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Open Access Publications from the University of California

Scholarly Works (13 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2023)

Advancements in therapeutics have seen mortality decrease for a variety of cancers. However, pancreatic cancer continues to develop resistance and progress to late-stage metastasis in patients. Research continues to investigate ways to counter these mechanisms set up by the tumor. Previous work done in the lab found characterized a therapeutic resistant population finding thousands of novel genes to be possible key regulators. Studies presented here focus on the novel pancreatic cancer regulator Prap1 and its function. Prap1 inhibition saw functional impact on the growth of pancreatic cancer across various murine and human in vitro and in vivo models. Characterization of this population identified DNA damage repair and ROS metabolism to be key functions downstream of Prap1. A possible feedback loop is activated in Prap1 inhibition as decreased ROS metabolism leads to higher amounts of DNA damage that can’t be repaired. These studies elucidate a unique role that Prap1 has within pancreatic cancer. Follow up studies were also performed on Celsr1, Celsr2, and Pear1in an inducible shRNA inhibition model on autochthonous in vivo tumors. Dox chow was found to be the most consistent and least taxing method on the mouse for induction of the vector. Studies here further compound that Celsr1 and Celsr2 are viable pancreatic cancer targets as shRNA positive cells were selected against in the tumor. These studies identify possible clinical targets in pancreatic cancer that can help overcome pancreatic cancer’s therapy resistance.

    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2023)

    Synthesis is the major bottleneck in the computational design of new inorganic materials for technological applications, including rechargeable batteries, catalysis, sensing, photovoltaics, and more. Unlike molecular reactions, where individual bond-breaking or formation steps can be explicitly followed, reactions involving solid inorganic materials happen at great spatial scales where atomic control cannot be achieved. This is especially true for solid-state synthesis, which involves the reaction of bulk solid powder precursors at elevated temperatures (i.e., the ceramic method). Solid-state synthesis is an especially attractive approach given its simplicity and scalability to bulk chemical manufacturing. Recent work has focused on the experimental characterization of reaction pathways in solid-state synthesis to elucidate theoretical principles determining the performance of the synthesis reactions. These works have shown that powder reactions often involve a significant number of intermediates, including crystalline phases that sometimes persist as undesirable impurity products. Meanwhile, databases of crystalline materials and their properties have rapidly grown with the development of high-throughput density functional theory calculation workflows. These databases enable the study of phase equilibria, reaction energetics, and predicted products in solid-state reactions.

    In this dissertation, we address the current deficit of theoretical models for predicting reaction pathways in solid-state synthesis, taking advantage of the aforementioned breadth of computed thermodynamic data. We focus on the development of both forward and inverse prediction models, with the goal of identifying synthesis reactions that selectively produce desired target phases with minimal competing intermediates. The central contribution of this work is the development of a reaction network model and methodology for predicting reaction pathways and key intermediate phases encountered in solid-state synthesis. The solid-state reaction network is a large graph-based network created from a mixture of computed and experimental free energies, supplemented by a machine-learned descriptor for Gibbs free energy. Using standard graph pathfinding approaches, we perform forward prediction, suggesting a ranked set of likely reaction pathways in the synthesis of four inorganic materials (YMnO3, Y2Mn2O7, Fe2SiS4, and YBa2Cu3O6.5). We compare the network-predicted pathways to experimental results characterized in the literature and find that the reaction network model captures key intermediates and reproduces the experimental reaction pathways for several targets.

    We then address the current challenge of predicting the selectivity of solid-state reactions. In a detailed study of the synthesis of the Y2Mn2O7 pyrochlore, we show that assisted metathesis reactions with precursors 3 A2CO3 + 2 YCl3 + Mn2O3 selectively produce Y2Mn2O7, but only when sodium is used as the alkali metal (A=Na). We explain the unique selectivity of the Na-based reaction from observation of the formation of an NaxMnO2 intermediate with in situ synchrotron X-ray diffraction. It is the stability of this intermediate into high oxygen chemical potential ranges that facilitates direct reaction with YOCl intermediate to produce Y2Mn2O7. Using this observation, we develop a selectivity metric related to the change of chemical potentials of the precursors that rationalizes the unique selectivity of the NaxMnO2 intermediate due to its direct connection in chemical potential space with Y2Mn2O7. We improve upon this idea further and suggest two reaction selectivity metrics (primary and secondary competition) measuring the degree of competitiveness of impurity reactions acquired from the solid-state reaction network. We validate the use of these metrics by applying them to reactions previously documented in the literature, as well as for planning our own experiments synthesizing BaTiO3 from eleven different precursor combinations spanning a range of chemistries, free energies, and selectivities. Our work culminates in the development of an inorganic synthesis planning workflow that can be used to suggest precursors that optimally yield a desired target.

    Along the way, we devise a possible explanation for related experiments that produce two metastable polymorphs: orthorhombic YMnO3 and layered α-NaFeO2. Our hypothesis centers around the fact that metastable polymorphs may be stabilized in certain environmental conditions due to differences in defect accommodation between the nominally stable and metastable polymorphs. For both YMnO3 and NaFeO2, we show, with charged point defect density functional theory calculations, that the metastable polymorphs can be targeted in oxygen-rich environments. Altogether, the application of theoretical concepts and predictive models developed herein will greatly enhance both human-led and machine-driven synthesis exploration efforts, reducing the synthesis bottleneck.

      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2021)
      Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable "synthesis by design" in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO3, Y2Mn2O7, Fe2SiS4, and YBa2Cu3O6.5. The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials.
        Cover page: A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2023)
        Abstract: 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. 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. Graphical abstract: [Figure not available: see fulltext.]TOC Diagram: Schematic representation of the energy bandgaps, photogenerated charge separation, and photocatalytic activities improvement mechanism over g-C3N4/SmFeO3 nanosheets Z-scheme based nanocomposites.
          Cover page: Modernist materials synthesis: Finding thermodynamic shortcuts with hyperdimensional chemistryCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2021)
          In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.
            Cover page: A framework for quantifying uncertainty in DFT energy corrections
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2022)
            NaTaO3 is a stable and wide bandgap n-type semiconductor material with many different applications. Here, a flux-mediated synthesis method is presented for NaTaO3 resulting in highly distinctive, substrate covering shapes via precursor chemistry variation at comparatively low temperatures. It is found that the microstructure of the resulting NaTaO3 films can be varied from nanocubes to smooth thin films. These shapes and surface chemistries can be correlated by employing density functional theory calculations and surface sensitive X-ray photoemission spectroscopy. This study provides guidance on how to synthesize the material and tailor its shape and surface termination for different applications. Finally, as a proof of concept of one possible application, NaTaO3 is applied to perovskite solar cells as the electron transport layer, resulting in conversion efficiencies of >19%. This study provides a new strategy for designing ternary oxide thin films for renewable energy applications.
              Cover page: Shape‐Controlled NaTaO3 by Flux‐Mediated SynthesisCreative Commons 'BY-NC' version 4.0 license
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2021)
              Low-temperature synthesis routes are necessary for selectively synthesizing many metastable solid-state materials. Here, we identify a cooperative effect that starting materials have in lowering temperatures in solid-state metathesis reactions by studying the formation of yttrium manganese oxide. Previous studies have shown that YMnO3 can be synthesized by ternary metathesis with an alkali halide being produced as a secondary product. In this contribution, we show that by using alkaline earth metals instead of alkali metals, the polymorph selectivity of the reaction is changed, as orthorhombic YMnO3 forms at lower temperatures than the hexagonal polymorph. Reactions were studied using ex post facto synchrotron X-ray diffraction. These experiments reveal that reactions using alkaline earth manganese oxides as a starting material require high temperatures to progress. Reaction temperatures can be lowered from 700 to 550 °C while maintaining phase selectivity by reacting both MgMn2O4 and CaMn2O4 with YOCl in a cooperative "cometathesis"reaction. The nascent halide salts appear to improve the reaction kinetics. Since the onset temperature for YMnO3 formation falls 50 °C below the MgCl2-CaCl2 liquidus, the enhanced reactivity is consistent with the surface melting of a nasscent salt byproduct at the interfaces. Cometathesis routes have similar phase selectivity and temperature reduction in reactions that form TbMnO3, ErMnO3, and DyMnO3. Cometathesis lowers reaction temperatures while preserving the reaction selectivity of the end members, making it a valuable approach for synthesizing metastable targets.
              • 1 supplemental PDF
              Cover page: Lowering Ternary Oxide Synthesis Temperatures by Solid-State Cometathesis Reactions
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2023)
              To close the gap between the rates of computational screening and experimental realization of novel materials1,2, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.
                Cover page: An autonomous laboratory for the accelerated synthesis of novel materials
                • Article
                • Peer Reviewed
                LBL Publications (2023)
                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.
                  Cover page: Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials.
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2024)
                  The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, the A-Lab, with around 3500 samples synthesized over 1.5 years.
                    Creative Commons 'BY' version 4.0 license
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