for utilizing Pd/PdO-NiPh catalysts in developing direct N2H4 fuel cells is investigated in this work, enabling the simultaneous production of robust energy -saving H-2 fuel and electricity.

Wide band gap AInSe(2) (A = K, Rb, Cs) is an important interlayer material for improving the efficiency of Cu(In,Ga)(S,Se)(2) (CIGS) solar cells. Compared to high-vacuum deposition and solid-state synthesis, a less energy-intensive method is of interest for its fabrication. Herein, we present the rapid, low-temperature colloidal synthesis of AInSe(2) nanocrystals that opens a pathway for convenient solution processing. The crystal structures and electronic band structures of the nanocrystals were studied, and their particle morphology was found to be dependent on the choice of alkali metal and selenium precursors. Homogeneous solid solution (K,Rb,Cs)InSe2 nanocrystals were synthesized using a mixture of alkali metal precursors. Their compositions, lattice parameters, and band gaps were easily tuned based on the K:Rb:Cs precursor ratio, providing potential for interface engineering of CIGS nanocrystal-based solar cells.

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

Colloidal platinum nanoparticles (Pt NPs) possess a myriad of technologically relevant applications. A potentially sustainable route to synthesize Pt NPs is via polyol reduction in ionic liquid (IL) solvents; however, the development of this synthetic method is limited by the fact that reaction kinetics have not been investigated. In-line analysis in a flow reactor is an appealing approach to obtain such kinetic data; unfortunately, the optical featurelessness of Pt NPs in the visible spectrum complicates the direct analysis of flow chemistry products via ultraviolet-visible (UV-vis) spectrophotometry. Here, we report a machine learning (ML)-based approach to analyze in-line UV-vis spectrophotometric data to determine Pt NP product concentrations. Using a benchtop flow reactor with ML-interpreted in-line analysis, we were able to investigate NP yield as a function of residence time for two IL solvents: 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf). While these solvents are structurally similar, the polyol reduction shows radically different yields of Pt NPs depending on which solvent is used. The approach presented here will help develop an understanding of how the subtle differences in the molecular structures of these solvents lead to distinct reaction behavior. The accuracy of the ML prediction was validated by particle size analysis and the error was found to be as low as 4%. This approach is generalizable and has the potential to provide information on various reaction outcomes stemming from solvent effects, for example, differential yields, orders of reaction, rate coefficients, NP sizes, etc.

Thermoelectric materials convert heat into electricity, with a broad range of applications near room temperature (RT). However, the library of RT high-performance materials is limited. Traditional high-temperature synthetic methods constrain the range of materials achievable, hindering the ability to surpass crystal structure limitations and engineer defects. Here, a solution-based synthetic approach is introduced, enabling RT synthesis of powders and exploration of densification at lower temperatures to influence the material's microstructure. The approach is exemplified by Ag2Se, an n-type alternative to bismuth telluride. It is demonstrated that the concentration of Ag interstitials, grain boundaries, and dislocations are directly correlated to the sintering temperature, and achieve a figure of merit of 1.1 from RT to 100 degree celsius after optimization. Moreover, insights into and resolve Ag2Se's challenges are provided, including stoichiometry issues leading to irreproducible performances. This work highlights the potential of RT solution synthesis in expanding the repertoire of high-performance thermoelectric materials for practical applications.

Additive engineering of lead halide perovskites has been a successful strategy for reducing a variety of deleterious defect types. Ionic liquids (ILs) are a unique group of such additives that have been used to passivate halide vacancies in both bulk lead halide perovskites and their colloidal nanocrystal analogues. Herein, we expand the types of defects that can be addressed through IL treatments in CsPbBr3 nanocrystals with a novel phosphonium tribromide IL that heals metallic lead surface defects through redox chemistry. This new type of surface treatment leads to a significant increase in PLQY and outperforms equivalent treatments with non-redox-active bromide ILs. Such redox-active ligands widen the scope of defect types that can be addressed in semiconductor nanocrystals.

The power conversion efficiencies of lead halide perovskite thin film solar cells have surged in the short time since their inception. Compounds, such as ionic liquids (ILs), have been explored as chemical additives and interface modifiers in perovskite solar cells, contributing to the rapid increase in cell efficiencies. However, due to the small surface area-to-volume ratio of the large grained polycrystalline halide perovskite films, an atomistic understanding of the interaction between ILs and perovskite surfaces is limited. Here, we use quantum dots (QDs) to study the coordinative surface interaction between phosphonium-based ILs and CsPbBr3. When native oleylammonium oleate ligands are exchanged off the QD surface with the phosphonium cation as well as the IL anion, a threefold increase in photoluminescent quantum yield of as-synthesized QDs is observed. The CsPbBr3 QD structure, shape, and size remain unchanged after ligand exchange, indicating only a surface ligand interaction at approximately equimolar additions of the IL. Increased concentrations of the IL lead to a disadvantageous phase change and a concomitant decrease in photoluminescent quantum yields. Valuable information regarding the coordinative interaction between certain ILs and lead halide perovskites has been elucidated and can be used for informed pairing of beneficial combinations of IL cations and anions.

Copper selenides are an important family of materials with applications in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the Cu-Se phase diagram is complex and contains multiple crystal structures in addition to several metastable structures that are not found on the thermodynamic phase diagram. Consequently, the ability to synthetically navigate this complex phase space poses a significant challenge. We demonstrate that data-driven learning can successfully map this phase space in a minimal number of experiments. We combine soft chemistry (chimie douce) synthetic methods with multivariate analyses via classification techniques to enable predictive phase determination. A surrogate model was constructed with experimental data derived from a design matrix of four experimental variables: C-Se bond strength of the selenium precursor, time, temperature, and solvent composition. The reactions in the surrogate model resulted in 11 distinct phase combinations of copper selenide. These data were used to train a classification model that predicts the phase with 95.7% accuracy. The resulting decision tree enabled conclusions to be drawn about how the experimental variables affect the phase and provided prescriptive synthetic conditions for specific phase isolation. This guided the accelerated phase targeting in a minimum number of experiments of klockmannite CuSe, which could not be isolated in any of the reactions used to construct the surrogate model. The reaction conditions that the model predicted to synthesize klockmannite CuSe were experimentally validated, highlighting the utility of this approach.

Recycling ionic liquid (IL) solvents can reduce the lifecycle cost of these expensive solvents. Liquid-liquid extraction is the most straightforward approach to purify IL solvents and is typically performed with an immiscible washing agent (e.g., water). Herein, we describe a recycling route for water-miscible ILs in which direct recycling is usually challenging. We use hydrophobic ILs as accommodating agents to draw the water-miscible IL from the aqueous washing stream. A biphasic slug flow of the mixed ILs and water is then separated by using a membrane. The water-miscible IL can then be drawn out from the mixed IL phase with acidified water and dried under vacuum. Both the water-miscible IL and the accommodating agent are then recycled. Here, we demonstrated a proof-of-concept of this process by recycling 1-butyl-3-methylimidazolium trifluoromethanesulfonate (BMIM-OTf) in the presence of the accommodating agent 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (BMIM-NTf2) and acidified water. We then demonstrated the capacity to recycle 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) from a realistic synthetic application: Pt nanoparticle synthesis in the water-miscible IL.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTWe Need to Talk about New Materials CharacterizationTrevor W. Hayton*Trevor W. HaytonDepartment of Chemistry and Biochemistry, University of California at Santa Barbara, Santa Barbara, California 93106, United States*Email: [email protected]More by Trevor W. HaytonView Biographyhttps://orcid.org/0000-0003-4370-1424, Simon M. HumphreySimon M. HumphreyDepartment of Chemistry, University of Texas at Austin, 105 East 24th Street, Stop A5300, Austin, Texas 78734-0165, United StatesMore by Simon M. HumphreyView Biographyhttps://orcid.org/0000-0001-5379-4623, Brandi M. CossairtBrandi M. CossairtDepartment of Chemistry, University of Washington, Seattle, Washington 98195, United StatesMore by Brandi M. CossairtView Biographyhttps://orcid.org/0000-0002-9891-3259, and Richard L. BrutcheyRichard L. BrutcheyDepartment of Chemistry, University of Southern California, Los Angeles, California 90089, United StatesMore by Richard L. BrutcheyView Biographyhttps://orcid.org/0000-0002-7781-5596Cite this: Inorg. Chem. 2023, 62, 33, 13165–13167Publication Date (Web):August 9, 2023Publication History Received24 July 2023Published online9 August 2023Published inissue 21 August 2023https://pubs.acs.org/doi/10.1021/acs.inorgchem.3c02524https://doi.org/10.1021/acs.inorgchem.3c02524editorialACS PublicationsCopyright © Published 2023 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views5137Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (947 KB) Get e-AlertscloseSUBJECTS:Diffraction,Inorganic chemistry,Materials,Metal organic frameworks,Nuclear magnetic resonance spectroscopy Get e-Alerts

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

An experimentally guided, early-stage techno-economic analysis reveals how ionic liquids can be economically adapted at scale through novel recycling methods to unlock their environmental benefits when used as solvents for nanoparticle syntheses.