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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 CsPbBr 3 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.

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)InSe 2 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., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

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.

The hydrogenation of CO 2 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 CO 2 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 SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 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.

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-NTf 2 ) 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., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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.

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.

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., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

A P 16 2- polyphosphide dianion ink was produced by the reaction of red phosphorus with a binary thiol-amine mixture of ethanethiol (ET) and ethylenediamine (en). The polyphosphide was identified by solution 31 P NMR spectroscopy and electrospray ionization mass spectrometry. This solute was compared to the reaction products of white phosphorus (P 4 ) and other elemental pnictides in the same solvent system. The reaction of P 4 with ET and en gives the same P 16 2- polyphosphide; however, the easier handling and lower reactivity of red phosphorus highlights the novelty of that reaction. Elemental arsenic and antimony both give mononuclear pnictogen-sulfide-thiolate complexes upon reaction with ET and en under otherwise identical conditions, with this difference likely resulting from the greater covalency and tendency of phosphorus to form P-P bonds.

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42 650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. A classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr 3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

There is increasing demand for tailored molecular inks that produce phase-pure solution-processed semiconductor films. Within the Cu-Sn-S phase space, Cu 2 SnS 3 belongs to the I 2 -IV-VI 3 class of semiconductors that crystallizes in several different polymorphs. We report the ability of thiol-amine solvent mixtures to dissolve inexpensive bulk Cu 2 S and SnO precursors to generate free-flowing molecular inks. Upon mild annealing, polymorphic control over phase-pure tetragonal ( I 4̅2 m ) and orthorhombic ( Cmc 2 1 ) Cu 2 SnS 3 films was realized simply by switching the identity of the thiol (i.e., 1,2-ethanedithiol vs 2-mercaptoethanol, respectively). Polymorph control is dictated by differences in the resulting molecular metal-thiolate complexes and their subsequent decomposition profiles, which likely seed distinct Cu 2- x S phases that template the ternary sulfide sublattice. The p-type tetragonal and orthorhombic Cu 2 SnS 3 films possess similar experimental direct optical band gaps of 0.94 and 0.88 eV, respectively, and strong photoelectrochemical current responses. Understanding how ink formulation dictates polymorph choice should inform the development of other thiol-amine inks for solution-processed films., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Transition-metal carbides are promising low-cost materials for various catalytic transformations due to their multifunctionality and noble-metal-like behavior. Nanostructuring transition-metal carbides offers advantages resulting from the large surface-area-to-volume ratios inherent in colloidal nanoparticle catalysts; however, a barrier for their utilization is removal of the long-chain aliphatic ligands on their surface to access active sites. Annealing procedures to remove these ligands require temperatures greater than the catalyst synthesis and catalytic reaction temperatures and may further result in coking or particle sintering that can reduce catalytic performance. One way to circumvent this problem is by replacing the long-chain aliphatic ligands with smaller ligands that can be easily removed through low-temperature thermolytic decomposition. Here, we present the exchange of native oleylamine ligands on colloidal α-MoC 1- x nanoparticles for thermally labile tert -butylamine ligands. Analyses of the ligand exchange reaction by solution 1 H NMR spectroscopy, FT-IR spectroscopy, and thermogravimetric analysis-mass spectrometry (TGA-MS) confirm the displacement of 60% of the native oleylamine ligands for the thermally labile tert -butylamine, which can be removed with a mild activation step at 250 °C. Catalytic site densities were determined by carbon monoxide (CO) chemisorption, demonstrating that the mild thermal treatment at 250 °C activates ca. 25% of the total binding sites, while the native oleylamine-terminated MoC 1- x nanoparticles showed no available surface binding sites after this low-temperature treatment. The mild pretreatment at 250 °C also shows distinctly different initial activities and postinduction period selectivities in the CO 2 hydrogenation reaction for the ligand exchanged MoC 1- x nanoparticle catalysts and the as-prepared material., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

The broad implementation of thermoelectricity requires high-performance and low-cost materials. One possibility is employing surfactant-free solution synthesis to produce nanopowders. We propose the strategy of functionalizing "naked" particles' surface by inorganic molecules to control the nanostructure and, consequently, thermoelectric performance. In particular, we use bismuth thiolates to functionalize surfactant-free SnTe particles' surfaces. Upon thermal processing, bismuth thiolates decomposition renders SnTe-Bi 2 S 3 nanocomposites with synergistic functions: 1) carrier concentration optimization by Bi doping; 2) Seebeck coefficient enhancement and bipolar effect suppression by energy filtering; and 3) lattice thermal conductivity reduction by small grain domains, grain boundaries and nanostructuration. Overall, the SnTe-Bi 2 S 3 nanocomposites exhibit peak z   T up to 1.3 at 873 K and an average z   T of ≈0.6 at 300-873 K, which is among the highest reported for solution-processed SnTe., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Angewandte Chemie published by Wiley-VCH GmbH.)

The broad implementation of thermoelectricity requires high-performance and low-cost materials. One possibility is employing surfactant-free solution synthesis to produce nanopowders. We propose the strategy of functionalizing "naked" particles' surface by inorganic molecules to control the nanostructure and, consequently, thermoelectric performance. In particular, we use bismuth thiolates to functionalize surfactant-free SnTe particles' surfaces. Upon thermal processing, bismuth thiolates decomposition renders SnTe-Bi 2 S 3 nanocomposites with synergistic functions: 1) carrier concentration optimization by Bi doping; 2) Seebeck coefficient enhancement and bipolar effect suppression by energy filtering; and 3) lattice thermal conductivity reduction by small grain domains, grain boundaries and nanostructuration. Overall, the SnTe-Bi 2 S 3 nanocomposites exhibit peak z T up to 1.3 at 873 K and an average z T of ≈0.6 at 300-873 K, which is among the highest reported for solution-processed SnTe., (© 2022 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Oxide dissolution is important for metal extraction from ores and has become an attractive route for the preparation of inks for thin film solution deposition; however, oxide dissolution is often kinetically challenging. While binary "alkahest" systems comprised of thiols and N -donor species, such as amines, are known to dissolve a wide range of oxides, the mechanism of dissolution and identity of the resulting solute(s) remain unstudied. Here, we demonstrate facile dissolution of both bulk synthetic and natural mineral ZnO samples using an "alkahest" that operates via reaction with thiophenol and 1-methylimidazole (MeIm) to give a single, pseudotetrahedral Zn(SPh) 2 (MeIm) 2 molecular solute identified by X-ray crystallography. The kinetics of ZnO dissolution were measured using solution 1 H NMR, and the reaction was found to be zero-order in the presence of excess ligands, with more electron withdrawing para -substituted thiophenols resulting in faster dissolution. A negative entropy of activation was measured by Eyring analysis, indicating associative ligand binding in, or prior to, the rate determining step. Combined experimental and computational surface binding studies on ZnO reveal stronger, irreversible thiophenol binding compared to MeIm, leading to a proposed dissolution mechanism initiated by thiol binding to the ZnO surface with the liberation of water, followed by alternating MeIm and thiolate ligand additions, and ultimately cleavage of the ligated zinc complex from the ZnO surface. Design rules garnered from the mechanistic insight provided by this study should inform the dissolution of other bulk oxides into inks for solution processed thin films., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)