Enabling Solar Fuels Technology With High Throughput Experimentation

In the sessions of Materials Genome Initiative (MGI) and Materials Informatics (MI) at recent international and domestic conferences, majority of research presentations are on material prediction using a combination of computational chemistry and machine learning. On the other hand, the contents incorporating high-throughput synthesis and evaluation experiments are only occasionally seen. It is also true that the number of samples that can be handled per hour is small compared to the former data science.However, if a series of experimental processes linked to synthesis, evaluation, and analysis is made user-friendly and high-throughput, it is possible to efficiently generate multi-condition and multi-component data in a unified experimental environment such as starting materials. As a result, even when creating a data set by extracting text from a paper, the data shortage can be supplemented with experimental data.Unlike thin films and polymers, powder synthesis is a heterogeneous reaction that requires the use of a wet process to perform high-throughput experiments. We have hitherto developed powder synthesis apparatus equipped with a micropump for solution dispensing, and high-throughput exploration system for powder and thin-film library based on electrostatic spray deposition consisted by combining multiple syringe pumps and a high-voltage power supply. By using these system, we have been exploring multi-component cathode materials for lithium ion battery and thermoelectric materials. In addition, recently, high-throughput experiments in a high-pressure environment of 200 MPa and 500ºC are possible.If the time required for physical property evaluation and data analysis is the same as before, even if only the synthesis based on experiments becomes high throughput, the attractiveness of MI will be weakened. Therefore, it is necessary to continue to develop jigs and software for efficient evaluation and analysis of huge libraries obtained in high-throughput experiments.In this presentation, we will introduce the exploration for A-site and B-site substitutes of perovskite-type CaMnO3, which is expected as an n-type thermoelectric material. In the preliminary search, it was confirmed that Ca1-x Bi x Mn1-y Ni y O3 contributes to the improvement of thermoelectric power by substituting elements at each site. As the next step, the amount of Bi substitution was determined to be within 10% from the combination of high-throughput synthesis and high-throughput Seebeck coefficient measurement. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. We also developed software that automates Rietveld analysis, although we need basic knowledge to set up an initial structural model. As a result, we have made it easier to generate data sets for visualizing crystallographic data and physical property data at once. In the obtained Bi-substituted CaMnO3 powder, when Bi was 8% or less, the conductivity and power factor increased due to the increase in carrier concentration. At Bi8% and higher, PF reaches its limit value, which involves a large change in the MnO6 octahedral plane bond distances Mn-O1 (1) and Mn-O1 (2), that is, a significant increase in octahedral distortion. High-throughput experiments including the collection of crystallographic information through the development of these technologies are expected to be an effective tool for future machine learning.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering

The depletion of traditional fossil fuels and growing energy concerns have led to the development of efficient, carbon-neutral energy conversion technologies powered by renewable sources. Among these, green hydrogen production through electrochemical water splitting is widely considered a promising strategy for energy storage. The proton exchange membrane (PEM) system in various water electrolysis technologies is particularly noteworthy for its high current densities, excellent voltage efficiency, and minimal hydrogen cross-diffusion, bringing it closer to commercialization. However, the efficiency of water splitting is constrained by the slow oxygen evolution reactions (OER), the anodic reaction in the process. Artificial intelligence (AI) is revolutionizing numerous fields, including science and technology, by streamlining complex tasks. High-throughput experiments (HTE), aided by AI, provide an accelerated pathway for catalyst discovery, overcoming the challenges posed by traditional, time-consuming experimental methods. Metal oxide catalysts are attractive for OER due to their low-cost transition metals, high structural stability, and element substitutability. However, the reaction mechanisms of oxides, including reaction pathways, kinetic barriers, and surface reconstruction, highlight the need to explore their structure-performance relationships for deeper insights. The complex chemical space offers countless potential materials for synthesis, which cannot be addressed through traditional reliance on heuristics and extensive testing. Researchers are increasingly utilizing AI to predict material properties, enabling the design of new materials by navigating chemical space. Given the complexity of optimizing materials with multidimensional compositions (potentially exceeding > 200,000 candidates based on pyrochlores), novel experimental methods are essential for advancing effective OER catalysts. This work reveals the relationship among catalyst synthesis, reaction mechanisms, and performance (activity/stability) by establishing a combined AI-HTE platform, which includes a self-built database, a state-of-the-art AI model, and high-throughput (HT) experiments/ calculations. Meanwhile, for the first time, this work successfully implement AI to achieve green hydrogen production and drive renewable energy storage and transition.

Opportunities for catalyst discovery and development: Integrating surface science and theory with high throughput methods

High-throughput experimentation is a fast and efficient methodology for materials discovery, screening, and optimization. It has, therefore, garnered a lot of attention from battery researchers as a potential way to accelerate the development of next-generation Li-ion batteries and battery systems beyond Li-ion. However, its successful use in battery materials discovery has been extremely limited due to a variety of challenges including: (i) difficulties in synthesizing mg-scale samples that are comparable to bulk samples made by industrial methods, (ii) limited high-throughput characterization acting like a bottleneck even after high throughput synthesis is developed, and (iii) high cost of commercially available high-throughput instrumentation. Here, we develop high throughput techniques for solid-state electrolytes.First, a novel synthesis approach is used to make 64 mg-scale solid electrolytes simultaneously. The powder samples are prepared by a citrate sol-gel method using an automated dispensing system. These powders were formulated into sixty-four pellets using a home-made high-throughput pellet press die. The samples are then sintered at high temperature to yield the solid electrolytes. The proof of concept here is performed on the Li-La-Ti-O pseudo-ternary system that includes the perovskites Li3xLa2/3-xTiO3, considered candidates for solid-state electrolytes for high energy lithium-ion batteries. Given that vacancies and other defects play an essential role in ionic conductivity, the screening of complete phase diagrams has the potential to reveal phases with optimized defect contents and thus yield improved solid electrolytes.We have therefore developed a high-throughput characterization suite for solid electrolytes. First, we use a high throughput X-ray diffractometer capable of producing Rietveld quality patterns for mg-scale samples in 10 min such that 64 patterns can be obtained in under 12 h. This system is readily used to produce structural phase diagrams in ternary or even quaternary systems. Next, a high throughput electrochemical impedance spectrometer is used to map the ionic conductivities onto the phase diagrams. Finally, high throughput electrochemistry is used to determine the stability window of the electrolytes. All aspects of this workflow are demonstrated on the Li-La-Ti-O system. This now permits the synthesis and full characterization of approximately 192 compositions per week with one researcher only. This approach is well suited to determine the structure-property relations in complex composition spaces.The complete ternary Li-La-Ti phase diagram synthesized at 1150 °C with slow cooling was determined and will be presented in detail. The total (ionic + electronic) conductivity of the phase diagram was mapped by the electrochemical impedance spectroscopy. Quenching was performed to verify that transformations during slow cooling are moderate in this system. Consequences for the development of other solid electrolytes will also be discussed.

The development of an efficient photoanode remains the primary materials challenge in the establishment of a scalable technology for solar water splitting. The typical photoanode architecture consists of a semiconductor light absorber coated with a metal oxide that serves a combination of functions, including corrosion protection, electrocatalysis, light trapping, hole transport, and elimination of deleterious recombination sites. To provide a more efficient exploration of metal oxide coatings for a given light absorber, we introduce a high throughput methodology wherein a uniform BiVO4 library is coated with 858 unique metal oxides covering a range of metal oxide loadings and the full Ni-La-Co-Ce oxide or Ni-Fe-Co-Ce oxide psuedo-quaternary composition spaces. Photoelectrochemical characterization of each photoanode reveals that approximately one third of the coatings lower the photoanode performance while select combinations of metal oxide composition and loading provide up to a 14-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Particular Ce-rich coatings also exhibit an anti-reflection effect that further amplifies the performance, yielding a 20-fold enhancement in power conversion efficiency compared to bare BiVO4. By use of in situ optical spectroscopy and comparisons between the metal oxide coatings and their extrinsic optical and electrocatalytic properties, we present a suite of data-driven discoveries, including composition regions which form optimal interfaces with BiVO4 and photoanodes that are suitable for integration with a photocathode due to their excellent power conversion and solar transmission efficiencies. The initial high throughput discoveries were extended and validated through follow-up high throughput investigations and conventional photoelectrochemical measurements. The high throughput experimentation and informatics provides a powerful platform for both identifying the pertinent interfaces for further study and discovering high performance photoanodes for incorporation into efficient water splitting devices.

High Throughput Experimentation (HTE) has revolutionized the pharmaceutical industry over the last two decades, developing from a technology for screening compounds against biological targets, to now being used for everything from parallel synthesis of diverse libraries of analogues and optimization of catalytic systems and reaction conditions, to screening for optimal crystallization solvents and API polymorphs. Many pharmaceutical companies (and several academic institutions) have invested in HTE capabilities (often including automation) to support their drug discovery pipelines and fundamental research. With HTE workflows now being firmly established and many successful HTE groups to draw inspiration from, new facilities being set up today have options on where and how to build and integrate new high throughput and automation capabilities into their organizations. This chapter discusses and classifies different options for the build and integration of high throughput experimentation resources into existing chemistry departments.

The development of a scalable and highly reproducible in vitro tumor microenvironment (TME) platform still sheds light on new insights into cancer metastasis mechanisms and anticancer therapeutic strategies. Here, we present an all-in-one injection molded plastic array three-dimensional culture platform (All-in-One-IMPACT) that integrates vascularized tumor spheroids for highly reproducible, high-throughput experimentation. This device allows the formation of self-assembled cell spheroids on a chip by applying the hanging drop method to the cell culture channel. Then, when the hydrogel containing endothelial cells and fibroblasts is injected, the spheroid inside the droplet can be patterned together in three dimensions along the culture channel. In just two steps above, we can build a vascularized TME within a defined area. This process does not require specialized user skill and minimizes error-inducing steps, enabling both reproducibility and high throughput of the experiment. We have successfully demonstrated the process, from spheroid formation to tumor vascularization, using patient-derived cancer cells (PDCs) as well as various cancer cell lines. Furthermore, we performed combination therapies with Taxol (paclitaxel) and Avastin (bevacizumab), which are used in standard care for metastatic cancer. The All-in-One IMPACT is a powerful tool for establishing various anticancer treatment strategies through the development of a complex TME for use in high-throughput experiments.

High-throughput experimentation (HTE) in catalysis and materials science has become a mature technology since the late 1990s. The reasons for the success of HTE are the potential of increased R&D efficiency, which allow shortening of experimental periods, quick systematic insights, and finally faster time-to-market. Key elements of HTE technology are parallelization, fast sequential execution of experimental operations, and implementation of highly automated hardware and software components into HTE. This article contains sections titled: 1. Introduction 1.1. Origin of High-Throughput Experimentation in Catalysis and Materials Science 1.2. Definition and Scope of HTE 1.3. Workflow Development 1.4. Classification of Screening Approaches into Experimental Stages 1.5. Experimental Planning and Use of Complex Advanced Algorithms in HTE 2. Approaches for High-Throughput Synthesis of Heterogeneous Catalysts and Advanced Materials 3. HTE in Catalysis 3.1. HTE in Heterogeneous Catalysis 3.2. Environmental Catalysis 3.3. Petrochemical Applications 3.4. Homogeneous Catalysis 3.4.1. Homogeneous Catalyst Libraries 4. HTE in Materials Science 4.1. Materials for Optical Applications 4.2. Materials for Electronic Applications 4.3. Materials for Magnetic Applications 5. Conclusions and Perspectives

Data obtained from high throughput experiments often exhibit complex dependencies among features. These dependencies arise from various sources, including genetic correlation, batch effects, technical replicates, and shared biological pathways. Ignoring these dependencies can lead to inflated false discovery rate (FDR), reduced statistical power, and biased biological interpretations. Properly accounting for these dependencies is crucial for accurate detection of biological signals. We propose a new method called Analysis of Correlated Expressions (ACE) to compare the mean expression of features between two groups. ACE is based on a factor analytic model that accounts for dependence among features and also incorporates heterogeneity of variances between groups, a common feature of high throughput data. Furthermore, ACE does not require the data to be normally distributed. It is scalable and free of any unknown tuning parameters. Extensive simulation studies indicate that it is more powerful than many existing methods while controlling the FDR. Application of ACE to a microRNA dataset, a neuroblastoma gene expression dataset, and a Huntington's disease dataset resulted in some novel findings that were missed by existing methods.

To accelerate the discovery of new or improved homogeneous catalysts, research groups in industry and academia have embraced high throughput experimentation (HTE). Such methodologies consist of preparing and testing large numbers of catalysts in parallel. Homogeneous metal catalysts are very well-suited for HTE, since in many cases, they can be prepared by simply mixing a metal precursor and a ligand. However, an HTE program requires a large set of chemically diverse ligands, i.e. a ligand library. In this review, we describe five different approaches for assembling ligand libraries based on an extensive survey of the literature. These approaches are based on commercial ligands, modular ligands, mixtures of ligands, supramolecular ligands or ligands prepared via the tools of biochemistry.

Background: A fundamental limitation in the development of new therapies to prevent metastatic cancer is a lack of in vitro systems that can accurately recapitulate the steps of cancer cell metastasis. Currently, most assays for examining the steps of metastasis fail to incorporate the biophysical forces experienced by tumor cells due to blood flow, or are low throughput and therefore not amenable to drug screening and high throughput experimentation. Methods: We have developed a novel high throughput mesofluidic platform for assaying cell adhesion under flow in a 96-well format. This device functions like a cone and plate viscometer in each well by inducing shear stress on cells cultured in a standard 96-well plate. We validated the fluid flow and alignment of the device and studied the adhesion of cultured leukocytic monocytes (THP-1 cells) and multiple cancer cell lines (HCT116, MDA-MB-231 and MCF-7 cells) to purified extracellular matrix molecules (ECM), endothelial cells and immobilized platelets. All assays were carried out under flow (0.5 dynes/cm2 of shear stress) and static conditions. Results: Our studies show that adhesion assays performed under flow yield markedly different results from static adhesion assays, and are better at identifying both aggressive cancer cells lines and known pathways for circulating cancer and immune cell adhesion. Treatment of breast cancer cells with a small library of integrin inhibitors demonstrated that these compounds had minimal effect on cancer cell adhesion to endothelial cells under static conditions, whereas under shear conditions many of these compounds reduced adhesion of cancer cells. In addition, a static adhesion assay of breast cancer cells to various types of ECM showed higher adhesion of the less aggressive MCF-7 cell line in comparison to the more aggressive MDA-MB-231 cell line. In contrast, flow incorporating assays showed increased adhesion of the MDA-MB-231 in comparison to the MCF-7 cell line. Finally, we performed a high throughput screening experiment using a kinase inhibitor library with 80 compounds and found that the shear based assay yielded notably different results from a similar screen under static conditions for cancer cell adhesion to endothelial cells, immune cell adhesion to endothelial cells and cancer cell adhesion to platelets. Conclusions: Our studies show that adhesion assays performed under flow yield markedly different results from static adhesion assays, and are better at identifying both aggressive cancer cells lines and known pathways for circulating cancer and immune cell adhesion. Thus, this high-throughput screening platform may enable the development of novel compounds to inhibit cancer metastasis and facilitate the study of the systems level behavior of cancer-endothelium adhesion. Citation Format: Adrianne Shearer, Chris Spruell, Victoria Le, Mar Creixell, Seema Nandi, Aaron B. Baker. Mesofluidic platform for high throughput screening for inhibitors of metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-008. doi:10.1158/1538-7445.AM2015-LB-008

High throughput process development (HTPD) using liquid handling robotics and RoboColumns is an established methodology in downstream process development to screen chromatography resins and optimize process designs to meet target product profiles. However, HTPD is not yet widely available for use in viral clearance capability of the resin due to a variety of constraints. In the present study, a BSL-1-compatible, non-infectious MVM model, MVM-VLP, was tested for viral clearance assessment with various resin and membrane chromatography operations in a HTPD mode. To detect the MVM-VLP in the high throughput experiments, an electrochemiluminescence immunoassay (ECLIA) assay was developed with up to 5 logs of dynamic range. Storage time suitability of MVM-VLP solutions in various buffer matrices, in the presence or absence of a glycoprotein vaccine candidate, were assessed. Then, MVM-VLP and a test article monoclonal antibody (mAb) were used in a HTPD design that included commercially available ion exchange media chemistries, elucidating a wide variety of viral clearance ability at different operating conditions. The methodologies described herein have the potential to be a part of the process design stage in biologics manufacturing process development, which in turn can reduce risk associated with viral clearance validation studies.

Combinatorial experiments provide a means of generating large amounts of experimental data; however that does not necessarily lead to high throughput interpretation of that data. In this paper we provide a brief summary of how one can use informatics techniques to accelerate data interpretation from high throughput experiments. We provide examples from high throughput nanoindentation and diffraction experiments.

The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.