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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.
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The project supported the development of new methodologies for performing matrix computations that form key building blocks in modern scientific computing, such as low rank approximation of matrices, and efficient representations of global operators that arise in simulations of physical phenomena.
The global carbon cycle is carefully balanced through the use of specialized enzymes in plants, algae, bacteria, and archaea. A primordial metabolic pathway for the conversion of inorganic carbon into cellular biomass uses a large, nickel-containing enzyme called carbon monoxide dehydrogenase (CODH)/acetyl coenzyme A synthase (ACS). This system fixes carbon dioxide (CO 2 ) into carbon monoxide (CO) at the CODH site. The carbon monoxide is then used to generate acetyl coenzyme A, a biological building block, through a key carbon-carbon bond forming step at the nickel site in ACS. Despite the significance of these processes in the context of energy conversion, the fundamental chemistry underlying these transformations has remained elusive, in part due to the complexity of the natural enzyme. To better understand the principles governing these biological processes, which represent one-carbon (C 1 ) activation reactions, this project will develop functional models of CODH and ACS based on modifications to a small metalloprotein scaffold. Comprehensive characterization of these systems using advanced spectroscopic and biophysical techniques will reveal key elements responsible for conferring high levels of activity to the model systems, with implications for gaining insight into the mechanisms of the natural enzymes. Moreover, the principles learned from this research can be used to guide design of robust catalysts for efficient conversion of CO 2 and CO into liquid fuels.
This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.
This paper discusses the implementation of the One-Turn Delay Feedback (OTFB) for the Electron-Ion Collider (EIC). The theory of the OTFB is well documented, therefore, this paper will mainly discuss the details of the implementation. After a short introduction, the paper will discuss the building-blocks of the OTFB, simulation testing of the OTFB, and finally hardware validation of the OTFB.
This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations (PDEs) and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for implementing large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication.
The next generation of qubits, the building blocks of quantum technologies, are designed using molecules. Molecular qubits have natural advantages over solid-state equivalents in biological and chemical sensing. Chemistry allows the properties of molecular qubits to be fine-tuned, such as the ability of multiple qubits to work together using qubit-qubit coupling. Molecules and structures containing multiple qubits, made from both inorganic and organic elements, are developed in this work to bring quantum technologies closer to room-temperature operation. The goal is achieved by understanding how the core of the qubit, known as spin, interacts with the vibrating parts of the molecule, other spins, and the surrounding environment. A combined approach involving theory, spectroscopy, and synthesis is used to achieve the design goal.
Superconducting transmon qubits are a central building block of modern quantum computing architectures, yet their coherence properties remain sensitive to external influences that can limit performance or introduce temporal instabilities. In this talk, I will discuss two experimental studies aimed at quantifying these effects. First, I will examine how ionizing radiation impacts qubit relaxation. Using the same transmon device operated at two locations with dramatically different radiation backgrounds—the above-ground SQMS facility at Fermilab (USA) and the deep-underground Gran Sasso Laboratory (Italy)—we observe a higher rate of radiation-induced decay events above ground, even though intrinsic noise remains the dominant source of single-shot errors. I will also discuss the detection efficiency of a radiation detector made using such a device. Second, I will present results on how small magnetic fields, applied either during cooldown or at millikelvin temperatures, influence device stability. We find that fixed-frequency transmons maintain robust coherence up to approximately 600 mG of trapped out-of-plane field, and that controlled application of magnetic field can reduce temporal fluctuations in T1. Together, these studies provide insight into how external environments shape transmon coherence and offer potential pathways for improving qubit stability in scalable quantum processors.
With the discovery of the Higgs boson at the CERN LHC, the world's highest-energy particle accelerator complex, scientists have acquired an important tool to study the fundamental building blocks of the universe. Precision measurements of Higgs bosons produced with large momentum allow for unique insights into the structure of the interactions of the Higgs boson with other particles that may shed light on physics beyond the standard model. While experimentally challenging, exploring such interactions with novel artificial intelligence (AI) methods can advance our understanding of the Higgs sector, including the Higgs boson's self-interaction. Moreover, the LHC is undergoing a major upgrade to further increase its particle collision rate and thereby operate for an additional decade. The experimental detectors at the upgraded facility must process at least a factor of ten more data at rates of hundreds of terabytes per second all under challenging conditions. New AI techniques are required to reconstruct and select, or trigger on, the most physics-sensitive events in real-time to handle the resulting avalanche of data. The proposed research will achieve the goals of the LHC program at the CMS experiment by developing a sub-microsecond event reconstruction system using real-time AI algorithms that employ field-programmable gate array technologies. By harnessing sophisticated AI techniques, this research focuses on measuring the production of Higgs bosons at large momentum while enhancing particle reconstruction methods in the trigger and beyond. Overall, the proposed research has broader implications for the use of AI in resource-constrained, low-latency embedded applications across all fields of science.
Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).
Ultra-Fast timing detectors, having a time resolution of 10pSec or less, are becoming increasingly important for many scientific disciplines, including High Energy Physics, Nuclear Physics, Medical Imaging and more. When developing ultra-fast detectors such as MCPs, LAPPDs, LGADs, SNSPDs, it is necessary to have access to a readout system that is low power, can support many channels and is cost effective. The PSEC5 chip was an application specific integrated circuit (ASIC) developed by the pioneering group of Prof. Henry Frisch at University of Chicago and micro electronic engineers at Fermi National Accelerator Laboratory. Three boards were designed, two to implement the readout system for an LAPPD and one to only characterize the PSEC1 chip. The main goal of the project shifted to setting up a testing environment to focus on the characterization of the chip. My student and I tested and characterized this ASIC using the third chip-on-board printed circuit board. Results show the successful fabrication and operation of some of the key building blocks and lead to the suggestions on modification for the second iteration of the chip.
One important test bed system for frustrated magnetism is the pyrochlore lattice with the formula A2B2O7 in the cubic space group Fd3(-)m. Both the A and B sites occupy crystallographic trigonal sites and both sites possess a tetrahedral relationship with the other similar metal ion sites. These symmetry relationships generate a 3 D frustrated structure. In frustrated magnetic systems, typically a trivalent lanthanide ion occupies the A site and a magnetically silent tetravalent ion (Ti, Zr, Sn, etc.) occupies the B site. A classical problem related to the crystal growth of these compounds is sample quality. As refractory oxides, the traditional synthesis and crystal growth techniques require high temperatures, which creates two problems that are quite common and endemic to the pyrochlores. One is oxide lattice defects and the other is A/B site disorder. Both issues often occur in the same crystals at the same time and can have a significant impact on the study of physical properties. A hydrothermal approach can be applied to this problem. The relatively low crystal growth temperatures (600-720˚C) greatly minimize nonstoichiometry, lattice defects and A/B site disorder. The reduction of these pervasive imperfections significantly increases the quality of the observed physical property data. This talk will focus on the lanthanide stannates Ln2Sn2O7 as a prototype test bed of these systems. The stannates are among the few building blocks that can stabilize the pyrochlore structure type for all the lanthanides and they serve as an excellent proving ground for a wide range of physical properties. The synthesis and crystal growth of the lanthanide stannates will be reviewed, along with a discussion of some of the physical properties that result from the frustrated magnetic states of these low defect crystals.
The ICARUS collaboration has completed a search for muon-neutrino disappearance in the context of the 3+1 sterile neutrino model using data collected with the Booster Neutrino Beam at Fermilab during 2022-2023 (ICARUS Run 2). Events are reconstructed with two different reconstruction frameworks and we select events with 1 muon, at least 1 proton, and no pions in the final state. A new fitting framework, called PROfit, was developed for this analysis and used for the final results presented here. As a single detector oscillation search this analysis is systematics limited, but the tools shown here will serve as a building block for future SBN searches where systematics will be constrained by the addition of a near detector. I will discuss the details of the analysis with a focus on the fitting framework, mock data studies, and the final fitting procedure during the data unboxing.
This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations (PDEs) and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for implementing large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication.
The ICARUS collaboration has completed a search for muon-neutrino disappearance in the context of the 3+1 sterile neutrino model using data collected with the Booster Neutrino Beam at Fermilab during 2022-2023 (ICARUS Run 2). Events are reconstructed with two different reconstruction frameworks and we select events with 1 muon, at least 1 proton, and no pions in the final state. A new fitting framework, called PROfit, was developed for this analysis and used for the final results presented here. As a single detector oscillation search this analysis is systematics limited, but the tools shown here will serve as a building block for future SBN searches where systematics will be constrained by the addition of a near detector. I will discuss the details of the analysis with a focus on the fitting framework, mock data studies, and the final fitting procedure during the data unboxing.
Final Scientific/Technical Report for DOE Award DE-SC0019581, “TTDAQ: A Continuous Flow, Timing and Trigger DAQ System.” The report summarizes Telluric Labs’ Phase II STTR work developing silicon-photonic building blocks for a software-defined, continuous-flow, trigger-less data acquisition system for next-generation high-energy and nuclear-physics detectors. The project focused on radiation-hard photonic integrated circuits, remote optical illumination, dense wavelength-division multiplexing, and a differential microring-resonator transceiver architecture designed to improve high-speed optical link stability and bandwidth. The report describes project objectives, technical accomplishments, AIM Photonics tape-outs, bench characterization, radiation-hardness testing, deferred integration work, and potential applications beyond physics readout.
Achieving a better understanding of anion interactions both in solution and crystalline state was the overarching goal of this project. Anions are everywhere throughout Nature and play important roles in biological and environmental processes. They can be beneficial or deleterious or both in different situations and concentrations. For either reason it is important to have molecules that can bind anions for key needs that benefit society. However, recognition of specific anions is challenging due to the diffuse nature of their negative charge(s) as well as their various shapes and sizes. Understanding the basic properties of anions and how they interact with other molecules and ions in surrounding environments is key to selective recognition. In this project multi-tasking molecules for selective binding of targeted anions were designed to achieve cooperativity and synergism in one rather than multiple host molecules, including (1) cation:anion pair hosts for anions with charges of -2 or greater; (2) pH and redox activated hosts for on-off binding and release; and (3) multiple anion capture in extended host networks. Our design strategy was to combine the use of simple inexpensive building blocks and high yield synthetic pathways to provide economically feasible scale-up for applications. Oxoanions representing multiple shapes and charges were chosen based on having the potential for significant impact on DOE separations needs. Amide/amine-based macrocycles and urea/amine-based chelates and macrocycles with multiple hydrogen bonding sites provided the basic anion-binding frameworks. Successful multi-tasking outcomes were forthcoming in all three tasks. In Task 1, successful ion pair binding for anions with multiple charges was achieved. Furthermore, the ion pair molecules were capable of extended interactions through supramolecular intertwining, like fishing nets for capturing pools of fish (also fitting with Task 3). In Task 2, molecules were synthesized possessing on-off switches. These included a pH sensitive sensor for on-off binding of anions in general, as well as an electrochemical sensor selective for sulfate capture. Three new classes of extended anion host networks capable of binding multiple ions was a major outcome of Task 3. These systems included: anion sensitive, fluorescent organogels; channel-forming macrocycles for studying anion-water including larger macrocyclic cluster sandwiches; and, the offshoot of Task 1, fishing net ion-pair networks for higher valent anions. These strategies can be expanded in the future to other ions and molecules for a better understanding of intermolecular and interionic interactions.
Grid architecture (GA) is a concept that was developed to address the need for a comprehensive view of power grid challenges. GA can be viewed as a relatively consistent and fixed high-level approach; however, for any instantiation of grid structures, a combinatorial explosion results from each lower-layer expansion. This constitutes the main challenge with GA—it is a grid architect’s view of the system, which might not be very informative at the implementation level. Grid Architecture Mapping to Understand Transformation (GAMUT project) seeks to bridge that gap by integrating subject matter expertise across GA structures, providing users who lack expertise in GA approaches with valuable insights and informational materials. GAMUT seeks to answer feasibility questions for the approach. System-level expectations are that a GA baseline needs to be established in order for GA to be the common framework to which any lower layer approach is tied. This report explores a potential information ingestion and documentation framework to support GAMUT. The main concepts that enable the solution domain of GAMUT are discussed, and examples are provided. The solution domain leverages already-existing technology and concepts related to GA, knowledge management, and other relevant areas. To assess GAMUT building blocks and the overall approach, a feasibility assessment is proposed, rooted in systems engineering and GA architecture evaluation concepts.
The simulated pixelated calorimeter uses 0.1 mm × 0.1 mm silicon pixels (0.0001 cm²) silicon pixels as active layers — far finer than current concepts like HGCAL (~0.5 cm²) — improving energy resolution through much finer segmentation. While the standard resolution formalism fits three numbers to a handful of discrete test-beam energies, this work is a first try at a more data-efficient alternative: learning the full response shape continuously in energy with a mixture density network. This continuous, differentiable surrogate is a natural building block for fast simulation of extremely complex calorimeters.