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DOE OSTI2026

Unraveling Defect-Dependent Conductivity-Type Switching in CuFe 2 O 4 for Enhanced Photoelectrocatalytic Reduction of Benzaldehyde

Photoelectrocatalytic (PEC) reduction provides a sustainable route for upgrading biomass-derived feedstocks with reduced energy requirements, yet remains largely unexplored beyond hydrogen evolution and CO 2 reduction due to the scarcity of stable photocathodes. Here, we report a defect-engineered CuFe 2 O 4 photocathode that enables directly quantified PEC reduction of benzaldehyde to benzyl alcohol using a singlecomponent, earth-abundant oxide. By controlling annealing temperature and oxygen partial pressure, CuFe 2 O 4 is systematically tuned from n-type to p-type conductivity. Electrochemical measurements, X-ray and ultraviolet photoelectron spectroscopy, and firstprinciples defect calculations collectively show that oxygen-rich annealing conditions suppress deep donor-type oxygen vacancies while stabilizing shallow acceptor-type copper vacancies, resulting in enhanced hole concentration and improved charge transport. In a mixed acetonitrile/water electrolyte employing 1,4-benzoquinone as a redox mediator, the optimized CuFe 2 O 4 photocathode achieves stable photoelectrochemical operation over 18 h under continuous illumination with a benzyl alcohol production rate of 2.57 μmol/h at −0.50 V vs Ag/AgNO 3 , corresponding to a Faradaic efficiency of 51.3%. This PEC approach lowers the required applied potential by ∼1 V compared to traditional electrocatalytic methods, offering a more energy-efficient route for carbonyl reduction. These findings establish CuFe 2 O 4 as a viable photocathode platform for sustainable photoelectrocatalytic organic transformations under mild reaction conditions.

benzaldehyde reduction↗

DOE OSTI2023

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗

DOE OSTI2016

SU-G-JeP1-12: Head-To-Head Performance Characterization of Two Multileaf Collimator Tracking Algorithms for Radiotherapy

Purpose: Multi-leaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and abdominal motion during radiotherapy. The purpose of this work is to characterize the performance of two MLC tracking algorithms for cancer radiotherapy, based on a direct optimization and a piecewise leaf fitting approach respectively. Methods: To test the algorithms, both physical and in silico experiments were performed. Previously published high and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient-measured organ-specific trajectories. For both MLC tracking algorithm, the plans were run with their corresponding patient trajectories. The physical experiments were performed on a Trilogy Varian linac and a programmable phantom (HexaMotion platform). For each MLC tracking algorithm, plan and patient trajectory, the tracking accuracy was quantified as the difference in aperture area between ideal and fitted MLC. To compare algorithms, the average cumulative tracking error area for each experiment was calculated. The two-sample Kolmogorov-Smirnov (KS) test was used to evaluate the cumulative tracking errors between algorithms. Results: Comparison of tracking errors for the physical and in silico experiments showed minor differences between the two algorithms. The KS D-statistics for the physical experiments were below 0.05 denoting no significant differences between the two distributions pattern and the average error area (direct optimization/piecewise leaf-fitting) were comparable (66.64 cm2/65.65 cm2). For the in silico experiments, the KS D-statistics were below 0.05 and the average errors area were also equivalent (49.38 cm2/48.98 cm2). Conclusion: The comparison between the two leaf fittings algorithms demonstrated no significant differences in tracking errors, neither in a clinically realistic environment nor in silico. The similarities in the two independent algorithms give confidence in the use of either algorithm for clinical implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

F3D Image Processing and Analysis for Many - and Multi-core Platforms

F3D is written in OpenCL, so it achieve[sic] platform-portable parallelism on modern mutli-core CPUs and many-core GPUs. The interface and mechanims to access F3D core are written in Java as a plugin for Fiji/ImageJ to deliver several key image-processing algorithms necessary to remove artifacts from micro-tomography data. The algorithms consist of data parallel aware filters that can efficiently utilizes[sic] resources and can work on out of core datasets and scale efficiently across multiple accelerators. Optimizing for data parallel filters, streaming out of core datasets, and efficient resource and memory and data managements over complex execution sequence of filters greatly expedites any scientific workflow with image processing requirements. F3D performs several different types of 3D image processing operations, such as non-linear filtering using bilateral filtering and/or median filtering and/or morphological operators (MM). F3D gray-level MM operators are one-pass constant time methods that can perform morphological transformations with a line-structuring element oriented in discrete directions. Additionally, MM operators can be applied to gray-scale images, and consist of two parts: (a) a reference shape or structuring element, which is translated over the image, and (b) a mechanism, or operation, that defines the comparisons to be performed between the image and the structuring element. This tool provides a critical component within many complex pipelines such as those for performing automated segmentation of image stacks. F3D is also called a "descendent" of Quant-CT, another software we developed in the past. These two modules are to be integrated in a next version. Further details were reported in: D.M. Ushizima, T. Perciano, H. Krishnan, B. Loring, H. Bale, D. Parkinson, and J. Sethian. Structure recognition from high-resolution images of ceramic composites. IEEE International Conference on Big Data, October 2014.

Source record↗

DOE OSTI2015

A microfluidic platform for precision small-volume sample processing and its use to size separate biological particles with an acoustic microdevice [Precision size separation of biological particles in small-volume samples by an acoustic microfluidic system]

Here, a major advantage of microfluidic devices is the ability to manipulate small sample volumes, thus reducing reagent waste and preserving precious sample. However, to achieve robust sample manipulation it is necessary to address device integration with the macroscale environment. To realize repeatable, sensitive particle separation with microfluidic devices, this protocol presents a complete automated and integrated microfluidic platform that enables precise processing of 0.15–1.5 ml samples using microfluidic devices. Important aspects of this system include modular device layout and robust fixtures resulting in reliable and flexible world to chip connections, and fully-automated fluid handling which accomplishes closed-loop sample collection, system cleaning and priming steps to ensure repeatable operation. Different microfluidic devices can be used interchangeably with this architecture. Here we incorporate an acoustofluidic device, detail its characterization, performance optimization, and demonstrate its use for size-separation of biological samples. By using real-time feedback during separation experiments, sample collection is optimized to conserve and concentrate sample. Although requiring the integration of multiple pieces of equipment, advantages of this architecture include the ability to process unknown samples with no additional system optimization, ease of device replacement, and precise, robust sample processing.

42 ENGINEERING↗

DOE OSTI2014

F3D Image Processing and Analysis for Many - and Multi-core Platforms

Source record↗

DOE OSTI2020

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

It has been shown that model predictive control (MPC) is a promising solution for energy-efficient building operations. However, the deployment of MPC in a large portion of the building stock has not been possible partially because of high installation costs. Every building is unique and requires a tailored MPC solution. The best performing solutions are often based on physics-based modeling, which is, however, computationally expensive and requires dedicated software. A promising direction that tackles this problem is to train a neural network-based optimal control policy to imitate the behavior of physics-based MPC from the simulation data generated offline. The neural networks give control actions that closely approximate those produced by physics-based MPC, but with a fraction of the computational and memory requirements and without the need for licensed software. The main advantage of the proposed approach stems from simple evaluation at execution time, leading to low computational footprints and easy deployment on embedded HW platforms. In the case study, we present the energy savings potential of physics-based MPC applied to an office building in Belgium. We demonstrate how neural network approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the remaining challenges and open research questions.

Drgona, Jan↗

DOE OSTI2025

Comparing Control Performance Between Simulation and Experiment using the Microreactor Automated Control System Testbed

In the advanced reactor domain, a flexible and scalable software/hardware infrastructure is crucial for integrating and validating various control technologies. This study used the Microreactor Automated Control System (MACS) hardware platform as a testbed. MACS was originally designed to mirror Idaho National Laboratory (INL)'s Microreactor Applications Research Validation and Evaluation (MARVEL), a 85-kW thermal fission microreactor. It features control drums for simulated reactivity control; lights that function as a surrogate reactor core, with the brightness being proportional to the reactor power; and light sensors that emulate neutron detectors. To transform MACS into a physical twin of MARVEL for evaluating control methods, the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software was employed. This software integrated the hardware with two models of the MARVEL core, based on Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The study aimed to demonstrate the gap between control theory and actual practice—a gap that often necessitates empirical adjustments such as control gain retuning, filters, time discretization, and integrator anti-windup measures. Controllers were developed based on increasingly complex simulations without hardware, starting from the base MARVEL model and then introducing actuator saturation constraints and sensor noise. The final control strategy was then tested using MACS, and a comparative performance analysis was conducted.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2026

Microbial valorization of lignin to malic acid by Aspergillus niger

Lignin is the largest renewable source of aromatic carbon, yet its heterogeneity and recalcitrance limit its use in higher-value bioconversion processes. In this study, Aspergillus niger was engineered to enable the bioconversion of lignin-derived aromatics and base-catalyzed depolymerized (BCD) lignin streams into malic acid, a value-added C4 dicarboxylic acid with broad industrial relevance. Overexpression of the C4 dicarboxylate transporter C4T318 from Aspergillus oryzae enhanced malic acid secretion, while medium optimization under buffered conditions further improved the production. The engineered strain efficiently assimilated representative lignin-derived aromatics, including 4-hydroxybenzoic acid and p-coumaric acid, producing up to 3.9 g/L malic acid. Conversion of BCD lignin liquors from poplar and sorghum demonstrated effective utilization of heterogeneous aromatic mixtures, generating up to 0.82 g/L malic acid. This work demonstrates direct fungal conversion of real lignin streams into malic acid and establishes A. niger as a promising platform for sustainable lignin valorization.

Aromatic bioconversion↗

DOE OSTI2023

High-fidelity parallel entangling gates on a neutral-atom quantum computer

The ability to perform entangling quantum operations with low error rates in a scalable fashion is a central element of useful quantum information processing. Neutral-atom arrays have recently emerged as a promising quantum computing platform, featuring coherent control over hundreds of qubits and any-to-any gate connectivity in a flexible, dynamically reconfigurable architecture. The main outstanding challenge has been to reduce errors in entangling operations mediated through Rydberg interactions. Here we report the realization of two-qubit entangling gates with 99.5% fidelity on up to 60 atoms in parallel, surpassing the surface-code threshold for error correction. Our method uses fast, single-pulse gates based on optimal control, atomic dark states to reduce scattering and improvements to Rydberg excitation and atom cooling. We benchmark fidelity using several methods based on repeated gate applications, characterize the physical error sources and outline future improvements. Finally, we generalize our method to design entangling gates involving a higher number of qubits, which we demonstrate by realizing low-error three-qubit gates. By enabling high-fidelity operation in a scalable, highly connected system, these advances lay the groundwork for large-scale implementation of quantum algorithms, error-corrected circuits and digital simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

High Multiplicity Trigger for long-lived particles in CMS detector

Searches for long-lived particles (LLPs) at the CMS experiment often involve unconventional event topologies that are difficult to efficiently select using standard trigger strategies. To improve sensitivity to such signatures during LHC Run 3 operation, a dedicated High Multiplicity Trigger (HMT) has been developed and deployed in the CMS trigger system. The trigger targets events containing unusually large numbers of hits in the CMS cathode strip chamber (CSC) muon detectors, a characteristic signature of several LLP scenarios involving displaced decays in the muon system. The HMT implementation, trigger logic, rate dependence with pileup, and operational stability are described. Optimized hit multiplicity thresholds are used to maintain acceptable trigger rates under high-luminosity and high-pileup conditions while preserving high efficiency across a broad range of LLP lifetimes and kinematic regimes. The trigger performance is evaluated using both simulated event samples and proton-proton collision data collected during Run 3 of the LHC. The HMT substantially extends the CMS sensitivity to non-standard signatures associated with LLP decays and provides a flexible platform for future searches for physics beyond the Standard Model.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

Artificial Neural Network as the FPGA Trigger in the Cyclone V based Front-End for a Detection of Neutrino-Origin Showers

Neutrinos play a fundamental role in the understanding of the origin of ultra-high-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, their a very low rate of events potentially generated by neutrinos is a significant challenge for a detection technique and requires both sophisticated algorithms and high-resolution hardware. A trigger based on a artificial neural network was implemented into the Cyclone{sup R} V E FPGA 5CEFA9F31I7 - the heart of the prototype Front-End boards developed for tests of new algorithms in the Pierre Auger surface detectors. Showers for muon and tau neutrino initiating particles on various altitudes, angles and energies were simulated in CORSICA and Offline platforms giving pattern of ADC traces in Auger water Cherenkov detectors. The 3-layer 12-8-1 neural network was taught in MATLAB by simulated ADC traces according the Levenberg-Marquardt algorithm. Results show that a probability of a ADC traces generation is very low due to a small neutrino cross-section. Nevertheless, ADC traces, if occur, for 1-10 EeV showers are relatively short and can be analyzed by 16-point input algorithm. We optimized the coefficients from MATLAB to get a maximal range of potentially registered events and for fixed-point FPGA processing to minimize calculation errors. New sophisticated triggers implemented in Cyclone{sup R} V E FPGAs with large amount of DSP blocks, embedded memory running with 120 - 160 MHz sampling may support a discovery of neutrino events in the Pierre Auger Observatory. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Using Grover’s search protocol to select the best qubit pairs

This research represents a continuation of our investigation of the Rigetti quantum platform as part of the Quantum Leap for Fusion Energy Sciences project. We evaluate the performance of the new Aspen-11, Aspen-M-2, Aspen-M-3 quantum processing units (QPU) through the application of the Grover’s search algorithm and the validation of single gate fidelities. The performance of the new QPUs is compared to the older Aspen-7 device, and it is shown that qubit pair selection plays a key role in the optimization process. Additionally, we delve into the examination of coherent and decoherent errors associated with native gates. To optimize our approach, we have developed several relatively inexpensive hardware protocols aimed at facilitating the selection of the most suitable qubit pairs. These protocols involve running various circuits on the hardware and assessing the overall performance of the tested qubit pairs. Through these protocols, we have demonstrated that the quality of qubit pairs on a single chip can exhibit significant variations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.

42 ENGINEERING↗

DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Cr-Be Pushered Single Shell (PSS) Capsule Development

Pushered single shell (PSS) has emerged as an alternative platform of implosion to ease the stability issue at the ablation front. The design consists of a thin inner Be layer, a 50% Cr:Be plateau region, an S-curve gradient, a low Cr (1.5%) tamper layer, and followed by a pure Be outer layer. General Atomics has developed a way to fabricate the higher-Z Cr to lower-Z Be gradients on glow discharge polymer mandrels with a designed S-shape profile for optimal implosion stability using magnetron sputtering. Microstructure analysis of the gradient coating indicated that at lower Cr concentration a short order or amorphous structure was formed. Here, these fabricated PSS capsules were subsequently built into capsule fill tube assemblies, verified to be leak tight at both ambient and cryogenic conditions, and delivered to Lawrence Livermore National Laboratory for the shots. The capsule thermal stability was demonstrated by invariable Cr profiles before and after pyrolysis. However, cracking at inner Cr layers was observed, which has been attributed to thermal stress.

Capsule↗

DOE OSTI2019

Multiplexed CRISPR-Cas9-Based Genome Editing of Rhodosporidium toruloides

Microbial production of biofuels and bioproducts offers a sustainable and economic alternative to petroleum-based fuels and chemicals. The basidiomycete yeast Rhodosporidium toruloides is a promising platform organism for generating bioproducts due to its ability to consume a broad spectrum of carbon sources (including those derived from lignocellulosic biomass) and to naturally accumulate high levels of lipids and carotenoids, two biosynthetic pathways that can be leveraged to produce a wide range of bioproducts. While R. toruloides has great potential, it has a more limited set of tools for genetic engineering relative to more advanced yeast platform organisms such as Yarrowia lipolytica and Saccharomyces cerevisiae. Significant advancements in the past few years have bolstered R. toruloides ’ engineering capacity. Here we expand this capacity by demonstrating the first use of CRISPR-Cas9-based gene disruption in R. toruloides . Transforming a Cas9 expression cassette harboring nourseothricin resistance and selecting transformants on this antibiotic resulted in strains of R. toruloides exhibiting successful targeted disruption of the native URA3 gene. While editing efficiencies were initially low (0.002%), optimization of the cassette increased efficiencies 364-fold (to 0.6%). Applying these optimized design conditions enabled disruption of another native gene involved in carotenoid biosynthesis, CAR2 , with much greater success; editing efficiencies of CAR2 deletion reached roughly 50%. Finally, we demonstrated efficient multiplexed genome editing by disrupting both CAR2 and URA3 in a single transformation. Altogether, our results provide a framework for applying CRISPR-Cas9 to R. toruloides that will facilitate rapid and high-throughput genome engineering in this industrially relevant organism.

59 BASIC BIOLOGICAL SCIENCES↗