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At least 73 records · Page 4

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DOE OSTI2026

High-throughput small-angle X-ray scattering reveals effective structure factor transitions linked to high-concentration antibody viscosity

High-concentration monoclonal antibody (mAb) formulations are often constrained by elevated viscosity, largely driven by protein–protein interactions, which complicates manufacturing and limits subcutaneous delivery. Early viscosity risk assessment is essential during discovery, yet traditional measurements require large sample volumes, and lack high-throughput capability. Here, we develop a high-throughput small-angle X-ray scattering (SAXS) protocol to detect mAb self-association at dilute concentrations, enabling early predictive insights into high-concentration viscosity. Synchrotron SAXS measurements were conducted for 21 mAbs formulated in a histidine buffer at pH 6.0. An initial subset of 10 mAbs analyzed across 1–150 mg/mL revealed that effective structure factor transitions in the low-q region, indicative of interparticle interactions, consistently emerged below 25 mg/mL. Subsequently, 11 additional mAbs were analyzed at 1–25 mg/mL using automated liquid handling and flow cells to enable high-throughput screening. High-viscosity mAbs exhibited detectable low-q upturns at concentrations ≤10 mg/mL, whereas low-viscosity mAbs showed downturns. A classification criterion based on effective structure factor transitions accurately classified all high- and low-viscosity mAbs at 150 mg/mL, offering a scalable, sample-efficient alternative to conventional methods. These results extend recent findings on the concentration-dependent sensitivity of SAXS to short-range attractions, demonstrating that they can emerge at lower concentrations than previously reported. This study presents the most comprehensive and diverse SAXS dataset for mAbs reported to date within a single formulation, providing a valuable resource for developing and validating coarse-grained models that can more accurately capture intermolecular interactions governing high-concentration solution behavior, thereby enabling rational antibody engineering and improved developability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. Furthermore, these innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Throughput-Oriented and Accuracy-Aware DNN Training with BFloat16 on GPU

Deep Neural Networks (DNNs) have transformed the field of artificial intelligence and achieved extraordinary success in many areas. The training of DNNs is commonly compute and memory-intensive, which has resulted in several optimizations in the training phase. Among them, reduced precision is a typical and widely used technique to accelerate DNN training and reduce memory requirements. However, applying a widely adopted reduced precision format such as Float16 to all involved operations in DNN training is not optimal as the use of Float16 in some operations can hurt model accuracy. Meanwhile, additional optimizations including loss scaling and autocast techniques can mitigate the accuracy loss but lead to inherent overhead and inadequate use of reduced precision. In this work, we leverage another reduced precision format, BFloat16, and introduce a throughput-oriented and accuracy-aware approach to maximize the performance potential of DNN training. Since the high throughput provided by BFloat16 format is accompanied by low precision of the floating-point representation, this approach achieves high throughput by using BFloat16 on all DNN operations and avoids the accuracy loss through a customized accuracy-aware normalization. Results show that our approach outperforms the state-of-the-art mixed-precision training by 1.21x on an NVIDIA A100 GPU.

Xie, Zhen↗

DOE OSTI2026

Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations

Entanglement distribution is a core function of quantum networks essential for operations including teleportation, distributed quantum sensing, and multisite computation. Entanglement throughput and fidelity are two critical performance measures that depend on the quantum transmission along the links and swapping operations at the repeaters along the path. We study the problem of computing a end-to-end entanglement path that satisfies both fidelity and throughput requirements, leveraging qubit buffers at the nodes and considering the sequential swapping order. We show that the general problem of simultaneously satisfying both metrics to be NP-hard, and develop an algorithm to maximize throughput subject to a given fidelity threshold. We introduce the concepts of entanglement probability distribution and path domination and exploit them in the design of our algorithm. Extensive numerical results show that our algorithm can find optimal solutions in networks with thousands of nodes in less than a second. We also describe practical and possible implementation aspects of this algorithm in terms of devices and architecture support.

Xue, Guoliang [Arizona State University]↗

DOE OSTI2021

Integrating continuous hypermutation with high–throughput screening for optimization of cis,cis –muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

High-Throughput Computing: Case Study of Medical Image Processing Applications

HPC is designed for large-scale simulations using monolithic codes of tightly coupled processes highly optimized to deliver decreased time to solution. Medical image processing is not a traditional field of HPC. Similar to AI applications, medical image processing parses large datasets, typically multiple times, to support a variety of studies for classification, diagnosis or monitoring purposes. The convergence of AI, HPC and Big Data encouraged more fields using image processing to transition to HPC. However, not all applications benefit from the same optimizations. In this paper we focus on high throughput medical image processing applications that analyze a huge dataset of small MRI images and that require HPC systems to decrease the time of parsing the entire dataset and not individual MRIs. We show in this research the performance of running SLANT, an image processing application for a whole brain segmentation, on large-scale systems and highlight performance limitations. We present optimizations prioritizing throughput that exhibit a 3.5x speed-up on the Summit Supercomputer that can be used as a baseline for building a high-throughput execution framework for other HPC systems.

Predescu, Maria↗

DOE OSTI2025

Data for "Enhancing Lipid Production in Plant Cells through Automated High-Throughput Genome Engineering and Phenotyping"

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

AI/ML↗

DOE OSTI2020

Building high throughput permissioned blockchain fabrics: challenges and opportunities

Since the introduction of Bitcoin---the first widespread application driven by blockchains---the interest in the design of blockchain-based applications has increased tremendously. At the core of these applications are consensus protocols that securely replicate client requests among all replicas, even if some replicas are Byzantine faulty. Unfortunately, these consensus protocols typically have low throughput, and this lack of performance is often cited as the reason for the slow wider adoption of blockchain technology. Consequently, many works focus on designing more efficient consensus protocols to increase throughput of consensus. We believe that this focus on consensus protocols only explains part of the story. To investigate this belief, we raise a simple question: Can a well-crafted system using a classical consensus protocol outperform systems using modern protocols? In this tutorial, we answer this question by diving deep into the design of blockchain systems. Further, we take an in-depth look at the theory behind consensus, which can help users select the protocol that best-fits their requirements. Finally, we share our vision of high-throughput blockchain systems that operate at large scales.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Ambient Ion Trapping and Separations for High Throughput Structurally Selective Material Deposition

While ion mobility coupled to mass spectrometry (IMS/MS) has been increasingly adapted to biological analysis, the structural and molecular specificity offered by IMS/MS has untapped potential for creating novel materials (for energy, catalysis, biomolecular switches etc.) with rationally tailored properties by precisely controlling the chemical structure and composition of these materials. While electrochemical and catalytic properties of the bulk material are understood by mass-selective ion-soft-landing, different structures of one molecule can exhibit distinct properties from the bulk. Many biological phenomenon occur at interfaces and are highly structurally specific. IMS/MS offers means to separate molecules in a sample (which is first ionized and released to gas phase from the condensed phase) based on mass and structure. Species thus separated can potentially be deposited onto surfaces with molecular and structural specificity, to perform fundamental investigations for understanding critical interfacial phenomenon. Key performance metrics for IMS/MS analysis are resolution of separations and sensitivity. Recently developed Structures for Lossless Ion Manipulations (SLIM)4 enabled unprecedented resolution in IMS separations. In addition, high sensitivity was enabled in SLIM due to lossless confinement of ions using radio frequency fields. Thus, SLIM enabled extremely long path length separations (up to 0.1 km thus far) providing efficient separation of molecular ions never before achieved (e.g. isotopologues, D&L amino acids). While SLIM has unprecedented analytical separations power/utility, the use of SLIM platform for depositing mobility and mass selected molecular ions has two specific technological challenges: (a) Pulsed nature of IMS separations and their low duty cycle and (b) the need for a low vacuum of ~4 torr for lossless ion confinement. These limitations lead to long deposition times due to loss of ions generated from the sample. Enabling ion confinement at atmospheric pressure will be path-breaking contribution to the fundamental understanding of ion manipulations and separations and will also enable novel science currently precluded due to the inability of the present technologies to confine ions under ambient conditions. Traditionally used radio frequency fields are ineffective in confining ions at high pressures due to ion/neutral collisions. To address these, we propose to (a) develop methods for confining and manipulating ions for lossless IMS separations at atmospheric pressure enabling high sensitivity and throughput and (b) use the high specificity of IMS/MS and high throughput from ambient ion confinement to perform mobility and mass selective ion deposition at atmospheric pressure. Our goal is to enable the study of the fundamental properties of energy/catalysis materials and biomolecules. This project paves the way for high-throughput, mass and structure selective material deposition for highly specific surface creation/functionalization (discussed in Section 2 of the report). Further, the developments reported continue to push the boundaries of this process to ambient conditions (discussed in Section 3 of the report). We conclude with a list of specific intellectual property and publications which have been the outcome of this funded project.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Are the U.S. Biorefineries Over the Hurdle of 2000 Ton Daily Throughput Yet?

The efficient utilization of lignocellulosic biomass for biofuel and biochemical production is hindered by material handling issues such as clogging and segregation among other challenges. Preprocessing methods such as drying, screening, and milling have improved conversion yield but have not sufficiently enhanced flowability, especially herbaceous biomass. The poor flowability of herbaceous biomass is rooted in some particle attributes that remain less altered by those methods, e.g., irregular particle shape, high roughness, and high compressibility, making it hard to scale up throughput to a key benchmark for a biorefinery – 2000 ton per day. Applying additional preprocessing methods like pelletization and torrefaction to drastically change those particle attributes can improve flow and handling but has not been comprehensively verified through test. The flowability of herbaceous biomass feedstock formats generated by three different preprocessing methods was recently assessed at Idaho National Laboratory’s Biomass Feedstock National User Facility: first, loose particles size reduced from as-received materials; second, pellets produced from an efficient densification process; and third, powders milled from torrefied pellets. Benchmarking tests including static angle of repose, basic flow energy measured in a powder rheometer, and discharge flow in an adjustable hopper, were conducted to evaluate those feedstock formats. Beyond the capacity of existing experimental apparatuses, a digital engineering approach involving flow simulations and AI models were used to identity the material attributes and processing parameters that have dominant influences on flow throughput. Techno-economic analysis focusing on hopper flow as a typical material handling operation was conducted for those feedstock formats. Perspectives will be discussed on whether the 2000-ton daily throughput for a biorefinery is achievable at an acceptable cost by using any of the tested preprocessing methods.

09 - BIOMASS FUELS↗

DOE OSTI2020

Fermi Level Engineering of Passivation and Electron Transport Materials for p-Type CuBi 2 O 4 Employing a High-Throughput Methodology

Metal oxide semiconductors are promising for solar photochemistry if the issues of excessive charge carrier recombination and material degradation can be resolved, which are both influenced by surface quality and interface chemistry. Coating the semiconductor with an overlayer to passivate surface states is a common remedial strategy but is less desirable than application of a functional coating that can improve carrier extraction and reduce recombination while mitigating corrosion. Here, a data-driven materials science approach utilizing high-throughput methodologies, including inkjet printing and scanning droplet electrochemical cell measurements, is used to create and evaluate multi-element coating libraries to discover new classes of candidate passivation and electron-selective contact materials for p-type CuBi 2 O 4 . The optimized overlayer (Cu 1.5 TiO z ) improves the onset potential by 110 mV, the photocurrent by 2.8×, and the absorbed photon-to-current efficiency by 15.5% compared to non-coated photoelectrodes. It is shown that these enhancements are related to reduced surface recombination through passivation of surface defect states as well as improved carrier extraction efficiency through Fermi level engineering. This work presents a generalizable, high-throughput method to design and optimize passivation materials for a variety of semiconductors, providing a powerful platform for development of high-performance photoelectrodes for incorporation into solar-fuel generation systems.

36 MATERIALS SCIENCE↗

DOE OSTI2023

High‐Throughput Screening Assisted Discovery of a Stable Layered Anti‐Ferromagnetic Semiconductor: CdFeP 2 Se 6

Recent advances in 2D magnetism have heightened interest in layered magnetic materials due to their potential for spintronics. In particular, layered semiconducting antiferromagnets exhibit intriguing low-dimensional semiconducting behavior with both charge and spin as carrier controls. However, synthesis of these compounds is challenging and remains rare. Here, first-principles based high-throughput search is conducted to screen potentially stable mixed metal phosphorous trichalcogenides (MM ' P 2 X 6 , where M and M ' are transition metals and X is a chalcogenide) that have a wide range of tunable bandgaps and interesting magnetic properties. Among the potential candidates, a stable semiconducting layered magnetic material, CdFeP 2 Se 6 , that exhibits a short-range antiferromagnetic order at T N = 21 K with an indirect bandgap of 2.23 eV is successfully synthesized . This work suggests that high-throughput screening assisted synthesis can be an effective method for layered magnetic materials discovery.

2D materials↗

DOE OSTI2024

Harnessing High‐Throughput Computational Methods to Accelerate the Discovery of Optimal Proton Conductors for High‐Performance and Durable Protonic Ceramic Electrochemical Cells

Abstract The pursuit of high‐performance and long‐lasting protonic ceramic electrochemical cells (PCECs) is impeded by the lack of efficient and enduring proton conductors. Conventional research approaches, predominantly based on a trial‐and‐error methodology, have proven to be demanding of resources and time‐consuming. Here, this work reports the findings in harnessing high‐throughput computational methods to expedite the discovery of optimal electrolytes for PCECs. This work methodically computes the oxygen vacancy formation energy (E V ), hydration energy (E H ), and the adsorption energies of H 2 O and CO 2 for a set of 932 oxide candidates. Notably, these findings highlight BaSn x Ce 0.8‐x Yb 0.2 O 3‐δ (BSCYb) as a prospective game‐changing contender, displaying superior proton conductivity and chemical resilience when compared to the well‐regarded BaZr x Ce 0.8‐x Y 0.1 Yb 0.1 O 3‐δ (BZCYYb) series. Experimental validations substantiate the computational predictions; PCECs incorporating BSCYb as the electrolyte achieved extraordinary peak power densities in the fuel cell mode (0.52 and 1.57 W cm −2 at 450 and 600 °C, respectively), a current density of 2.62 A cm −2 at 1.3 V and 600 °C in the electrolysis mode while demonstrating exceptional durability for over 1000‐h when exposed to 50% H 2 O. This research underscores the transformative potential of high‐throughput computational techniques in advancing the field of proton‐conducting oxides for sustainable power generation and hydrogen production.

08 HYDROGEN↗

DOE OSTI2023

High-Throughput Automated Exploration of Phase Growth Behaviors in Quasi-2D Formamidinium Metal Halide Perovskites

Quasi-2D metal halide perovskites (MHPs) are an emerging material platform for sustainable functional optoelectronics, but the uncontrollable, broad phase distribution remains a critical challenge for applications. Nevertheless, the basic principles for controlling phases in quasi-2D MHPs remain poorly understood, due to the rapid crystallization kinetics during the conventional thin-film fabrication process. In this work, a high-throughput automated synthesis-characterization-analysis workflow is implemented to accelerate material exploration in formamidinium (FA)-based quasi-2D MHP compositional space, revealing the early-stage phase growth behaviors fundamentally determining the phase distributions. Upon comprehensive exploration with varying synthesis conditions including 2D:3D composition ratios, antisolvent injection rates, and temperatures in an automated synthesis-characterization platform, it is observed that the prominent n = 2 2D phase restricts the growth kinetics of 3D-like phases—α-FAPbI 3 MHPs with spacer-coordinated surface—across the MHP compositions. Thermal annealing is a critical step for proper phase growth, although it can lead to the emergence of unwanted local PbI 2 crystallites. Additionally, fundamental insights into the precursor chemistry associated with spacer-solvent interaction determining the quasi-2D MHP morphologies and microstructures are demonstrated. The high-throughput study provides comprehensive insights into the fundamental principles in quasi-2D MHP phase control, enabling new control of the functionalities in complex materials systems for sustainable device applications.

2D perovskites↗

DOE OSTI2020

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs), key components of crucial nutrients, soaps, industrial chemicals, and fuels. Yet, our ability to control the specificity of microbially synthesized FFAs is still limited for medium-chain fatty acids (MCFAs), which is mainly due to lack of high-throughput approaches for FFA analysis. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. Specially, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and the m/z peak ratio at 730 and 758 was used as an index for improved MCFA production. This yeast colony-based method can be performed at a rate of ~2 second per sample, which is much faster than the gold standard gas chromatography (GC)-MS (typically > 30 min per sample). To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified 9 mutants that resulted in improved MCFA production relative to the wild-type strain. This colony-based MALDI-ToF MS screening method offers an alternative way for engineering microbial fatty acid compositions in a high-throughput manner.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

High‐Throughput Study of Amorphous Stability and Optical Properties of Superlattice‐Like Ge–Sb–Te Thin Films

A high‐throughput ion beam sputtering system is used to synthesize compositional gradient superlattice‐like (SLL) thin film libraries of Ge–Sb–Te alloys over the entire phase diagram. Here, the optical properties and structural evolution of the Ge–Sb–Te combinatorial SLL thin film are investigated. A systematic screening over the annealing temperature, annealing time, and modulation period has elucidated the critical factors that affect the stability of the metastable phase and optical properties. It is found that amorphous stability and optical constant are highly dependent on the modulation period and chemical composition of the thin film. This data‐driven approach offers new perspectives for accelerating the development of new materials with excellent optical and amorphous stability and for exploring their mechanisms, by greatly expanding the dataset of Ge–Sb–Te alloys with SLL structures through high‐throughput experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Generation of macro- and microplastic databases by high-throughput FTIR analysis with microplate readers

Abstract FTIR spectral identification is today’s gold standard analytical procedure for plastic pollution material characterization. High-throughput FTIR techniques have been advanced for small microplastics (10–500 µm) but less so for large microplastics (500–5 mm) and macroplastics (> 5 mm). These larger plastics are typically analyzed using ATR, which is highly manual and can sometimes destroy particles of interest. Furthermore, spectral libraries are often inadequate due to the limited variety of reference materials and spectral collection modes, resulting from expensive spectral data collection. We advance a new high-throughput technique to remedy these problems using FTIR microplate readers for measuring large particles (> 500 µm). We created a new reference database of over 6000 spectra for transmission, ATR, and reflection spectral collection modes with over 600 plastic, organic, and mineral reference materials relevant to plastic pollution research. We also streamline future analysis in microplate readers by creating a new particle holder for transmission measurements using off-the-shelf parts and fabricating a nonplastic 96-well microplate for storing particles. We determined that particles should be presented to microplate readers as thin as possible due to thick particles causing poor-quality spectra and identifications. We validated the new database using Open Specy and demonstrated that additional transmission and reflection spectra reference data were needed in spectral libraries. Graphical abstract

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Aviation security screening optimizer for risk and throughput (ASSORT)

The increasing number of air travelers each year presents a challenge as many airports are near their capacity in terms of resources and space for passenger screening. Fortunately, advancements in technologies like next-generation millimeter wave scanning offer solutions to ease this strain. The focus remains on managing risk while enhancing the passenger experience for the traveling public. The risk model presented in this paper known as the Aviation Security Screening Optimizer for Risk and Throughput (ASSORT) is designed to assess risk-based approaches for passenger screening and checkpoint operations. Additionally, ASSORT is exploring various traveler categories — general, trusted, and trusted-plus — along with different checkpoint screening Concept of Operations tailored to each traveler type. For instance, travelers with a higher trust level may experience fewer screening technologies, resulting in quicker processing times at the checkpoint. The output of ASSORT provides a risk score for predefined threat scenarios, as well as the overall risk to the checkpoint, aircraft, and airport by traveler type. In conclusion, benefits of using this tool include assessing the trade-offs between the overall risk associated with checkpoints and the throughput rate of passengers screened. We show for example the impact that different passenger volumes at the checkpoint can have on risk.

99 GENERAL AND MISCELLANEOUS↗