Synthesis Of Inorganic Materials Research Articles

Flash-within-flash synthesis of gram-scale solid-state materials.

Sustainable manufacturing that prioritizes energy efficiency, minimal water use, scalability and the ability to generate diverse materials is essential to advance inorganic materials production while maintaining environmental consciousness. However, current manufacturing practices are not yet equipped to fully meet these requirements. Here we describe a flash-within-flash Joule heating (FWF) technique-a non-equilibrium, ultrafast heat conduction method-to prepare ten transition metal dichalcogenides, three group XIV dichalcogenides and nine non-transition metal dichalcogenide materials, each in under 5 s while in ambient conditions. FWF achieves enormous advantages in facile gram scalability and in sustainable manufacturing criteria when compared with other synthesis methods. Also, FWF allows the production of phase-selective and single-crystalline bulk powders, a phenomenon rarely observed by any other synthesis method. Furthermore, FWF MoSe2 outperformed commercially available MoSe2 in tribology, showcasing the quality of FWF materials. The capability for atom substitution and doping further highlights the versatility of FWF as a general bulk inorganic materials synthesis protocol.

Flux synthesis of two-dimensional covalent organic frameworks.

Covalent organic frameworks (COFs) are crystalline porous polymers constructed from organic building blocks into ordered two- or three-dimensional networks through dynamic covalent bonds. Attributed to their high porosity, well-defined structure, tailored functionality and excellent chemical stability, COFs have been considered ideal sorbents for various separation applications. The synthesis of COFs mainly employs the solvothermal method, which usually requires organic solvents in sealed Pyrex tubes, resulting in unscalable powdery products and environmental pollution that seriously limits their practical applications. Herein, our protocol focuses on an emerging synthesis method for COFs based on organic flux synthesis without adding solvents. The generality of this synthesis protocol has been applied in preparing various types of COFs, including olefin-linked, imide-linked, Schiff-based COFs on both gram and kilogram scales. Furthermore, organic flux synthesis avoids the disadvantages of solvothermal synthesis and enhances the crystallization and porosity of COFs. Typically, COF synthesis takes 3-5 d to complete, and subsequent washing procedures leading to pure COFs need 1 d. The procedure for kilogram-scale production of COFs with commercially available monomers is also provided. The resulting COFs are suitable for separation applications, particularly as adsorbent materials for industrial gas separation and water treatment applications. The protocol is suited for users with prior expertise in the synthesis of inorganic materials and porous organic materials.

Chiral Janus emulsions with gold nanocomposite for enantioselective catalysis

Chiral emulsions, with the chiral center of chiral-surfactant as the emulsifier, provide opportunities for selective catalysis and inorganic material synthesis. Janus emulsion draws researcher’s attention due to excellent multiple chambers, advanced structures and diverse interfaces. Chiral Janus emulsions are prepared with two immiscible oils of vegetable oil (VO)/dimethyl phthalate (DMP) and 2-(Perfluorooctyl)ethyl methacrylate (PFOEMA)/Ethoxylated trimethylolpropane triacrylate (ETPTA) as inner phases, by emulsified by chiral-surfactant [H-G-C16]FeCl4, in batch-scale by traditional vortex mixing. AuNPs as functional nanomaterials with peroxidase activity are then applied as active centers, adsorbing at the oil/water interface due to the electrostatic interactions between the positive charge of [H-G-C16]FeCl4 at Janus emulsion interfaces and the negative charge of AuNPs modified by SDS. Chiral Janus emulsion is endowed with catalytic activity and enantioselectivity. Catalytic results show two AuNPs@ Janus emulsions show better affinity to catalyze the oxidation of L-DOPA than D-DOPA. By investigating two AuNPs@ Janus emulsions with different inner phases, chiral Janus emulsion of AuNPs@ETPTA/PFOEMA/[H-G-C16]FeCl4(aq) has better catalytic activity and enantioselectivity than AuNPs@DMP/VO/[H-G-C16]FeCl4 (aq) because of the smaller droplets have larger interface areas could adsorb more [H-G-C16]FeCl4 molecules and AuNPs. The findings open an avenue in the production of chiral Janus emulsions with catalytic effect and enantioselectivity, which would push forward future applications in selective catalysis and inorganic material synthesis.

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

Preparation of Poly (N-Isopropylacrylamide) -Poly (2-Ethyl-2-Oxazoline) and Their Self-Assembly Properties with Dicarboxylic Acid

This study reports the synthesis of copolymers that contain thermally responsive polymers, namely poly(N-isopropylacrylamide) (PNIPAM) and poly(2-ethyl-2-oxazoline) (PEOX), as well as biodegradable side groups that are water-soluble and capable of hydrogen bonding. The assay aims to produce heat-responsive PNIPAM and PEOX polymers with di-carboxylic acid (DCA) controlled structuring of the resulting pH-sensitive nano-structured polymers. These will be used as a template in the synthesis of inorganic materials. The study demonstrated the impact of pH, salt concentration, and temperature on the polymer/DCA. This fragment describes the functional groups of the thermosensitive polymers PNIPAM and PEOX. These polymers have carboxylic acid functional groups at both ends, are water soluble, and are capable of hydrogen bonding. The structure of these polymers can be recognized with small molecules of DCA in an aqueous solution at different pH, salt concentrations, and temperatures with H-bonds. Additionally, these polymers can be used as templates to synthesize hollow silica polymers. The synthesized monomers and polymers were structurally characterized using Fourier transform infrared spectrophotometer (FT-IR). The resulting structured polymers were identified by scanning electron microscopy and atomic force microscopy (SEM, AFM). UV-VIS spectrophotometer and Differential Scanning Calorimetry (DSC) were used to determine the Lower Critical Solution temperature of the polymers.

Unraveling the Synthesis of Positive Electrodes for Na-Ion Batteries by in Situ XRD

In situ diffraction studies can capture transient crystalline phases forming during chemical reactions. Whether the reaction is a chemical solid-state synthesis, or an electrochemical intercalation process within typical active compounds used as battery electrodes, proper sample environments allow nowadays to perform in situ diffraction experiments with high temporal and angular resolution even when using laboratory diffractometers. In both cases, the time-resolved nature of the experiments allows to obtain a greatly increased amount of information. For example, in the synthesis of inorganic materials, reactions often yield non-equilibrium kinetic byproducts instead of the thermodynamic equilibrium phase [1]. On the other hand, often stable compounds cannot be synthesized. To rationalize that, the competition between thermodynamics and kinetics occurring during the process need to be investigated in real time. Fully determining the reaction pathway is a key requirement to achieve the rational synthesis of target materials [2, 3]. In this presentation, recent unpublished examples will be reported from our work applying in situ XRD to understand the synthesis of cathode materials for Na-ion batteries. We focus on layered materials and on the relationship between P2 and P3 polymorphs in series of samples of the same composition NaxDyNizMn1-y-zO2 (where D is a substitutional element and Ni and Mn are in divalent and tetravalent oxidation state, respectively) and highlight the transformation of a phase into the other during calcination. Moreover, we provide electrochemical and morphologic characterizations of the target compounds, aiming to answer the question: does one or the other polymorph offer better performances? The charge compensation mechanism, involving Ni redox but also O redox, will be reported. Finally, the presentation will also briefly cover the investigation of novel solid-state synthesis routes for cathodes of the fluorophosphate Na3V2(PO4)2F3-xOx family.

Mechanoluminescence and Its Recent Applications

Abstract: Mechanoluminescence (ML) refers to the light emission from numerous organic and inorganic materials upon mechanical stimulation. As a brand new class of smart materials, mechanoluminescent materials are broadly relevant for fluorescence switches, mechanic sensors, protection papers, optoelectronic gadgets, record garages, and many others. In the past few years, systematic investigations have been executed, resulting in the production of an expanding number of mechanoluminescent substances. In this evaluation, the late developments in natural organic mechanoluminescent substances are summarized, along with mechano-fluorescent and triboluminescent results from conjugated small molecules. Organic-inorganic Nanocomposite substances have attracted great interest, given that they cover an extensive variety of properties by combining contrasting properties and/or developing novel residences. Composites’ useful properties may also be manipulated by stimuli, such as mildness, pH, magnetic or electric discipline mechanical pressure, and heat. While the stimuli have the ability to control a wide range of material properties, they can also be transferred and manipulated by stimuli, such as mildness, pH, magnetic or electric discipline, mechanical pressure, and heat. While the stimuli have the ability to control a wide range of material properties, they can also be transferred to a device with an ON-OFF machine: a transfer. A switchable cloth must showcase the belongings; this is quite distinctive upon stimulation (i.e., ON and off states) with the reversible mechanism. Switchable materials have been actively studied in order to maximize the difference between ON and stale states by editing the shape and composition of Nanocomposites, as well as on stimuli to control the response time. Switchable substances find their programs in numerous fields consisting of catalysts, sensors, photodetectors, reminiscence devices, and drug delivery systems. In this overview, we talk about the most current design innovations and synthesis of natural and inorganic nanoscale materials, whose properties can be controlled in reaction to a particular stimulus.

Syntheses characterization, and photocatalytic property of an organic-inorganic compound obtained by bromine salt and the β-Mo8O26 anion

According to the structure/property characteristics of polyacids, the design and synthesis of new inorganic organic hybrid materials based on POMs is of great significance for the rapid and effective removal of pollutants in wastewater. In addition, many researchers have synthesized a large number of POMs-based inorganic organic hybrid compounds with organic cations as templates. Based on the above background, in this article, a new supramolecular compound named o-[C20H18N2O4][Mo8O26]0.5.H2O was obtained through hydrothermal method by reacting 1, 2-bis[4-nitrile-pyridine)-N-methylene]phenyldibromide (L1) with (NH4)6Mo7O24.4H2O. The compound was characterized by IR, PXRD, and single crystal X-ray analysis. Single crystal X-ray analysis shows that the compound consists of a [β-Mo8O26] 2–, a dicarboxylic acid chelating ligand and a water molecule. This compound exhibits good catalytic activity towards MB. The degradation rate of MB by photocatalysis of compound 1 was 75.2% . The novelty of this work lies in the synthesis of a cyano-hydrolyzed ligand and the high efficiency of MB degradation of the compound.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data. This specialized AI model, named MatChat, focuses on predicting inorganic material synthesis pathways. MatChat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although MatChat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. MatChat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials.

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO3). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl2 and Na2TiO3) that produce BaTiO3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis.

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.

GAS DISCHARGES AS A TOOL FOR CLEANING GAS AND SOLUTION MEDIUMS AND SYNTHESIS OF INORGANIC MATERIALS

The article provides an overview of the results of research of the purification processes of gas and solution media from organic (tetrachloromethane, 1,4-dichlorobenzene and 2,4-di-chlorophenol, etc.) and inorganic (Cr(VI), Mn(VII), Cu2+, Fe2+3+, Zn2+, Cd2+, Ni2+, etc.) substances, as well as the synthesis of oxide materials based on the above metals under the action of a dielectric barrier discharge (DBD) and atmospheric pressure DC discharge. The work was carried out in joint research conducted at the Departments of Industrial Ecology and Microelectronic Devices and Materials of the ISUCT over the past 10 years. As a result of the research, the kinetic regularities of decomposition of the above organic substances were revealed, i.e. the rate and effective rate constants of the decomposition reactions and their dependences on the DBR parameters (power, gas and solution flow rates) were determined. On the basis of these data, the energy efficiency of decomposition processes was found. The main decomposition products and their dependence on the discharge parameters are determined. Probable mechanisms of the ongoing processes were proposed. It is shown that the action of a direct current discharge on aqueous solutions leads to the reduction of Cr(VI) and Mn(VII) in the composition of Cr2O72- and MnO4- to Cr3+ and Mn2+ ions. When the same discharge acts on solutions of salts containing Cu2+, Fe2+3+, Zn2+, Cd2+, Ni2+ ions, the formation of colloidal solutions of hydroxo compounds of these ions is observed. The sizes of resulting particles, their phase and chemical composition were determined by DLS, SEM, EDX, TGA, XRD, DSC methods. It is shown that the calcination of the obtained particles leads to the formation of crystalline oxides of the corresponding metals. Thus, the action of the discharge ensures the purification of aqueous solutions from heavy metals with the formation of oxide materials of the nanoscale range, which have semiconductor and catalytic properties. For citation: Guschin A.A., Grinevich V.I., Kvitkova E.Yu., Gusev G.I., Shutov D.A., Ivanov A.N., Manukyan A.S., Rybkin V.V. Gas discharges as a tool for cleaning gas and solution mediums and synthesis of inorganic materials. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2023. V. 66. N 7. P. 120-131. DOI: 10.6060/ivkkt.20236607. 6835j.

Microwave assisted green synthesis and characterization of new phase inorganic ion-exchange materials: bismuth tungstate and stannic arsenate

Microwave assisted green synthesis and characterization of new phase inorganic ion-exchange materials: bismuth tungstate and stannic arsenate

Simple Preparation Method of Silica Core–Shell Spheres for HPLC in Undergraduate Chemistry

High-performance liquid chromatography (HPLC) experiments are an important part of the undergraduate analytical instrumentation laboratory courses and usually use commercial C18 silica-packed reversed-phase columns. In this experiment, a simple method of preparing SiO2 core–shell packed columns was developed to enable students to master the whole process of HPLC experiment from stationary phase preparation and column packing to separation experiments, just as they do for gas chromatography. Nonporous SiO2 particles with a particle size of approximately 2.2 μm were prepared by a three-step dilution reaction using the sol–gel method. The product yield was 92.1%. Monodisperse and spherical SiO2 core–shell particles (SiO2@dSiO2) with a particle size of about 2.4 μm were prepared by a one-step hydrothermal method using the prepared SiO2 spheres as the core. The product yield was 83.3%. The prepared SiO2@dSiO2 particles were derivatized with n-octadecyldimethylchlorosilane and then packed into a stainless steel chromatography column. A high column efficiency of 83,188 theoretical plates per meter was obtained for fluorene in reversed-phase mode for the separation of a mixture of polycyclic aromatic hydrocarbons. The column efficiency obtained with this method is the same as that of a commercial column packed with fully porous particles of the same particle size. The SiO2 core–shell stationary phase preparation method developed in this experiment has the advantages of simple process, high yield, and good repeatability. The experiment can be designed to be completed over a period of 1–3 weeks. Inorganic material synthesis is another important aspect of this experiment.

Exploring the Role of Miniemulsion Nanodroplet Confinement on the Crystallization of MoO3: Morphology Control and Insight on Crystal Formation by In Situ Time-Resolved SAXS/WAXS

Enclosed nanoscale volumes, i.e., confined spaces, represent a fascinating playground for the controlled synthesis of inorganic materials, albeit their role in determining the synthetic outcome is currently not fully understood. Herein, we address the synthesis of MoO3 nano- and microrods with hexagonal section in inverse miniemulsion droplets and batch conditions, evaluating the effects of spatial confinement offered by miniemulsion droplets on their crystallization. Several synthetic parameters were systematically screened and their effect on the crystal structure of h-MoO3, as well as on its size, size distribution and morphology, were investigated. Moreover, a direct insight on the crystallization pathway of MoO3 in both synthetic conditions and as a function of synthetic parameters was provided by an in situ time-resolved SAXS/WAXS study, that confirmed the role of miniemulsion confined space in altering the stepwise process of the formation of h-MoO3.

Developing Quantitative Nondestructive Characterization of Nanomaterials: A Case Study on Sequential Infiltration Synthesis of Block Copolymers.

The sequential infiltration synthesis (SIS) of inorganic materials in nanostructured block copolymer templates has rapidly progressed in the last few years to develop functional nanomaterials with controllable properties. To assist this rapid evolution, expanding the capabilities of nondestructive methods for quantitative characterization of the materials properties is required. In this paper, we characterize the SIS process on three model polymers with different infiltration profiles through ex situ quantification by reference-free grazing incidence X-ray fluorescence. More qualitative depth distribution results were validated by means of X-ray photoelectron spectroscopy and scanning transmission electron microscopy combined with energy-dispersive X-ray spectroscopy.

On stars and spikes: Resolving the skeletal morphology of planktonic Acantharia using synchrotron X-ray nanotomography and deep learning image segmentation.

Acantharia (Acantharea) are wide-spread marine protozoa, presenting one of the rare examples of strontium sulfate mineralization in the biosphere. Their endoskeletons consist of 20 spicules arranged according to a unique geometric pattern named Müller's principle. Given the diverse mineral architecture of the Acantharia class, we set out to examine the complex three-dimensional skeletal morphology at the nanometer scale using synchrotron X-ray nanotomography, followed by image segmentation based on deep learning methods. The present study focuses on how the spicules emanate from the robust central junction in the orders Symphyacanthida and Arthracanthida, the geometry of lateral spicule wings as well as pockets of interspicular space, which may be involved in cell compartmentalization. Through these morphometric studies, we observed subtle deviations from the previously described spatial arrangement of the spicules. According to our data, spicule shapes are adjusted in opposite spicules as to accommodate the overall spicule arrangement. In all types examined, previously unknown interspicular interstices were found in areas where radial spicules meet, which could have implications for the crystal growth mechanism and overall endoskeletal integrity. A deeper understanding of the spiculogenesis in Acantharia can provide biomimetic routes towards complex inorganic shapes. STATEMENT OF SIGNIFICANCE: Morphogenesis, the origin and control of shape, provides an avenue towards tailored inorganic materials. In this work, we explored the intricate skeletal organization of planktonic Acantharia, which are amongst the few strontium sulfate biomineralizing organisms in nature. By using nanoscale X-ray imaging and deep learning image segmentation, we found deviations from previously described geometric patterns and undiscovered skeletal features. The bio-inspired synthesis of inorganic materials with complex shape has important ramifications for solid-state chemistry and nanotechnology.

Synthesis and application of inorganic oxide materials for oil hydroprocessing catalysts

Synthesis and application of inorganic oxide materials for oil hydroprocessing catalysts

Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction

Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by heuristics and chemists’ prior knowledge and experience, built upon experimental trial-and-error that can be both time and resource consuming. Recent developments in natural language processing have enabled large-scale text mining of scientific literature, providing open-source databases of synthesis information on realized compounds, material precursors, and reaction conditions (temperatures, times). We employ supervised classification machine learning (ML) models to distinguish between solid-state, sol–gel, and solution (hydrothermal, precipitation) synthesis routes based on specified reaction target material and/or precursor materials. We demonstrate regression ML models that are able to predict suitable temperatures and times for the crucial inorganic synthesis steps of calcination and sintering given the reaction target and precursor materials. We contrast this regression-based condition modeling with a conditional variational autoencoder neural network that can generate appropriate distributions for the synthesis conditions of interest. We evaluate model interpretability using the Shapley additive explanations approach to gain insight into factors influencing suitability of synthesis route and reaction conditions. We find that the aforementioned models are capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route that are present in the inorganic synthesis space. Moreover, they generalize well to unseen chemical entities, outperform common heuristics in the field, and show promise for predicting appropriate reaction routes and conditions for previously unsynthesized compounds of interest.

Supercritical Hydrothermal Reactions for Material Synthesis

Abstract Since the early 1990s, Adschiri and his colleagues have performed research on the use of supercritical water in diverse applications, including heavy oil reforming, waste polymer decomposition and chemical raw materials recovery, cellulose hydrolysis and sugar recovery, lignin decomposition and chemical raw materials recovery, and nanoparticle synthesis. Regarding inorganic materials synthesis, they invented supercritical hydrothermal synthesis for the continuous flow synthesis of metal oxide nanoparticles, which has already found worldwide industrial applications. They proposed a two-fluid mixing flow system where an aqueous metal salt solution is mixed with supercritical water to heat the solution to the supercritical state within a very short time, that is not attainable by conventional reactor systems. Organic-inorganic hybrid nanoparticles synthesis was demonstrated in the supercritical state, realizing homogeneous phase formation of organic molecules at high loadings. By optimizing the reaction conditions, small facet-controlled nanoparticles with narrow particle size distribution can be obtained. Organic modification of nanoparticles is shown to be effective for fabricating highly concentrated nanohybrid polymers or nano inks. Furthermore, these synthesized facet-controlled nanoparticles show high catalytic activities. Indeed, it is demonstrated that steam reforming of heavy oils or biomass wastes (black liquor) can occur in water even at low temperatures.