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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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Probing Cellular Activity Via Charge‐Sensitive Quantum Nanoprobes
Nitrogen‐vacancy (NV) based quantum sensors hold great potential for real‐time single‐cell sensing with far‐reaching applications in fundamental biology and medical diagnostics. Although highly sensitive, the mapping of quantum measurements onto cellular physiological states has remained an exceptional challenge. Here, we introduce a novel quantum sensing modality capable of detecting changes in cellular activity. Our approach is based on the detection of environment‐induced charge depletion within an individual particle that, owing to a previously unaccounted transverse dipole term, induces systematic shifts in the zero‐field splitting (ZFS). Importantly, these charge‐induced shifts serve as a reliable indicator for lipopolysaccharide (LPS)‐mediated inflammatory response in macrophages. Furthermore, we demonstrate that surface modification of our diamond nanoprobes effectively suppresses these environment‐induced ZFS shifts, providing an important tool for differentiating electrostatic shifts caused by the environment from other unrelated effects, such as temperature variations. Notably, this surface modification also leads to significant reductions in particle‐induced toxicity and inflammation. Our findings shed light on systematic drifts and sensitivity limits of NV spectroscopy in a biological environment with ramifications for the critical discussion surrounding single‐cell thermogenesis. Notably, this work establishes the foundation for a novel sensing modality capable of probing complex cellular processes through straightforward physical measurements.
Enhancing charge ratio sensitivity to hadronization effects via jet selections on resolved SoftDrop splitting
The study of quantum chromodynamics (QCD) at ultrarelativistic energies can be performed in a controlled environment through lepton-hadron deep inelastic scatterings. In such collisions, the high-energy partonic emissions that follow from the ejected hard partons are accurately described by perturbative QCD. However, the lower energy scales at which quarks and gluons experience color confinement, i.e., hadronization mechanism, fall outside the validity regions for perturbative calculations, requiring phenomenological models tuned to data to describe it. As such, hadronization physics cannot be currently derived from first principles alone. Monte Carlo event generators are useful tools to describe these processes as they simulate both the perturbative and the nonperturbative interactions, with model-dependent energy scales that control parton dynamics. This work employs jets—experimental reconstructions of final-state particles likely to have a common partonic origin—to inspect this transition further. Although originally proposed to circumvent hadronization effects, we show that jets can be utilized as probes of nonperturbative phenomena via their substructure. The charge correlation ratio was recently shown to be sensitive to hadronization effects. Our work further improves this sensitivity to nonperturbative scales by introducing a new selection based on the relative placement of the within the clustering tree, defined as the unclustering that resolves the jet’s leading charged particles. Published by the American Physical Society 2025
Exploring the Photophysics of N-Type Polymer N2200
The polymer interphase is a complex environment comprised of polymer chains, solvent molecules, and electrolyte ions. However, the microenvironments that these ingredients produce is both poorly-understood and difficult to investigate given the system's inherent structural heterogeneity. Nonetheless, a fundamental understanding of the structure, electrochemical behavior, and excited-state dynamics is a key prerequisite to optimizing devices based on such a system - for instance, photoelectrochemical cells for the direct, light-driven production of solar hydrogen, which may minimize efficiency losses compared to sequential light harvesting and electrochemical hydrogen production in separate but linked devices. This work combines transient absorption (TA) spectroscopy, which is a powerful tool for the observation of a variety of excited states on a femto- to microsecond timescale, with time-resolved microwave conductivity (TRMC) spectroscopy, which selectively probes the dynamics of free charge carriers (FC), of a blend of a donor polymer (PTB7-Th) and acceptor polymer (N2200) in a bulk heterojunction. Initial results indicate an extension of FC lifetime and homogenization of the microenvironment on a whole-film level, not just at the surface of the polymer; however, many questions are still under active investigation, including the changes in energy of the involved polaron states, effective conjugation length, FC mobility and migration distance, etc. Additional experiments regarding the photoinitiated behavior of the blend in different environments and on different timescales are ongoing.
Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors
Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.
The inertial confinement fusion experimental platform and diagnostics for studies of nuclear reactions relevant to nuclear astrophysics
High energy density plasmas generated in laser-driven inertial confinement fusion implosions provide unparalleled laboratory conditions for studying stellar-relevant nuclear reactions: plasma environment; hot and dense; uniquely high achievable neutron flux. These experiments have the potential to address long-standing questions about plasma effects on nuclear reactions hitherto experimentally inaccessible, including nuclear rates with thermally distributed reactants, plasma screening, and reactions involving nuclei in excited states. The National Ignition Facility (NIF) and OMEGA lasers are two primary facilities for executing experiments of this type. Existing and future nuclear diagnostics, along with supporting diagnostics to characterize the platform, enable exploitation of these plasmas for such nuclear astrophysics-relevant experiments. Here, this review describes the nuclear diagnostic capabilities currently available for these types of experiments at the NIF and OMEGA, including neutron time-of-flight spectrometers, charged-particle detectors, gamma detectors and radiochemistry diagnostics, and briefly summarizes other available diagnostic capabilities used for platform characterization. Enabling tools not yet available are also identified, including a rapid radioactive sample retrieval system, a low-energy neutron spectrometer and a high-efficiency gamma spectrometer.
Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features
Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.
Analyzing School Bus Electrification in Richmond, Virginia
School buses are an essential component of the transportation infrastructure, serving as a lifeline for students across the globe. However, the widespread use of diesel school buses has raised concerns about the health impact on millions of students exposed to harmful emissions daily. Recognizing this issue, school districts worldwide are urgently seeking cleaner energy alternatives. Electric school buses emerge as an environmentally friendly and sustainable option, fostering a healthier environment for both students and communities. However, school bus electrification faces the challenges of high upfront cost, cumbersome charging management, and constraints from power grids. To help school bus operators address those challenges, this study presents a data-driven analysis for school bus electrification. This study considered a real-world school bus system in Richmond, VA, and developed a mathematical programming model to analyze the system design, charging strategies, and charging load profiles for the electrification scenario. The study evaluated different charging strategies based on model outcomes, aiming to optimize efficiency and effectiveness. Ultimately, this research generated electric school bus charging demand profiles under various scenarios, shedding light on the feasibility and implications of transitioning to electric-powered school buses.