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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Particulate Fuel Modeling of MC 2 -3 using Iterative Local Spatial Self-shielding Method

We report a new spatial self-shielding method for particulate fuels has been developed based on disadvantage factors and implemented in the MC 2 -3 code. This method named the iterative local spatial self-shielding (ILSS) method considers the shadowing effect of randomly distributed particles on spatial self-shielding in particles through a homogenized composition region added outside the particle of interest at the center. The self-shielded cross sections of the central particle are determined iteratively since they are used in determining the cross sections of the homogenized composition region. The ILSS method was verified for infinite stochastic medium problems of single and multiple types of particles, VHTR unit cell problems, and HTTR assembly problems. The verification test results show that the ILSS method accurately predicts the stochastic particle shadowing effect and reaction rates in particles, whereas the regular array model and the stochastic collision probability method underpredict the particle shadowing effect and overestimate reaction rates in particles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Direct extracellular electron transfer for high electricity production by a new type of marine microalgae Nannochloropsis sp. HDY2

A growing number of autotrophic microalgae have been found to have extracellular electron transfer (EET) capabilities. However, the mechanism and function of EET pathway in microalgae have not thoroughly investigated. This study presents evidence of direct extracellular electron transfer (DEET) for impressive electricity production by a new type of marine microalgae Nannochloropsis sp. HDY2 in the single and double-chamber photosynthetic microbial fuel cells (PFMCs). The electricity generation of Nannochloropsis sp. is strongly inhibited by photosynthetic inhibitors, suggesting that electrons involved in the extracellular electron transfer (EET) of Nannochloropsis sp. are derived from photosynthesis. The results of dialysis bag and supernatant experiments suggest that Nannochloropsis sp. produce electricity by DEET. The lack of typical redox peaks in cyclic voltammetry (CV) analysis and the appearance of extracellular filamentous component (EFC) around cells support this finding. Under high light conditions, the chlorophyll a and carotenoid content of Nannochloropsis sp. rapidly decreased while the fatty acid synthesis and electricity generation increased, suggesting that the DEET pathway play an active role in the resistance to photooxidation of Nannochloropsis sp. In conclusion, this study provides the first evidence that Nannochloropsis has the capability of performing EET and sheds new light on the positive role of DEET on the environmental adaptation of Nannochloropsis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Single-shot switching in Tb/Co-multilayer based nanoscale magnetic tunnel junctions

Magnetic tunnel junctions (MTJs) are elementary units of magnetic memory devices. For high-speed and low-power data storage and processing applications, fast reversal of the magnetization by an ultrashort laser pulse is extremely important. Here we demonstrate single-shot switching of Tb/Co-multilayer based nanoscale MTJs by combining the optical writing and the electrical read-out methods. A 90-fs-long laser pulse switches the magnetization of the storage layer (SL). The change in the tunneling magnetoresistance (TMR) between the SL and a reference layer (RL) is probed electrically across the oxide barrier. Single-shot switching is demonstrated by varying the cell diameter from 300 nm to 20 nm. The anisotropy, magnetostatic coupling, and switching probability exhibit cell-size dependence. By suitable association of laser fluence and magnetic field, successive commutation between high-resistance and low-resistance states is achieved. The nature of the magnetization reversal of both SL and RL in a continuous film is probed with a depth-resolved magneto-optical Kerr effect (MOKE) magnetometry. The ultrafast dynamics in the continuous full-MTJ stack is investigated with the time-resolved pump–probe technique. Our experimental findings provide strong support for the growing interest in ultrafast spintronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Secretory stimuli distinctly regulate insulin secretory granule maturation through structural remodeling

Insulin secretory granule (ISG) maturation is a crucial aspect of insulin secretion and glucose homeostasis. The regulation of this maturation remains poorly understood, especially how secretory stimuli affect ISG maturity and subcellular localization. In this study, we used soft X-ray tomography (SXT) to quantitatively map ISG morphology, density, and location in single INS-1E and mouse pancreatic β cells under the effect of various secretory stimuli. We found that the activation of glucokinase (GK), gastric inhibitory polypeptide receptor (GIPR), glucagon-like peptide-1 receptor (GLP-1R), and G protein-coupled receptor 40 (GPR40) promotes ISG maturation. Each stimulus induces unique structural remodeling in ISGs, by altering size and density, depending on the specific signaling cascades activated. These distinct ISG subpopulations mobilize and redistribute in the cell, altering the overall cellular structural organization. Our results provide insight into how current diabetes and obesity therapies impact ISG maturation and may inform the development of future treatments that target maturation specifically.

insulin granule maturation↗
DOE OSTI2021

The Surface Chemistry and Structure of Colloidal Lead Halide Perovskite Nanocrystals

Since the initial discovery of colloidal lead halide perovskite nanocrystals, there has been significant interest placed on these semiconductors because of their remarkable optoelectronic properties, including very high photoluminescence quantum yields, narrow size- and composition-tunable emission over a wide color gamut, defect tolerance, and suppressed blinking. These material attributes have made them attractive components for next-generation solar cells, light emitting diodes, low-threshold lasers, single photon emitters, and X-ray scintillators. While a great deal of research has gone into the various applications of colloidal lead halide perovskite nanocrystals, comparatively little work has focused on the fundamental surface chemistry of these materials. While the surface chemistry of colloidal semiconductor nanocrystals is generally affected by their particle morphology, surface stoichiometry, and organic ligands that contribute to the first coordination sphere of their surface atoms, these attributes are markedly different in lead halide perovskite nanocrystals because of their ionicity. Herein, emerging work on the surface chemistry of lead halide perovskite nanocrystals is highlighted, with a particular focus placed on the most-studied composition of CsPbBr 3 . We begin with an in-depth exploration of the native surface chemistry of as-prepared, 0-D cuboidal CsPbBr 3 nanocrystals, including an atomistic description of their surface termini, vacancies, and ionic bonding with ligands. We then proceed to discuss various post-synthetic surface treatments that have been developed to increase the photoluminescence quantum yields and stability of CsPbBr 3 nanocrystals, including the use of tetraalkylammonium bromides, metal bromides, zwitterions, and phosphonic acids, and how these various ligands are known to bind to the nanocrystal surface. To underscore the effect of post-synthetic surface treatments on the application of these materials, we focus on lead halide perovskite nanocrystal-based light emitting diodes, and the positive effect of various surface treatments on external quantum efficiencies. We also discuss the current state-of-the-art in the surface chemistry of 1-D nanowires and 2-D nanoplatelets of CsPbBr 3 , which are more quantum confined than the corresponding cuboidal nanocrystals but also generally possess a higher defect density because of their increased surface area-to-volume ratios.

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

Sub-10 nm upconversion nanocrystals for long-term single-particle tracking

Lanthanide-doped upconversion nanoparticles are attractive single-molecule imaging probes due to their high photostability and anti-Stokes luminescence. However, achieving both small particle size and strong brightness has remained a major challenge, as reducing size often leads to dimmer emission. Herein, we fabricate a sub-10 nm cascade actively protected upconversion nanoparticles, which shows a 33-fold enhanced upconversion efficiency at the single-particle level compared to larger ~19 nm conventional nanoparticles. Theoretical modeling and time-resolved measurements show that emission loss mainly comes from energy leakage of Er 3+ ions to surface defects. By introducing a NaYbF 4 layer as photon-harvesting and protective intermediate layer, we minimize this energy loss and significantly boost brightness. A monolayer of inert NaLuF 4 can effectively suppress the surface quenching to Yb 3+ . Using these ultra-small bright probes, we successfully tracked single epidermal growth factor receptor molecules on live cells for up to one hour, revealing dynamic switching between different diffusion modes.

Qiu, Xiaochen [Fudan Univ., Shanghai (China); Shen↗
DOE OSTI2023

Tuning the thermal activation atmosphere breaks the activity–stability trade-off of Fe–N–C oxygen reduction fuel cell catalysts

Fe–N–C catalysts are the most promising platinum group metal-free oxygen-reduction catalysts, but they suffer from a low density of active metal sites and the so-called activity–stability trade-off. Here we report an Fe–N–C catalyst prepared by adding an optimal amount of H 2 to the traditional inert atmosphere during the thermal activation. The presence of H 2 significantly increases the total density of FeN 4 sites, suppressing the unstable pyrrolic-N-coordinated S1 sites and favouring the stable pyridinic-N-coordinated S2 sites with shortened Fe–N bond lengths. Here, we propose that the intrinsically stable S2 sites are probably arranged in well-graphitized carbon layers, and the S1 sites exist in less-graphitized carbon. H 2 could remove unstable S1 sites and retain stable S2 sites during the pyrolysis to break the challenging activity–stability trade-off. The Fe–N–C catalyst in membrane electrode assemblies maintains a current density of 67 mA cm -2 at 0.8 V (H 2 –air) after 30,000 voltage cycles (0.60 to 0.95 V under H 2 –air), achieving encouraging durability and performance simultaneously.

25 ENERGY STORAGE↗
DOE OSTI2020

Parallel high-frequency magnetic sensing with an array of flux transformers and multi-channel optically pumped magnetometer for hand MRI application

Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A perspective on ordered vacancy compound and parent chalcopyrite thin film absorbers for photoelectrochemical water splitting

Chalcopyrites could fill the gap between the low-cost, poor-efficiency single junction metal oxide photoelectrochemical (PEC) water splitting cells and the high efficiency, yet costly III–V tandems. In this Perspective, we first review the key barriers that must be addressed by the community to enable economical chalcopyrite-based PEC water splitting. Then, we highlight how theoretical modeling can be used to identify promising ordered vacancy compound absorbers with improved energetics compared to their chalcopyrite parents. Finally, we describe how advanced spectroscopic analysis performed on chalcopyrite photocathodes after PEC testing uncovered a new passivation layer candidate for prolonged durability.

08 HYDROGEN↗
DOE OSTI2022

Lightweight jet reconstruction and identification as an object detection task

We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

High-pressure neutron diffraction at ultra-low temperature

Neutron diffraction is a powerful tool in the study of quantum materials and associated emergent phenomena as it allows for direct detection of magnetic structure even at ultra-low temperatures. As pressure can drive systems across quantum critical points and tune emergent behaviors at the quantum scale, coupling neutron diffraction with extreme conditions of high pressure at (ultra-)low temperatures has been a key research target over decades. This has proven challenging as magnetic neutron diffraction typically requires large sample volumes due to flux limitations, whereas high-pressure studies require small sample volumes due to pressure limitations. Combining these two contradictory requirements poses many difficulties, but also brings opportunities in research and development. Here, we address these shortcomings through the use of a diamond anvil cell equipped with polycrystalline anvils with relatively large sample volumes coupled with the high neutron flux made available by the high-pressure diffractometer of the Spallation Neutron Source. This particular diamond anvil cell configuration has previously proven useful for single-crystal samples and is here coupled with the ultra-low temperatures of a dilution refrigerator for the first time. Our study on a single crystal of Yb 2 ⁢O 3 successfully collected magnetic neutron diffraction data at 5.3 GPa below 200 mK inside a dilution refrigerator. We have performed a detailed analysis of the magnetic diffraction data to investigate the magnetic structure under pressure and observed a subtle shift in the transition temperature. This development will open the door for future studies on magnetic materials with these much-desired coupled extremes of high pressure and ultra-low temperatures.

Wu, Yan [Oak Ridge National Laboratory (ORNL), Oak↗
DOE OSTI2023

Fast neutron radiography-based on single-photon digital photosensor: concept and demonstration

For decades, fast neutron radiography performed using pulse-counting detectors employed PMTs coupled to plastic scintillator pixel arrays. SiPM-based systems are now sought to replace fragile PMTs, but conventional, “analog” SiPMs suffer from intrinsic limitations which limit their achievable performance. Among these limitations, a complete analog readout and digitizer chain is required, a counterintuitive approach when considering that the single-photon avalanche diode (SPAD), the basic unit cell of SiPMs, is a Boolean detector providing digital detection at the sensor level. This paper outlines a new concept for neutron radiography instrumentation by using photon-to-digital converters (PDCs, aka digital SiPMs), a fully digital solution to sense the scintillation light.

Pratte, Jean-Francois↗
DOE OSTI2020

Calorimetry with deep learning: particle simulation and reconstruction for collider physics

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

mesoflow [SWR-22-56]

Mesoflow is a continuum scale simulation tool developed specifically for modeling transport and chemistry at the mesoscale. Our solver utilizes Cartesian block-structured adaptive mesh refinement to resolve complex surface morphologies (of catalysts/biomass particles among others) directly obtained from X-ray tomography data. An immersed boundary based formulation enables rapid representation of complex geometries prevalent in most mesoporous interfaces. The solver is developed on top of open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Our flexible software framework enables integration of complex chemical mechanisms at heterogenous interfaces and time-split algorithms for circumventing highly disparate reaction and flow time-scales. Our current studies indicate a ten-fold performance gain by using graphics-processing-units (GPU) compared to a single processor for representative problem sizes (2 million cell mesh).

Sitaraman, Hariswaran↗
DOE OSTI2026

Improving MicroBooNE's Inclusive Single-Photon Search with Low-Energy Hadronic Identification

This analysis aims to further investigate MicroBooNE’s inclusive single-photon search results, which reported a 2.2$\sigma$ excess below 600 MeV in shower energy for events with no reconstructed protons using roughly half of MicroBooNE's dataset. Taken together with MiniBooNE’s long-standing low-energy excess and MicroBooNE’s recent electron-like search results showing no observable excess with respect to Standard Model predictions, this result provides strong impetus for expanded exploration of the single-photon channel in Fermilab’s short-baseline liquid-argon time projection chamber (LArTPC) experiments. By identifying and classifying isolated MeV-scale energy depositions, or blips, in the vicinity of single-photon events selected by the Wire-Cell reconstruction framework, we establish a more comprehensive labeling scheme for nearby hadronic content. In particular, blips found backwards along the shower axis indicate the presence of previously-unidentified final-state protons, while elevated blip counts at wide angles signal the presence of final-state neutrons. By applying this new technique to its full dataset, MicroBooNE will perform a purer and higher-statistics test of the truly isolated nature of its modest photon-like excess, furthering its hunt for the presence of unexpected new physics beyond the Standard Model.

Andrade Aldana, Diego [Los Alamos; IIT, Chicago (m↗
DOE OSTI2026

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2022

Mesoflow: An Open-Source Reacting Flow Solver for Catalysis at Mesoscale

We present the capabilities and software performance metrics of our open-source continuum solver for catalysis, Mesoflow, developed specifically for modeling transport and chemistry at the mesoscale. Our solver utilizes Cartesian block-structured adaptive mesh refinement to resolve complex catalyst surface morphologies directly obtained from X-ray tomography data. An immersed boundary based formulation enables rapid representation of complex geometries prevalent in most mesoporous catalyst interfaces. The solver is developed on top of open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Our flexible software framework enables integration of complex chemical mechanisms at heterogenous interfaces and time-split algorithms for circumventing highly disparate reaction and flow time-scales. Our current studies indicate a ten-fold performance gain by using graphics-processing-units (GPUs) compared to a single processor for representative problem sizes (2 million cell mesh). We will also present a brief introduction on how to build and use this software for application problems pertaining to catalytic upgrading and gas transport within porous catalyst particles.

adaptive meshing↗
DOE OSTI2026

Inclusive Search for Anomalous Single-Photon Production in MicroBooNE

We present an inclusive search for anomalous production of single-photon events from neutrino interactions in the MicroBooNE experiment. The search and its signal definition are motivated by the previous observation of a low-energy excess of electromagnetic shower events from the MiniBooNE experiment. We use the Wire-Cell reconstruction framework to select a sample of inclusive single-photon final-state interactions with a final efficiency and purity of 7.0% and 40.2%, respectively. We leverage simultaneous measurements of sidebands of charged current 𝜈 𝜇 interactions and neutral current interactions producing 𝜋 0 mesons to constrain signal and background predictions and reduce uncertainties. We perform a blind analysis using a dataset collected from February 2016 to July 2018, corresponding to an exposure of 6.34 × 10 20 protons on target from the Booster Neutrino Beam at Fermilab. In the full signal region, we observe agreement between the data and the prediction, with a goodness-of-fit 𝑝 value of 0.11. We then isolate a subsample of these events containing no visible protons, and observe 93 ± 22⁢(stat) ±35⁢(syst) data events above prediction, corresponding to just above 2⁢𝜎 local significance, concentrated at shower energies below 600 MeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗