Uranyl bearing hybrid materials: Synthesis, speciation, and solid state structures
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Silicon-based anodes for lithium-ion batteries: Effectiveness of materials synthesis and electrode preparation
Lithium-ion batteries are widely used throughout the world for portable electronic devices and mobile phones and show great potential for more demanding applications like electric vehicles. Unfortunately, lithium-ion batteries still lack the required level of energy storage to completely meet the demands of such applications as electric vehicles. Among advanced materials being studied, silicon nanoparticles have demonstrated great potential as an anode material to replace the commonly used graphite. Silicon has been shown to have a high theoretical gravimetric capacity, approximately 4200 mA h/g, compared to only 372 mA h/g for graphite. Though silicon nanoparticles have remarkably high capacity, they suffer from rapid degradation with each cycle due to electrode volume expansion of approximately 400% during lithiation, placing a large strain on the material. With each cycle that strain creates cracks in the electrode particles and causes them to break down into smaller particles, which create void spaces between the particles and lead to poor contact as reflected in poor conductivity. In this review, we discuss exciting new research on silicon-based anodes conducted during the past couple of years. Besides stressing the importance of well-designed nanostructures of Si, we focus on optimization of the Si electrode and resulting performance enhancement by properly selecting binders and synergistically integrating them with various carbon materials during electrode design and fabrication. Importantly, although each improvement strategy has its own advantage, appropriate combination of them will yield much higher anode performance. We summarize the core issues in developing the silicon anode and effective strategies in yielding more promising results. (C) 2016 Elsevier Ltd. All rights reserved.
Casimir, Anix↗
Text-mined dataset of inorganic materials synthesis recipes
Abstract Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Effects of Dopant Metal Variation and Material Synthesis Method on the Material Properties of Mixed Metal Ferrites in Yttria Stabilized Zirconia for Solar Thermochemical Fuel Production
Mixed metal ferrites have shown much promise in two-step solar-thermochemical fuel production. Previous work has typically focused on evaluating a particular metal ferrite produced by a particular synthesis process, which makes comparisons between studies performed by independent researchers difficult. A comparative study was undertaken to explore the effects different synthesis methods have on the performance of a particular material during redox cycling using thermogravimetry. This study revealed that materials made via wet chemistry methods and extended periods of high temperature calcination yield better redox performance. Differences in redox performance between materials made via wet chemistry methods were minimal and these demonstrated much better performance than those synthesized via the solid state method. Subsequently, various metal ferrite samples (NiFe 2 O 4 , MgFe 2 O 4 , CoFe 2 O 4 , and MnFe 2 O 4 ) in yttria stabilized zirconia (8YSZ) were synthesized via coprecipitation and tested to determine the most promising metal ferrite combination. It was determined that 10 wt.% CoFe 2 O 4 in 8YSZ produced the highest and most consistent yields of O 2 and CO. By testing the effects of synthesis methods and dopants in a consistent fashion, those aspects of ferrite preparation which are most significant can be revealed. More importantly, these insights can guide future efforts in developing the next generation of thermochemical fuel production materials.
Leonard, Jeffrey↗
Combinatorial and High-Throughput Materials Synthesis to Advanced Nuclear Materials Discovery
Poster for end of LDRD project
36 MATERIALS SCIENCE↗
Optimal thermodynamic conditions to minimize kinetic by-products in aqueous materials synthesis
Abstract Phase diagrams offer substantial predictive power for materials synthesis by identifying the stability regions of target phases. However, thermodynamic phase diagrams do not offer explicit information regarding the kinetic competitiveness of undesired by-product phases. Here we propose a quantitative and computable thermodynamic metric to identify synthesis conditions under which the propensity to form kinetically competing by-products is minimized. We hypothesize that thermodynamic competition is minimized when the difference in free energy between a target phase and the minimal energy of all other competing phases is maximized. We validate this hypothesis for aqueous materials synthesis through two empirical approaches: first, by analysing 331 aqueous synthesis recipes text-mined from the literature; and second, by systematic experimental synthesis of LiIn(IO 3 ) 4 and LiFePO 4 across a wide range of aqueous electrochemical conditions. Our results show that even for synthesis conditions that are within the stability region of a thermodynamic Pourbaix diagram, phase-pure synthesis occurs only when thermodynamic competition with undesired phases is minimized.
Wang, Zheren (ORCID:0000000227429451)↗
Autonomous and dynamic precursor selection for solid-state materials synthesis
Abstract Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS 3 ) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS 3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS 3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.
36 MATERIALS SCIENCE↗
Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-Throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework's capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Lastly, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.
36 MATERIALS SCIENCE↗
Semi-supervised machine-learning classification of materials synthesis procedures
Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis. However, most content in the scientific literature is locked-up in written natural language, which is difficult to parse into databases using explicitly hard-coded classification rules. In this work, we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language. Without any human input, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps, such as “grinding” and “heating”, “dissolving” and “centrifuging”, etc. Guided by a modest amount of annotation, a random forest classifier can then associate these steps with different categories of materials synthesis, such as solid-state or hydrothermal synthesis. Finally, we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures. Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized, machine-readable database.
36 MATERIALS SCIENCE↗
MPA-11: Materials Synthesis and Integrated Devices; Overview of an Applied Energy Group
Our mission is to provide innovative and creative chemical synthesis and materials science solutions to solve materials problems across the LANL missions. Our group conducts basic and applied research in areas related to energy security as well as problems relevant to the Weapons Program.
36 MATERIALS SCIENCE↗
Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature
The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.
36 MATERIALS SCIENCE↗
Manufacturing Demonstration Facility: Low Temperature Materials Synthesis
The Manufacturing Demonstration Facility (MDF) low temperature materials synthesis project was established to demonstrate a scalable and sustainable process to produce nanoparticles (NPs) for advanced manufacturing. Previous methods to chemically synthesize NPs typically required expensive, high-purity inorganic chemical reagents, organic solvents and high temperatures. These processes were typically applied at small laboratory scales at yields sufficient for NP characterization, but insufficient to support roll-to-roll processing efforts or device fabrication. The new NanoFermentation processes described here operated at a low temperature (~60 C) in low-cost, aqueous media using bacteria that produce extracellular NPs with controlled size and elemental stoichiometry. Up-scaling activities successfully demonstrated high NP yields and quality in a 900-L pilot-scale reactor, establishing this NanoFermentation process as a competitive biomanufacturing strategy to produce NPs for advanced manufacturing of power electronics, solid-state lighting and sensors.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Perspective: Toward “synthesis by design”: Exploring atomic correlations during inorganic materials synthesis
Synthesis of inorganic extended solids is a critical starting point from which real-world functional materials and their consequent technologies originate. However, unlike the rich mechanistic foundation of organic synthesis, with its underlying rules of assembly (e.g., functional groups and their reactivities), the synthesis of inorganic materials lacks an underpinning of such robust organizing principles. In the latter case, any such rules must account for the diversity of chemical species and bonding motifs inherent to inorganic materials and the potential impact of mass transport on kinetics, among other considerations. Without such assembly rules, there is less understanding, less predictive power, and ultimately less control of properties. Despite such hurdles, developing a mechanistic understanding for synthesis of inorganic extended solids would dramatically impact the range of new material discoveries and resulting new functionalities, warranting a broad call to explore what is possible. Here we discuss our recent approaches toward a mechanistic framework for the synthesis of bulk inorganic extended solids, in which either embryonic atomic correlations or fully developed phases in solutions or melts can be identified and tracked during product selection and crystallization. The approach hinges on the application of high-energy x-rays, with their penetrating power and large Q-range, to explore reaction pathways in situ. We illustrate this process using two examples: directed assembly of Zr clusters in aqueous solution and total phase awareness during crystallization from K–Cu–S melts. These examples provide a glimpse of what we see as a larger vision, in which large scale simulations, data-driven science, and in situ studies of atomic correlations combine to accelerate materials discovery and synthesis, based on the assembly of well-defined, prenucleated atomic correlations.
36 MATERIALS SCIENCE↗
Ultrafast materials synthesis and manufacturing techniques for emerging energy and environmental applications
Energy and environmental issues have attracted increasing attention globally, where sustainability and low-carbon emissions are seriously considered and widely accepted by government officials. In response to this situation, the development of renewable energy and environmental technologies is urgently needed to complement the usage of traditional fossil fuels. While a big part of advancement in these technologies relies on materials innovations, new materials discovery is limited by sluggish conventional materials synthesis methods, greatly hindering the advancement of related technologies. To address this issue, this review introduces and comprehensively summarizes emerging ultrafast materials synthesis methods that could synthesize materials in times as short as nanoseconds, significantly improving research efficiency. We discuss the unique advantages of these methods, followed by how they benefit individual applications for renewable energy and the environment. We also highlight the scalability of ultrafast manufacturing towards their potential industrial utilization. Finally, we provide our perspectives on challenges and opportunities for the future development of ultrafast synthesis and manufacturing technologies. Here, we anticipate that fertile opportunities exist not only for energy and the environment but also for many other applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Material synthesis and hydrogen storage of palladium-rhodium alloy.
Pd and Pd alloys are candidate material systems for Tr or H storage. We have actively engaged in material synthesis and studied the material science of hydrogen storage for Pd-Rh alloys. In collaboration with UC Davis, we successfully developed/optimized a supersonic gas atomization system, including its processing parameters, for Pd-Rh-based alloy powders. This optimized system and processing enable us to produce {le} 50-{mu}m powders with suitable metallurgical properties for H-storage R&D. In addition, we studied hydrogen absorption-desorption pressure-composition-temperature (PCT) behavior using these gas-atomized Pd-Rh alloy powders. The study shows that the pressure-composition-temperature (PCT) behavior of Pd-Rh alloys is strongly influenced by its metallurgy. The plateau pressure, slope, and H/metal capacity are highly dependent on alloy composition and its chemical distribution. For the gas-atomized Pd-10 wt% Rh, the absorption plateau pressure is relatively high and consistent. However, the absorption-desorption PCT exhibits a significant hysteresis loop that is not seen from the 30-nm nanopowders produced by chemical precipitation. In addition, we observed that the presence of hydrogen introduces strong lattice strain, plastic deformation, and dislocation networking that lead to material hardening, lattice distortions, and volume expansion. The above observations suggest that the H-induced dislocation networking is responsible for the hysteresis loop seen in the current atomized Pd-10 wt% Rh powders. This conclusion is consistent with the hypothesis suggested by Flanagan and others (Ref 1) that plastic deformation or dislocations control the hysteresis loop.
08 HYDROGEN↗
Modernist materials synthesis: Finding thermodynamic shortcuts with hyperdimensional chemistry
Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can “spectator” elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis? By observing reaction pathways as they happen (in situ) and calculating their energetics using modern computational thermodynamics, we observe transient, crystalline intermediates that suggest that many reactions attain a local thermodynamic equilibrium dictated by local chemical potentials far before achieving a global equilibrium set by the average composition. Furthermore, using this knowledge, one can thermodynamically “shortcut” unfavorable intermediates by including additional elements beyond those of the desired target, providing access to a greater number of intermediates with advantageous energetics and selective phase nucleation. Ultimately, data-driven modeling that unites first-principles approaches with experimental insights will refine the accuracy of emerging predictive retrosynthetic models for complex materials synthesis.
36 MATERIALS SCIENCE↗
Materials synthesis, neutron powder diffraction, and first-principles calculations of (Mo x Sc 1–x ) 2 AlC i–MAX phase used as parent material for MXene derivation
Research on low-dimensional materials has increased drastically in the last decade, with the discovery of two-dimensional transition metal carbides and nitrides (MXenes) produced by atom-selective chemical etching of laminated parent M n+1 AX n (MAX) phases. In this study, we apply density functional theory and subsequent materials synthesis and analysis to explore the phase stability and Mo/Sc intermixing on the M site in the chemically ordered quaternary i-MAX phase (Mo x Sc 1-x ) 2 AlC. Transmission electron microscopy confirms the theoretical predictions of preferential in-plane ordering of Mo and Sc, with the highest crystal quality obtained for the ideal Mo:Sc ratio of 2:1 (predicted as the most stable), as well as a retained i-MAX structure even for an increased relative Sc content, with Sc partially occupying Mo sites. The results are supported by refined neutron diffraction data, which show space group C2/c (no. 15), and a C occupancy of 1. Subsequent chemical etching produces MXene for x=0.66, while for x=0.33 and 0.5 no MXene is observed. These results demonstrate that a precise control of the i-MAX composition is crucial for derivation of MXene, with a MXene quality optimized for a Mo:Sc ratio of 2:1 with minimal intermixing between Mo and Sc.
36 MATERIALS SCIENCE↗
Benchmarking large language models for materials synthesis: The case of atomic layer deposition
In this work, we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and, in particular, in the field of atomic layer deposition, a thin film growth technique used in energy applications and microelectronics. Our benchmark comprises questions with a level of difficulty ranging from the graduate level to domain expert current with the state of the art in the field. Human experts reviewed the questions along the criteria of difficulty and specificity, and the model responses along four different criteria: overall quality, specificity, relevance, and accuracy. We ran this benchmark on an instance of OpenAI’s GPT-4o. The responses from the model received a composite quality score of 3.7 on a 1–5 scale, consistent with a passing grade. However, 36% of the questions received at least one below average score. An in-depth analysis of the responses identified at least five instances of suspected hallucination. Finally, we observed statistically significant correlations between the difficulty of the question and the quality of the response, the difficulty of the question and the relevance of the response, the specificity of the question, and the accuracy of the response as graded by the human experts. Furthermore, this emphasizes the need to evaluate LLMs across multiple criteria beyond difficulty or accuracy.
Artificial intelligence↗