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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding interactions between biomolecules and two-dimensional nanomaterials using in silico microscopes

We report two-dimensional (2D) nanomaterials such as graphene are increasingly used in research and industry for various biomedical applications. Extensive experimental and theoretical studies have revealed that 2D nanomaterials are promising drug delivery vehicles, yet certain materials exhibit toxicity under biological conditions. So far, it is known that 2D nanomaterials possess strong adsorption propensities for biomolecules. To mitigate potential toxicity and retain favorable physical and chemical properties of 2D nanomaterials, it is necessary to explore the underlying mechanisms of interactions between biomolecules and nanomaterials for the subsequent design of biocompatible 2D nanomaterials for nanomedicine. The purpose of this review is to integrate experimental findings with theoretical observations and facilitate the study of 2D nanomaterial interaction with biomolecules at the molecular level. We discuss the current understanding and progress of 2D nanomaterial interaction with proteins, lipid membranes, and DNA based on molecular dynamics (MD) simulation. In this review, we focus on the 2D graphene nanosheet and briefly discuss other 2D nanomaterials. With the ever-growing computing power, we can image nanoscale processes using MD simulation that are otherwise not observable in experiment. We expect that molecular characterization of the complex behavior between 2D nanomaterials and biomolecules will help fulfill the goal of designing effective 2D nanomaterials as drug delivery platforms.

2D nanomaterials↗
DOE OSTI2022

Dynamics and growth rate implications of ribosomes and mRNAs interaction in E. coli

Understanding how cells grow and adapt under various nutrient conditions is pivotal in the study of biological stoichiometry. Recent studies provide empirical evidence that cells use multiple strategies to maintain an optimal protein production rate under different nutrient conditions. Mathematical models can provide a solid theoretical foundation that can explain experimental observations and generate testable hypotheses to further our understanding of the growth process. In this study, we generalize a modeling framework that centers on the translation process and study its asymptotic behaviors to validate algebraic manipulations involving the steady states. Using experimental results on the growth of E. coli under C-, N-, and P-limited environments, we simulate the expected quantitative measurements to show the feasibility of using the model to explain empirical evidence. Our results support the findings that cells employ multiple strategies to maintain a similar protein production rate across different nutrient limitations. Moreover, we find that the previous study underestimates the significance of certain biological rates, such as the binding rate of ribosomes to mRNA and the transition rate between different ribosomal stages. Furthermore, our simulation shows that the strategies used by cells under C- and P-limitations result in a faster overall growth dynamics than under N-limitation. In conclusion, the general modeling framework provides a valuable platform to study cell growth under different nutrient supply conditions, which also allows straightforward extensions to the coupling of transcription, translation, and energetics to deepen our understanding of the growth process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Membrane-Confined Iron Oxychloride Nanocatalysts for Highly Efficient Heterogeneous Fenton Water Treatment

Heterogeneous advanced oxidation processes (AOPs) allow for the destruction of aqueous organic pollutants via oxidation by hydroxyl radicals ( • OH). However, practical treatment scenarios suffer from the low availability of short-lived • OH in aqueous bulk, due to both mass transfer limitations and quenching by water constituents, such as natural organic matter (NOM). In this work, we overcome these challenges by loading iron oxychloride catalysts within the pores of a ceramic ultrafiltration membrane, resulting in an internal heterogeneous Fenton reaction that can degrade organics in complex water matrices with pH up to 6.2. With • OH confined inside the nanopores (~ 20 nm), this membrane reactor completely removed various organic pollutants with water fluxes of up to 100 L m –2 h –1 (equivalent to a retention time of 10 s). This membrane, with a pore size that excludes NOM (>300 kDa), selectively exposed smaller organics to •OH within the pores under confinement and showed excellent resiliency to representative water matrices (simulated surface water and sand filtration effluent samples). Moreover, the membrane exhibited sustained AOPs (>24 h) and could be regenerated for multiple cycles. Our results suggest the feasibility of exploiting ultrafiltration membrane-based AOP platforms for organic pollutant degradation in complex water scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Artificial Graphene Nanoribbons: A Test Bed for Topology and Low-Dimensional Dirac Physics

Here, we synthesize artificial graphene nanoribbons by positioning carbon monoxide molecules on a copper surface to confine its surface state electrons into artificial atoms positioned to emulate the low-energy electronic structure of graphene derivatives. We demonstrate that the dimensionality of artificial graphene can be reduced to one dimension with proper "edge" passivation, with the emergence of an effectively gapped one-dimensional nanoribbon structure. These one-dimensional structures show evidence of topological effects analogous to graphene nanoribbons. Guided by first-principles calculations, we spatially explore robust, zero-dimensional topological states by altering the topological invariants of quasi-one-dimensional artificial graphene nanostructures. The robustness and flexibility of our platform allow us to toggle the topological invariants between trivial and nontrivial on the same nanostructure. Ultimately, we spatially manipulate the states to understand fundamental coupling between adjacent topological states that are finely engineered and simulate complex Hamiltonians.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Identifying Entangled Physics Relationships Through Sparse Matrix Decomposition to Inform Plasma Fusion Design

We report a sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulation codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g., neutron yield, neutron velocity, areal density) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield, such as picket power and laser energy, variables that represent a dramatic change of the ICF design, such as number of pulse steps, are also very important. The obtained sparse components are then used to train a random forest (RF) regression surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF trained using all the design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulation results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Understanding extraction limits of plasma cathodes with experiment and simulation

The project is focused on computations for enhanced ionization near the exit orifice of a plasma cathode and how the cathode extraction electrode geometry and stray magnetic field structure influence available extractable current. The computational effort will take place at the Princeton Collaborative Research Laboratory (PCRF). The specific modeling platform to be utilized from PCRF is the electrostatic direct implicit particle in cell (EDIPIC) code. EDIPIC will be used to study ionization processes in the electron extraction region of the cathode. The control of electron energy is a key aspect to minimizing power losses in the plasma. Exiting electrons ionize gas leaving the orifice and in the process produce the dense plasma from which large amounts of current can be extracted. Project activities include studying processes that determine the energy distribution of the exiting electrons thus providing insight into how to optimize the source. The model will be validated and developed using experimental measurements as warranted. The familiarity and expertise of the team at PPPL with this type of plasma and the challenges posed greatly enhances the likelihood of gaining insight into the extraction sheath under the duration of the proposed activity. The effort is expected to shed a great deal of insight into electron extraction processes and pathways for optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Comparison of Coupled and Uncoupled Modeling of Floating Wind Farms with Shared Anchors

As design options for floating wind farms continue to be explored, shared (or multiline) anchors that secure mooring lines from multiple turbines remain a promising technology that can potentially reduce the number of anchors and overall mooring costs. This study evaluates two methods for analyzing the loads on shared anchors: one in which floating offshore wind turbines are simulated individually (using the software OpenFAST), and one in which an entire floating wind farm is simulated collectively (using the software FAST.Farm). A three-line shared anchor is evaluated for multiple loading scenarios in deep water, using the International Energy Agency 15 MW turbine on the VolturnUS-S semisubmersible platform. While the two methods produce broadly comparable results, the coupled wave loading on platforms within the farm results in wave force cancellations and amplifications that decrease multiline force directional ranges and increase multiline force extreme values (up to 7%) and standard deviations (up to 11%) for wave-driven load cases. The inclusion of wakes in FAST.Farm also reduces the net load on the shared anchor due to the velocity deficit, leading to larger differences between OpenFAST and FAST.Farm (up to 3% difference in mean loads) for load cases with operational turbines.

17 WIND ENERGY↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Quantum Enhanced Dark-Matter Search with Entangled Fock States in High-Quality Cavities

We present a quantum-enhanced protocol for detecting wave-like dark matter using an array of $N$ entangled superconducting cavities initialized in an $m$-photon Fock state. By distributing and recollecting the quantum state with an entanglement-distribution operation, the scan rate scales as $N^2(m+1)$ while thermal excitation is the dominant background, significantly outperforming classical single-cavity methods under matched conditions. We evaluate the robustness of our scheme against additional noise sources, including decoherence and beamsplitter infidelity, through theoretical analysis and numerical simulations. In practice, the key requirements, namely high-Q superconducting radio-frequency cavities that support long integration times, high-fidelity microwave beamsplitters, and universal cavity control, are already available on current experimental platforms, making the protocol experimentally feasible.

Freiman, Benjamin [Fermilab]↗
DOE OSTI2021

Improving scalability of electronic structure code for molecular simulations in the presence of environment

A scalable density functional electronic code with Gaussian basis set, called UTEP-NRLMOL, is developed to perform simulations of molecular systems in the presence of the environment with particular attention to the memory requirements. In the electronic structure calculations, the memory and computation time are proportional to the number of atoms. Memory requirements for density functional calculations scale as N*N, where N is the number of atoms. While the recent advances in HPC offer platforms with large numbers of cores, the limited amount of memory available on a given node and poor scalability of the electronic structure codes hinder their efficient usage of these platforms. We have introduced new scaling and parallelization paradigms using MPI-3 shared-memory functionality combined with usage of sparse algebra and storage of matrices in sparse format. This extends the range of applicability of the UTEP-NRLMOL code to large systems over 10,000 atoms, or using up to 67,000 basis functions, and making use of HPC architectures using over 6,000 processors utilizing all available cores. We have also interfaced code with effective fragment potential and polarizable continuum model libraries. The code was used in simulations of several applications which are published in reputed scientific journals.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Harnessing Peptide Binding to Capture and Reclaim Phosphate

With rising consumer demands, society is tapping into wastewater as an innovative source to recycle depleting resources. Novel reclamation technologies have been recently explored for this purpose, including several that optimize natural biological processes for targeted reclamation. However, this emerging field has a noticeable dearth of synthetic material technologies that are programmed to capture, release, and recycle specified targets; and of the novel materials that do exist, synthetic platforms incorporating biologically inspired mechanisms are rare. We present here a prototype of a materials platform utilizing peptide amphiphiles that has been molecularly engineered to sequester, release, and reclaim phosphate through a stimuli-responsive pH trigger, exploiting a protein-inspired binding mechanism that is incorporated directly into the self-assembled material network. This material is able to harvest and controllably release phosphate for multiple cycles of reuse, and it is selective over nitrate and nitrite. We have determined by simulations that the binding conformation of the peptide becomes constrained in the dense micelle corona at high pH such that phosphate is expelled when it otherwise would be preferentially bound. However, at neutral pH, this dense structure conversely employs multichain binding to further stabilize phosphate when it would otherwise be unbound, opening opportunities for higher-order conformational binding design to be engineered into this controllably packed corona. With this work, we are pioneering a new platform to be readily altered to capture other valuable targets, presenting a new class of capture and release materials for recycling resources on the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Protection against Chemical Warfare Agents and Biological Threats Using Metal–Organic Frameworks as Active Layers

The SARS-CoV-2 pandemic outbreak and the unfortunate misuse of toxic chemical warfare agents (CWAs) highlight the importance of developing functional materials to protect against these chemical and pathogen threats. Metal–organic frameworks (MOFs), which comprise a tunable class of crystalline porous materials built from inorganic nodes and organic linkers, have emerged as a class of heterogeneous catalysts capable of rapid detoxification of multiple classes of these harmful chemical or biological hazards. In particular, zirconium-based MOFs (Zr-MOFs) feature Lewis acidic nodes that serve as active sites for a wide range of catalytic reactions, including the hydrolysis of organophosphorus nerve agents within seconds in basic aqueous solutions. In addition, postsynthetic modification of Zr-MOFs enables the release of active species capable of reacting with and deactivating harmful pathogens. Despite this impressive performance, utilizing Zr- MOFs in powder form is not practical for application in masks or protective uniforms. To address this challenge, our team sought to develop MOF/fiber composite systems that could be adapted for use under realistic operating conditions to protect civilians, military personnel, and first responders from harmful pathogens and chemical warfare agents. Over the last several years, our group has designed and fabricated reactive and biocidal MOF/fiber composites that effectively capture and deactivate these toxic species. In this Account, we describe the evolution of these porous and reactive MOF/fiber composites and focus on key design challenges and considerations. First, we devised a scalable method for the integration of Zr-MOFs onto textile substrates using aqueous precursor solutions and without using pretreated textiles, highlighting the potential scalability of this method. Moving beyond standard textiles, we also developed a microbial synthesis strategy to prepare hierarchically porous MOF/bacterial cellulose nanofiber composite sponges that can both capture and detoxify nerve agents when exposed to contaminated gas flows. The mass loading of the MOF in the nanofibrous composite sponge is up to 90%, affording higher work capacities compared to those of textile-fiber-based composites with relatively lower MOF loadings. Next, we demonstrated that heterogeneous polymeric bases are suitable replacements for volatile liquid bases typically used in solution-phase reactions, and we showed that these composite systems are capable of effectively hydrolyzing nerve agents in the solid state by using only water that is present as humidity. Moreover, incorporating a reactive dye precursor into the composite affords a dual function sensing and detoxifying material that changes color from white to orange upon reaction with the byproduct following nerve agent hydrolysis, demonstrating the versatility of this platform for use in decontamination applications. We then created chlorine-loaded MOF/fiber composites that act as biocidal and reactive textiles that are capable of not only detoxifying sulfur-mustard-based chemical warfare agents and simulants but also deactivating both bacteria and the SARS-CoV-2 virus within minutes of exposure. Lastly, we synthesized a mixed-metal Ti/Zr-MOF coating on cotton fibers to afford a photoactive biocidal cloth that shows fast and broad-spectrum biocidal performance against viruses and Gram-positive and Gram-negative bacteria under visible light irradiation. Given the tunable, multifunctional nature of these MOF/fiber composites, we believe that this Account will offer new insights for the rational design and preparation of functional MOF/fiber composites and pave the way toward the development of next-generation reactive and protective textiles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solving the Bernstein-Vazirani problem using Majorana-based topological quantum algorithms

Executing quantum algorithms using Majorana zero modes—a major milestone for the field of topological quantum computing—requires a platform that can be scaled to large quantum registers, can be controlled in real time and space, and a braiding protocol that uses the unique properties of these exotic particles. Here, we demonstrate the first successful simulation of a Majorana-based, fault-tolerant quantum algorithm to solve the Bernstein-Vazirani problem in two-dimensional magnet-superconductor hybrid structures from initialization to read-out of the final many-body state. Utilizing the Majorana zero modes’ topological properties, we introduce an optimized braiding protocol for the algorithm and a scalable architecture for its implementation with an arbitrary number of qubits. We visualize the algorithm protocol in real time and space by computing the non-equilibrium density of states, which is proportional to the time-dependent differential conductance, and the non-equilibrium charge density, which assigns a unique signature to each final state of the algorithm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Metasurface-based ultra-lightweight high-gain off-axis flat parabolic reflectarray for microwave beam collimation/focusing

Abstract Ultra-lightweight deployable antennas with high-gain are pivotal communication components for small satellites, which are intrinsically constrained in size, weight, and power. In this work, we design and demonstrate metasurface-based ultra-lightweight flat off-axis reflectarrays for microwave beam collimation and focusing, similar to a parabolic dish-antenna. Our ultra-thin reflectarrays employ resonators of variable sizes to cover the full 2 π phase range, and are arranged on the metasurface to realize a two-dimensional parabolic focusing phase distribution. We demonstrate a 30° off-axis focusing reflector that exhibits a measured gain of 27.5 dB at the central operating frequency of 11.8 GHz and a 3 dB directionality < $$\pm $$ ± 1.6°. Furthermore, we carry out full-wave simulations of the reflectarray, showing high gain of the beam focusing/collimation functionality, in good agreement with measurements. The demonstrated reflectarrays will enable low-cost, lightweight, and high-gain deployable transceivers for small-satellite platforms.

42 ENGINEERING↗
DOE OSTI2025

Candidate strongly lensed type Ia supernovae in the Zwicky Transient Facility archive

Gravitationally lensed type Ia supernovae (glSNe Ia) are unique astronomical tools that can be used to study cosmological parameters, distributions of dark matter, the astrophysics of the supernovae, and the intervening lensing galaxies themselves. A small number of highly magnified glSNe Ia have been discovered by ground-based telescopes such as the Zwicky Transient Facility (ZTF), but simulations predict that a fainter, undetected population may also exist. We present a systematic search for glSNe Ia in the ZTF archive of alerts distributed from June 1 2019 to September 1 2022. Using the AMPEL platform, we developed a pipeline that distinguishes candidate glSNe Ia from other variable sources. Initial cuts were applied to the ZTF alert photometry (with constraints on the peak absolute magnitude and the distance to a catalogue-matched galaxy, as examples) before forced photometry was obtained for the remaining candidates. Additional cuts were applied to refine the candidates based on their light curve colours, lens galaxy colours, and the resulting parameters from fits to the SALT2 SN Ia template. The candidates were also cross-matched with the DESI spectroscopic catalogue. Seven transients were identified that passed all the cuts and had an associated galaxy DESI redshift, which we present as glSN Ia candidates. Although superluminous supernovae (SLSNe) cannot be fully rejected as contaminants, two events, ZTF19abpjicm and ZTF22aahmovu, are significantly different from typical SLSNe and their light curves can be modelled as two-image glSN Ia systems. From this two-image modelling, we estimate time delays of 22 ± 3 and 34 ± 1 days for the two events, respectively, which suggests that we have uncovered a population of glSNe Ia with longer time delays. The pipeline is efficient and sensitive enough to parse full alert streams. It is currently being applied to the live ZTF alert stream to identify and follow-up future candidates while active. This pipeline could be the foundation for glSNe Ia searches in future surveys, such as the Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
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↗