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High-throughput MBE with cyclical growth and in situ etch increases experimental throughput by approximately 6× and substrate utilization by >40×.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
Microfluidics has revolutionized high-throughput miniaturized biological assays. However, co-culture of mammalian cells and bacteria remains challenging in microfluidic systems due to incompatible growth requirements, limited spatial control, and the requirement for a mammalian cell adhesion matrix. Here, we present a microfluidic platform that generates multiphasic droplets which encapsulate mammalian and bacterial cells, enabling their direct and indirect co-culture. By combining photopolymerizable hydrogels with polymer phase separation, we generate core–shell droplets composed of a liquid and a hydrogel compartment. The hydrogel compartment supports mammalian cell adhesion and culture, while the liquid compartment sustains bacterial growth. We demonstrate two droplet architectures that allow physical bacteria–mammalian cell contacts or enforce complete physical separation, representing direct and indirect co-culture. Our multiphasic droplets are stable, customizable, able to sustain co-culture for over 24 hours, and compatible with fluorescence-based cell sorting technologies. Overall, our multiphasic droplet microfluidic platform provides a scalable and versatile tool for high-throughput co-culture and screening of host–microbe interactions.
Neuromuscular junctions (NMJs) are specialized synapses responsible for signal transduction between motor neurons (MNs) and skeletal muscle tissue. Malfunction at this site can result from developmental disorders, toxic environmental exposures, and neurodegenerative diseases leading to severe neurological dysfunction. Exploring these conditions in human or animal subjects is restricted by ethical concerns and confounding environmental factors. Therefore, in vitro NMJ models provide exciting opportunities for advancements in tissue engineering. In the last two decades, multiple NMJ prototypes and platforms have been reported, and each model system design is strongly tied to a specific application: exploring developmental physiology, disease modeling, or high-throughput screening. Directing the differentiation of stem cells into mature MNs and/or skeletal muscle for NMJ modeling has provided critical cues to recapitulate early-stage development. Patient-derived inducible pluripotent stem cells provide a personalized approach to investigating NMJ disease, especially when disease etiology cannot be resolved down to a specific gene mutation. Having reproducible NMJ culture replicates is useful for high-throughput screening to evaluate drug toxicity and determine the impact of environmental threat exposures. Cutting-edge bioengineering techniques have propelled this field forward with innovative microfabrication and design approaches allowing both two-dimensional and three-dimensional NMJ culture models. Many of these NMJ systems require further validation for broader application by regulatory agencies, pharmaceutical companies, and the general research community. In this summary, we present a comprehensive review on the current state-of-art research in NMJ models and discuss their ability to provide valuable insight into cell and tissue interactions.
Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.
BACKGROUND: Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS: In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS: We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.
Abstract Background The role of beneficial microbes in mitigating plant abiotic stress has received considerable attention. However, the lack of a reproducible and relatively high-throughput screen for microbial contributions to plant thermotolerance has greatly limited progress in this area, this slows the discovery of novel beneficial isolates and the processes by which they operate. Results We designed a rapid phenotyping method to assess the effects of bacteria on plant host thermotolerance. After testing multiple growth conditions, a hydroponic system was selected and used to optimize an Arabidopsis heat shock regime and phenotypic evaluation. Arabidopsis seedlings germinated on a PTFE mesh disc were floated onto a 6-well plate containing liquid MS media, then subjected to heat shock at 45 °C for various duration. To characterize phenotype, plants were harvested after four days of recovery to measure chlorophyll content. The method was extended to include bacterial isolates and to quantify bacterial contributions to host plant thermotolerance. As an exemplar, the method was used to screen 25 strains of the plant growth promoting Variovorax spp. for enhanced plant thermotolerance. A follow-up study demonstrated the reproducibility of this assay and led to the discovery of a novel beneficial interaction. Conclusions This method enables rapid screening of individual bacterial strains for beneficial effects on host plant thermotolerance. The throughput and reproducibility of the system is ideal for testing many genetic variants of Arabidopsis and bacterial strains.
This dataset contains submission files and raw output files from high-throughput DFT simulations to analyze the systemic errors in lattice constant, bulk moduli and formation energy predictions for a range of binary and ternary oxides using four exchange correlation functionals (LDA, PBE, PBEsol and vdW-DF-C09). This data was then used as the basis for employing materials informatics methods to predict the expected errors in the lattice constants of the studied compounds. Predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide “error bars” for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.
Antiferroelectric ceramics are a special class of material that have shown great potential as the dielectric in electrical capacitors due to their high energy- and power-density. During each charge-discharge cycle, the ceramic undergoes transformation to a ferroelectric phase and resumes its antiferroelectric phase. The hysteresis associated with the transitions leads to a mediocre energy efficiency and service lifetime of antiferroelectric capacitors and, hence, their almost absence in commercial products. Under the support of this research project, we first formulated a universal lattice-compatibility theory that included electrostatic polarization energy along with elastic energy and thermal energy to understand the origin of the hysteresis in antiferroelectric oxides. Guided by this compatibility theory, we conducted high-throughput density functional theory (DFT) calculations to assess chemical modifiers and their effect on crystal structures of 400+ PbZrO 3 -based compositions. Down-selected compositions were experimentally validated for their suppressed hysteresis and higher energy efficiency. The verified low-hysteresis compositions were then expanded to an antiferroelectric ceramic library with nearly 500 new compositions (more than 1,500 samples) using high-throughput experiments involving ceramic synthesis and property screening. The large quantity of data generated (theory and experimental) in these tasks were processed by machine-learning techniques and identified trends were fed to the next iteration. In the end, we successfully discovered four compositions with near-zero hysteresis, yielding a world-record energy efficiency of 98.2% at an energy density of 3.0 J/cm 3 . Furthermore, our antiferroelectric ceramic capacitor reaches 79.5 million charge-discharge cycles lifetime, a factor of 80 enhancement over previous antiferroelectric ceramics with large hysteresis. These research accomplishments have not only met the milestones set in the SOPO, but also led to two patent filings, three journal publications (one of them was in Advanced Materials, impact factor 29.4), and nine oral presentations at various venues. Through the course of the project, three postdocs, four Ph.D. students, and one M.S. student were trained. In short, our project established a new methodology in searching next-generation functional ceramics on the fundamental side and discovered several high-efficiency antiferroelectric compositions for capacitors on the applied side. Once fabricated into the multilayer form for commercial applications, these ceramic capacitors can potentially enable the high temperature high power density DC-link capacitors that are critical for the next generation inverters in electric vehicles. The project also significantly contributed to the nation’s workforce development in the STEM fields.
The rapid analysis of biopolymers including lignin and sugars in lignocellulosic biomass cell walls is essential for the analysis of the large sample populations needed for identifying heritable genetic variation in biomass feedstocks for biofuels and bioproducts. In this study, we reported the analysis of cell wall lignin content, syringyl/guaiacyl (S/G) ratio, as well as glucose and xylose content by high-throughput pyrolysis-molecular beam mass spectrometry (py-MBMS) for >3,600 samples derived from hundreds of accessions of Populus trichocarpa from natural populations, as well as pedigrees constructed from 14 parents (7 × 7). Partial Least Squares (PLS) regression models were built from the samples of known sugar composition previously determined by hydrolysis followed by nuclear magnetic resonance (NMR) analysis. Key spectral features positively correlated with glucose content consisted of m/z 126, 98, and 69, among others, deriving from pyrolyzates such as hydroxymethylfurfural, maltol, and other sugar-derived species. Xylose content positively correlated primarily with many lignin-derived ions and to a lesser degree with m/z 114, deriving from a lactone produced from xylose pyrolysis. Models were capable of predicting glucose and xylose contents with an average error of less than 4%, and accuracy was significantly improved over previously used methods. The differences in the models constructed from the two sample sets varied in training sample number, but the genetic and compositional uniformity of the pedigree set could be a potential driver in the slightly better performance of that model in comparison with the natural variants. Broad-sense heritability of glucose and xylose composition using these data was 0.32 and 0.34, respectively. In summary, we have demonstrated the use of a single high-throughput method to predict sugar and lignin composition in thousands of poplar samples to estimate the heritability and phenotypic plasticity of traits necessary to develop optimized feedstocks for bioenergy applications.
A 2019 retrospective study analyzed wristband personal samplers from fourteen different communities across three different continents for over 1530 organic chemicals. Investigators identified fourteen chemicals (G14) detected in over 50% of personal samplers. The G14 represent a group of chemicals that individuals are commonly exposed to, and are mainly associated with consumer products including plasticizers, fragrances, flame retardants, and pesticides. The high frequency of exposure to these chemicals raises questions of their potential adverse human health effects. Additionally, the possibility of exposure to mixtures of these chemicals is likely due to their co-occurrence; thus, the potential for mixtures to induce differential bioactivity warrants further investigation. This study describes a novel approach to broadly evaluate the hazards of personal chemical exposures by coupling data from personal sampling devices with high-throughput bioactivity screenings using in vitro and non-mammalian in vivo models. To account for species and sensitivity differences, screening was conducted using primary normal human bronchial epithelial (NHBE) cells and early life-stage zebrafish. Mixtures of the G14 and most potent G14 chemicals were created to assess potential mixture effects. Chemical bioactivity was dependent on the model system, with five and eleven chemicals deemed bioactive in NHBE and zebrafish, respectively, supporting the use of a multi-system approach for bioactivity testing and highlighting sensitivity differences between the models. In both NHBE and zebrafish, mixture effects were observed when screening mixtures of the most potent chemicals. Observations of BMC-based mixtures in NHBE (NHBE BMC Mix) and zebrafish (ZF BMC Mix) suggested antagonistic effects. In this study, consumer product-related chemicals were prioritized for bioactivity screening using personal exposure data. High-throughput high-content screening was utilized to assess the chemical bioactivity and mixture effects of the most potent chemicals.
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.
In this work, we apply the morphology control of hydride vapor phase epitaxial (HVPE) growth to planarize faceted, non-planar substrates. Controlled spalling is a promising high-throughput substrate reuse technology that could reduce substrate costs for III-V devices; however, the spalling fracture for (100)-oriented GaAs substrates produces a regularly corrugated surface of facets that are 5-20 um in height. We discuss how to planarize these surfaces using only a few minutes of HVPE growth and how to minimize the impact on throughput when integrating faceted wafers into a potential manufacturing process at scale.
Gallium oxide (Ga2O3) is an emerging ultra-wide bandgap semiconductor material that has attracted attention for its potential to outperform existing SiC and GaN based devices operating at high breakdown voltages and high temperature. Isovalent alloying of In and Al in Ga2O3 provides the ability to engineer bandgap energy and strain of the material. Alloying with Al increases the bandgap energy and the theoretically achievable Baliga's figure of merit, a key measure of a material's ultimate performance limits for high power switching devices. Alloying with In introduces compressive strain and can be used to counteract the tensile strain of Al incorporation. The resulting (AlxGa1-x-yIny)2O3 alloy can be lattice-matched to commercially available Ga2O3 wafers and has a tunable bandgap energy greater than that of Ga2O3, 4.76 eV. Such lattice-matched material can be grown arbitrarily thick without the detrimental effects of elastic strain and relaxation, making it suitable for high voltage diodes and transistors. However, efforts to synthesize isovalent alloys are complicated by their tendency to phase separate into corundum Al2O3 or bixbyite In2O3. Literature reports of the quaternary (AlxGa1-x-yIny)2O3 are limited to <1% unintentional indium incorporation in In-catalyzed (AlxGa1-x)2O3. The primary limitation to quaternary growth is the limited incorporation of indium at elevated growth temperatures. This limited incorporation is due to both the volatility of indium oxide and Al and Ga cation exchange reactions which replace indium in In2O3. We report on the development of a novel high-throughput molecular beam epitaxy (MBE) technique to screen the growth conditions for the ternary alloy (InyGa1-y)2O3, and the application of these findings to the first successful synthesis of phase pure monoclinic (AlxGa1-x-yIny)2O3 by MBE. By leveraging the unique sub-oxide chemistry of Ga2O3 and in-situ monitoring of crystal properties by reflection high-energy electron diffraction (RHEED), a cyclical growth and etch-back method is developed and applied to rapidly characterize the (InyGa1-y)2O3 growth space. This cyclical method provides approximately 10x increase in experimental throughput and up to 46x improvement in Ga2O3 substrate utilization. Appropriate growth conditions for monoclinic (InyGa1- y)2O3 are identified by machine learning analysis of RHEED patterns and targeted growths are characterized ex-situ to confirm improved In incorporation. These growth conditions are then combined with established (AlxGa1-x)2O3 growth conditions to grow quaternary (AlxGa1-x-yIny)2O3 with Al mole fractions ranging from 1.4% - 24.4% and In mole fractions ranging from 3.1% to 15.5%. The chemical and optical properties of the alloys are investigated by XRD, XPS, and spectroscopic ellipsometry. A lattice-matched (AlxGa1-x-yIny)2O3 alloy is examined by 4D-STEM and the chemical and physical uniformity of Al and In incorporation are discussed.
Abstract The ability to replicate the microenvironment of biological tissues creates unique biomedical possibilities for stem cell applications. Current fabrication methods are limited by either the control on feature size and shape, or by the throughput and size of the replicas. Here, a novel platform is reported that combines thermal scanning probe lithography (tSPL) with innovative methodologies for the low‐cost and high‐throughput nanofabrication of large area quasi‐3D bone tissue replicas with high fidelity, sub‐15 nm lateral precision, and sub‐2 nm vertical resolution. This bio‐tSPL platform features a biocompatible polymer resist that withstands multiple cell culture cycles, allowing the reuse of the replicas, further decreasing costs and fabrication times. The as‐fabricated replicas support the culture and proliferation of human induced mesenchymal stem cells, which display broad therapeutic and biomedical potential. Furthermore, it is demonstrated that bio‐tSPL can be used to nanopattern the bone tissue replicas with amine groups, for subsequent tissue‐mimetic biofunctionalization. The achieved level of time and cost‐effectiveness, as well as the cell compatibility of the replicas, make bio‐tSPL a promising platform for the production of tissue‐mimetic replicas to study stem cell‐tissue microenvironment interactions, test drugs, and ultimately harness the regenerative capacity of stem cells and tissues for biomedical applications.
A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.