Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “future accelerators”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bacterial α-diglucoside metabolism: perspectives and potential for biotechnology and biomedicine

In a competitive microbial environment nutrient acquisition is a major contributor to the survival of any individual bacterial species, and the ability to access uncommon energy sources can provide a fitness advantage. One set of soluble carbohydrates that have attracted increased attention for use in biotechnology and biomedicine are the α-diglucosides. Maltose is the most well studied member of this class, however the remaining four less common α-diglucosides (trehalose, kojibiose, nigerose, and isomaltose) are increasingly used in processed food and fermented beverages. The consumption of trehalose has recently been shown to be a contributing factor in gut microbiome disease as certain pathogens are using α-diglucosides to outcompete native gut flora. Kojibiose and nigerose have also been examined potential prebiotics and alternative sweeteners for a variety of foods. Compared to the study of maltose metabolism our understanding of the synthesis and degradation of uncommon α-diglucosides is lacking and several fundamental questions that remain unanswered, particularly in regards to the regulation of bacterial metabolism for α-diglucosides. Therefore this minireview attempts to provide a focused analysis of uncommon α-diglucoside metabolism in bacteria, and suggests some future directions for this research area that could potentially accelerate biotechnology and biomedicine developments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗
DOE OSTI2016

Self-interacting dark matter without direct detection constraints

For this paper, we explore the self-interacting dark matter scenario in a simple dark sector model where the dark matter interacts through a dark photon. Splitting a Dirac fermion dark matter into two levels using a small Majorana mass can evade strong direct detection constraints on the kinetic mixing between the dark and normal photons, thus allowing the dark sector to be more visible at high intensity and/or high energy experiments. It is pointed out that such a mass splitting has a strong impact on the dark matter self-interaction strength. We derive the new parameter space of a pseudo-Dirac self-interacting dark matter. Interestingly, with increasing mass splitting, a weak scale dark matter mass window survives that could be probed by the LHC and future colliders

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Observation of acceleration and deceleration in gigaelectron-volt-per-metre gradient dielectric wakefield accelerators

There is urgent need to develop new acceleration techniques capable of exceeding gigaelectron-volt-per-metre (GeV m –1 ) gradients in order to enable future generations of both light sources and high-energy physics experiments. To address this need, short wavelength accelerators based on wakefields, where an intense relativistic electron beam radiates the demanded fields directly into the accelerator structure or medium, are currently under intense investigation. One such wakefield based accelerator, the dielectric wakefield accelerator, uses a dielectric lined-waveguide to support a wakefield used for acceleration. Here we show gradients of 1.347±0.020 GeV m –1 using a dielectric wakefield accelerator of 15 cm length, with sub-millimetre transverse aperture, by measuring changes of the kinetic state of relativistic electron beams. We follow this measurement by demonstrating accelerating gradients of 320±17 MeV m –1 . As a result, both measurements improve on previous measurements by and order of magnitude and show promise for dielectric wakefield accelerators as sources of high-energy electrons.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Generative AI for design of nanoporous materials: review and future prospects

Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Ultrathin oxide layers for nanoscale integration of molecular light absorbers, catalysts, and complete artificial photosystems

Artificial photosynthesis is an attractive approach for the generation of renewable fuels because such systems will be suitable for deployment on highly abundant, non-arable land. Recently emerged methods of nanoscience to create conformal, ultrathin oxide layers enable the hierarchical integration of light absorbers, catalysts, and membranes into systems with far simpler synthetic approaches than available till now. This holds in particular for the coupling of molecular light absorbers and catalysts for sunlight to fuel conversion, providing photoelectrodes with greatly improved stability. Moreover, the use of ultrathin inert oxides as proton conducting, molecule impermeable membranes has opened up the integration of reduction and oxidation half reactions into complete photosynthetic systems on the shortest possible length scale-the nanometer scale. This capability affords minimization of energy-degrading resistance losses caused by ion transport over macroscale distances while separating the incompatible water oxidation and carbon dioxide reduction catalysis environments on the nanoscale. Understanding of charge transport between molecular components embedded in the oxide layers is critical for guiding synthetic design improvements of the light absorber-catalyst units to optimize performance and integrate them into complete artificial photosystems. Recent results and insights from transient optical, vibrational, and photoelectrochemical studies are presented, and future challenges and opportunities for engaging dynamic spectroscopies to accelerate the development of nanoscale integrated artificial photosystems are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ambient-temperature liquid jet targets for high-repetition-rate HED discovery science

High-power lasers can generate energetic particle beams and astrophysically relevant pressure and temperature states in the high-energy-density (HED) regime. Recently-commissioned high-repetition-rate (HRR) laser drivers are capable of producing these conditions at rates exceeding 1 Hz. However, experimental output from these systems is often limited by the difficulty of designing targets that match these repetition rates. To overcome this challenge, we have developed tungsten microfluidic nozzles, which produce a continuously replenishing jet that operates at flow speeds of approximately 10 m/s and can sustain shot frequencies up to 1 kHz. The ambient-temperature planar liquid jets produced by these nozzles can have thicknesses ranging from hundreds of nanometers to tens of micrometers. In this work, we illustrate the operational principle of the microfluidic nozzle and describe its implementation in a vacuum environment. Further, we provide evidence of successful laser-driven ion acceleration using this target and discuss the prospect of optimizing the ion acceleration performance through an in situ jet thickness scan. Future applications for the jet throughout HED science include shock compression and studies of strongly heated nonequilibrium plasmas. When fielded in concert with HRR-compatible laser, diagnostic, and active feedback technology, this target will facilitate advanced automated studies in HRR HED science, including machine learning-based optimization and high-dimensional statistical analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Targeted evolution of pinning landscapes for large superconducting critical currents

The ability of type II superconductors to carry large amounts of current at high magnetic fields is a key requirement for future design innovations in high-field magnets for accelerators and compact fusion reactors, and largely depends on the vortex pinning landscape comprised of material defects. The complex interaction of vortices with defects that can be grown chemically, e.g., self-assembled nanoparticles and nanorods, or introduced by postsynthesis particle irradiation precludes a priori prediction of the critical current and can result in highly nontrivial effects on the critical current. Here, we borrow concepts from biological evolution to create a vortex pinning genome based on a genetic algorithm, naturally evolving the pinning landscape to accommodate vortex pinning and determine the best possible configuration of inclusions for two different scenarios: a natural evolution process initiating from a pristine system and one starting with preexisting defects to demonstrate the potential for a postprocessing approach to enhance critical currents. Furthermore, the presented approach is even more general and can be adapted to address various other targeted material optimization problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A molecular view of peptoid-induced acceleration of calcite growth

The extensive deposits of calcium carbonate (CaCO 3 ) generated by marine organisms constitute the largest and oldest carbon dioxide (CO 2 ) reservoir. These organisms utilize macromolecules like peptides and proteins to facilitate the nucleation and growth of carbonate minerals, serving as an effective method for CO 2 sequestration. However, the precise mechanisms behind this process remain elusive. In this study, we report the use of sequence-defined peptoids, a class of peptidomimetics, to achieve the accelerated calcite step growth kinetics with the molecular level mechanistic understanding. By designing peptoids with hydrophilic and hydrophobic blocks, we systematically investigated the acceleration in step growth rate of calcite crystals using in situ atomic force microscopy (AFM), varying peptoid sequences and concentrations, CaCO 3 supersaturations, and the ratio of Ca 2+ / HCO 3 − . Mechanistic studies using NMR, three-dimensional fast force mapping (3D FFM), and isothermal titration calorimetry (ITC) were conducted to reveal the interactions of peptoids with Ca 2+ and HCO 3 − ions in solution, as well as the effect of peptoids on solvation and energetics of calcite crystal surface. Our results indicate the multiple roles of peptoid in facilitating HCO 3 − deprotonation, Ca 2+ desolvation, and the disruption of interfacial hydration layers of the calcite surface, which collectively contribute to a peptoid-induced acceleration of calcite growth. These findings provide guidelines for future design of sequence-specific biomimetic polymers as crystallization promoters, offering potential applications in environmental remediation (such as CO 2 sequestration), biomedical engineering, and energy storage where fast crystallization is preferred.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗
DOE OSTI2025

Determining spin-dependent light dark matter rates from neutron scattering

The scattering and absorption rates of light dark matter with electron spin-dependent interactions depend on the target’s spin response. We show how this response is encoded by the target’s dynamical magnetic susceptibility, which can be measured using neutron scattering. We directly use existing neutron scattering data to compute the dark matter scattering rate in a candidate target material, finding close agreement with the previous first-principles calculation at MeV dark matter masses. Complementary experiments and measurements can extend the reach of this technique to other dark matter models and masses, and identify promising target materials for future experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Virtual Power Plants: Pilots, Challenges, and Innovations Shaping Future Development

Virtual Power Plants (VPPs) aggregate distributed energy resources (DERs) to provide grid services traditionally delivered by centralized power plants. This article reviews the current state of VPP deployment, highlighting business models, compensation mechanisms, and global pilot projects. While VPPs offer benefits such as grid flexibility, resilience, and cost savings, challenges remain in communication infrastructure, regulatory frameworks, market access, and customer engagement. To address these, we propose a scalable, privacy-preserving hierarchical VPP architecture that coordinates with distribution utilities and preserves customer data. We also present the Integrated T&D Control Room of the Future as a key test bed for validating and accelerating VPP adoption. These innovations can help transition VPPs from pilot programs to integral components of a modern, reliable power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Performance of a geometric deep learning pipeline for HL-LHC particle tracking

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Progress Towards RF Conditioning of Low-Loss Couplers for a Conduction-Cooled Cryomodule

This work presents current progress on the conditioning of two new 25 kW couplers optimized for use in a compact, conduction-cooled SRF cryomodule. A connecting waveguide, previously used for conditioning the 805 MHz SNS couplers, was altered for use at 915 MHz. The necessary modifications were determined via RF modeling, while thermal analysis results identified additional cooling requirements during RF conditioning and provided insight about potential higher-power operation. Initial low-power conditioning will be performed with a 2.5 kW solid-state amplifier, with plans to use an industrial magnetron for RF conditioning at 25 kW in the near future.

Stilin, N. [Thomas Jefferson National Accelerator ↗
DOE OSTI2016

Catalytic Deoxygenation of Biomass Pyrolysis Vapors to Improve Bio-oil Stability

The President’s Advanced Energy Initiative called for a change in the way Americans fuel their vehicles to promote improved energy security. Increasing biofuels production from domestic lignocellulosic resources requires advanced technology development to achieve the aggressive targets set forth to reduce motor gasoline consumption by 20% in ten years (by 2017). The U.S. Department of Energy (USDOE) Office of the Biomass Program (currently Bioenergy Technologies Office) is actively funding research and development in both biochemical and thermochemical conversion technologies to accelerate the deployment of biofuels technologies in the near future to meet the goals of the Advanced Energy Initiative. Thermochemical conversion technology options include both gasification and pyrolysis to enable the developing lignocellulosic biorefineries and maximize biomass resource utilization for production of biofuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗