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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

Neutrino Interaction Identification for the DUNE Trigger

The DUNE will be a long baseline neutrino oscillation experiment using a high purity muon neutrino beam and near detector, both located at the FNAL, and a far detector hosted 1300 km downstream at the SURF. The 10 kt fiducial mass of LAr will allow DUNE to have a rich off-beam neutrino physics programme, including the study of neutrino signals from core collapse supernovae. The SP DUNE FD module will read out ionisation data at a rate of 1.2 TB\textsuperscript{-1} whilst only a total data volume of 30 PB per year can be permanently stored. DUNE will make use of FPGA resources in the front-end of the DAQ as part of the necessitated triggering system. This thesis presents a validation study of the FPGA-based TPG in the front-end DAQ using data collected by the ProtoDUNE experiment hosted at the CERN. The FPGA-based TPG was utilised as the first stage of a baseline SNB trigger whose performance was evaluated using simulated neutrino interactions for a $11.2 m_\odot$ progenitor star. The efficiency of the baseline SNB trigger was determined to have a lower limit of $97.7\substack{+ 0.2\\ -0.3}\%$ for supernovae at a distance of 20kpc, achieving the technical requirements set out for DUNE. To improve the performance of the SNB trigger at greater SNB distances, the use of a bounding box proposal network, YOLOv3, was explored. This was found to improve the efficiency of the SNB trigger to 100\% up to the far side of the Milky Way galaxy and to $92.5\substack{+ 0.5\\ -0.5}\%$ at the Large Magellanic Cloud.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Investigating Electro-Nuclear Interactions in a New Dark Matter Search

Electro-nuclear (EN) interactions are interactions in which an incident electron collides with a nucleus, scattering the electron and creating byproduct particles. Such interactions are of interest to neutrino physicists, who use EN interactions to inform model building of neutrino-nucleus interactions. The Light Dark Matter Experiment (LDMX) is a small-scale, fixed-target, electron beam experiment which seeks to probe for dark matter and mediator particle production in the sub-GeV mass region. The 8GeV LDMX electron beam will serve as an opportunity to study electro-nuclear interactions in their final states in the multi-GeV region. LDMX’s missing energy trigger however, will not be sufficient to efficiently capture EN interactions. An additional trigger needs to be created. Using simulated background events, including EN interactions, for the LDMX experiment, a trigger on momentum was developed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Absolute decay counting of 146 Sm with 4π cryogenic microcalorimetry

We present a methodology for absolute activity counting of long-lived isotopes based on cryogenic Decay Energy Spectroscopy. A 146 Sm source was produced at the TRIUMF Laboratory and then processed and purified at Lawrence Livermore National Laboratory, yielding a pure sample. The source was embedded within a 4π thermal absorber coupled to a magnetic microcalorimeter achieving nearly 100% counting efficiency. Experimental uncertainties were studied and modeled, including thermal coupling of the source to the absorber, pulse pile-up, trigger, and event selection efficiencies. Here, the absolute activity of the pure 146 Sm source was measured to better than 1% uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Force-Triggered, Biobased Sealants for Prefabricated Building Components: Toward Improved Efficiency and Performance

The prefabricated building construction industry has made extensive progress in expediting the manufacture of prefabricated components at off-site plants. However, the sealing of joints between these components, which is crucial to ensuring the weatherproofing of the assembly, still represents a labor-intensive, on-site effort that relies on the manual installation of tapes and caulks. Here, to reduce work at the jobsite and improve the airtightness and waterproofness of building envelopes, we developed a sealant that can be installed at the plant on prefab components and have the curing reaction triggered at the jobsite by using microencapsulation technology to separate the reactive agents. A series of force-triggered, high-strength, and fast-curing sealants derived from biobased feedstocks were developed, which consist of a biobased epoxy agent encapsulated in a polymer shell, embedded in a biobased amine curing agent. The shell of the microcapsules allows an effective separation of the reactive species in the one-part sealant, allowing shelf stability to an otherwise fast-curing system as well as improving the hydrophobicity of the whole system. When force activates and breaks the microcapsules, the highly reactive epoxy and amine mix and cure, exhibiting peel strength values of up to 143 ppi (pounds per inch). The hydrophobicity of the sealants allows them to retain up to 94% of the original peel strength after complete submersion in water for 24 h, showcasing the water resistivity of the sealant system. The open-air shelf stability of the sealant complex is demonstrated by the obtention of peel strength values of ∼16 ppi when triggering the curing reaction even after being exposed 8 months to open air and humidity. The successful on-demand triggering of curing reactions and the shelf stability provide efficacy of these force-triggered sealants for installation on prefabricated components, storage for months prior to delivery, and assembly at a jobsite. These force-triggered biobased sealants for prefabricated buildings can result in lower installation time and cost and better performance than tapes and caulks at the jobsite.

biobased↗
DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗
DOE OSTI2023

End-to-End Pipeline for Trigger Detection on Hit and Track Graphs

There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. Here, the triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Investigating Electro-Nuclear Interactions in a New Dark Matter Search

Electro-nuclear (EN) interactions are interactions in which an incident electron collides with a nucleus, scattering the electron and creating byproduct particles. Such interactions are of interest to neutrino physicists, who use EN interactions to inform model building of neutrino-nucleus interactions. The Light Dark Matter Experiment (LDMX) is a small-scale, fixed-target, electron beam experiment which seeks to probe for dark matter and mediator particle production in the sub-GeV mass region. The 8GeV LDMX electron beam will serve as an opportunity to study electro-nuclear interactions in final states in the multi-GeV region. LDMX s missing energy trigger for dark matter interactions however, will not be sufficient to efficiently capture EN interactions. An additional trigger is needed. Using simulated events, that included background and EN interactions, a trigger on momentum was developed.

Croteau, Beatrice↗
DOE OSTI2023

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta ($\beta_\textrm{N}$) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance $\beta_\textrm{N}$ efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same $\beta_\textrm{N}$ by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing $\beta_\textrm{N}$ during the suppression phase and sustaining the $\beta_\textrm{N}$-enhanced suppression state by optimizing the RMP strength. The integrated process achieves $\beta_\textrm{N}$ up to ~2.65 during the suppression phase, which is ~10% higher than the previous KSTAR record but ~6% lower than the target of the K-DEMO first phase ($\beta_\textrm{N}$ = 2.8), and maintains the suppression phase above the lower limit of target $\beta_\textrm{N}$ (= 2.4) for ~4 s (~60$\tau_\textrm{E}$). In addition to $\beta_\textrm{N}$ enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of $\beta_\textrm{N}$ compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of $\beta_\textrm{N}$ and the electron temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

PV-Finder: ML Based Algorithm for Primary Vertex Identification

he CMS detector at the High-Luminosity Large Hadron Collider (HL-LHC) will operate in challenging conditions with expected pile-up of up to 200 collisions per bunch crossing, necessitating the development of a more resilient primary vertex (PV) reconstruction method to ensure the integrity of data analysis and the efficiency of the CMS triggering system. This contribution describes preliminary studies on a new ML based PV-Finder method for PV identification. The method is based on a model trained using Kernel Density Estimations (KDEs) derived from the positions of reconstructed tracks at the beamline, incorporating uncertainties from track parameters. It also utilizes target histograms, modeled as Gaussian distributions centered on the actual ground truth values of specific primary vertices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Force-triggered, Bio-based, Sealants for Prefabricated Building Components: Towards Improved Efficiency, Performance and Sustainability

The prefabricated building construction industry has made extensive progress in expediting the manufacture of prefabricated components at offsite plants. However, this progress has not translated to the assembly of the prefabricated components at the construction site. Case in point, sealing the joints between components to prevent air leaks requires the manual application of tape, caulk, or spray foam at the jobsite, and performance is highly dependent on the skills of the installer. To reduce assembly time and improve assembly quality of prefabricated components, we developed a sealant that can be installed at the plant and triggered at the jobsite. Additionally, we used this opportunity to lower the use of fossil fuel derived feedstocks and introduced bio-based alternatives to decrease the embodied carbon of the new sealant. We evaluated a series of force-triggered, bio-based, high strength, and fast curing sealants, consisting of a one-part heterogeneous system. These sealants are derived from formulations with ≥80% of biogenic carbon, consisting of a cardanol derived diepoxy that is microencapsulated in a polymer shell and embedded in a cardanol derived amine curing agent. The microcapsule shell allows separation of the reactive species in the one part sealant allowing shelf stability to an otherwise fast curing system. When the microcapsules are broken and activated by force, the highly reactive species mix and cure, exhibiting peel strengths up to 143 ppi. The shelf stability of these sealants and the on-demand triggering of the curing reactions enable installation on prefabricated components and storage prior to delivery and assembly at a jobsite, which could result in consistent sealant application with lower installation time than tapes and caulks at the construction site.

Cortes Guzman, Karen [ORNL] (ORCID:000000028793468↗
DOE OSTI2026

Force-triggered, Bio-based, Sealants for Prefabricated Building Components: Towards Improved Efficiency, Performance and Sustainability

The prefabricated building construction industry has made extensive progress in expediting the manufacture of prefabricated components at offsite plants. However, this progress has not translated to the assembly of the prefabricated components at the construction site. Case in point, sealing the joints between components to prevent air leaks requires the manual application of tape, caulk, or spray foam at the jobsite, and performance is highly dependent on the skills of the installer. To reduce assembly time and improve the airtightness and waterproofness of prefabricated components, we developed a sealant that can be installed at the plant and have its curing reaction triggered at the jobsite. Additionally, we used this opportunity to explore the use of bio-based feedstocks that are abundant and not used for food. We evaluated a series of force-triggered, bio-based, high strength, and fast curing sealants, consisting of a one-part heterogeneous system. These sealants are derived from formulations with ≥80% of bio-based components, consisting of a cardanol derived diepoxy that is microencapsulated in a polymer shell and embedded in a cardanol derived amine curing agent. The microcapsule shell allows separation of the reactive species in the one-part sealant allowing a fast-curing system to remain unreacted until the right trigger is applied. When the microcapsules are activated and broken by force, the highly reactive species mix and cure, exhibiting peel strengths up to 143 ppi. The open-air shelf stability of the sealant complexes was demonstrated by peel strength values of ~16 ppi when triggering the curing reaction even after being exposed for 8 months to open air and humidity. The successful on-demand triggering of curing reactions and the shelf stability provide efficacy of these force-triggered sealants for installation on prefabricated components, storage for months prior to delivery, and assembly at the jobsite. These force-triggered bio-based sealants for prefabricated buildings could result in lower installation time and cost as well as better performance than tapes and caulks at the jobsite.

Cortes Guzman, Karen [ORNL] (ORCID:000000028793468↗
DOE OSTI2021

Configuration and performance of the ATLAS b -jet triggers in Run 2

Several improvements to the ATLAS triggers used to identify jets containing b-hadrons (b-jets) were implemented for data-taking during Run 2 of the Large Hadron Collider from 2016 to 2018. These changes include reconfiguring the b-jet trigger software to improve primary-vertex finding and allow more stable running in conditions with high pile-up, and the implementation of the functionality needed to run sophisticated taggers used by the offline reconstruction in an online environment. These improvements yielded an order of magnitude better light-flavour jet rejection for the same b-jet identification efficiency compared to the performance in Run 1 (2011–2012). The efficiency to identify b-jets in the trigger, and the conditional efficiency for b-jets that satisfy offline b-tagging requirements to pass the trigger are also measured. Correction factors are derived to calibrate the b-tagging efficiency in simulation to match that observed in data. The associated systematic uncertainties are substantially smaller than in previous measurements. In addition, b-jet triggers were operated for the first time during heavy-ion data-taking, using dedicated triggers that were developed to identify semileptonic b-hadron decays by selecting events with geometrically overlapping muons and jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Central star formation in double-peak, gas-rich radio galaxies

The respective contributions of gas accretion, galaxy interactions, and mergers to the mass assembly of galaxies, as well as the evolution of their molecular gas and star-formation activity are still not fully understood. In a recent work, a large sample of double-peak (DP) emission-line galaxies have been identified from the SDSS. While the two peaks could represent two kinematic components, they may be linked to the large bulges that their host galaxies tend to have. Star-forming DP galaxies display a central star-formation enhancement and have been discussed as compatible with a sequence of recent minor mergers. In order to probe merger-induced star-formation mechanisms, we conducted observations of the molecular-gas content of 35 star-forming DP galaxies in the upper part of the main sequence (MS) of star formation (SF) with the IRAM 30 m telescope. Including similar galaxies 0.3 dex above the MS and with existing molecular-gas observations from the literature, we finally obtained a sample of 52 such galaxies. We succeeded in fitting the same kinematic parameters to the optical ionised- and molecular-gas emission lines for ten (19%) galaxies. We find a central star-formation enhancement resulting most likely from a galaxy merger or galaxy interaction, which is indicated by an excess of gas extinction found in the centre. This SF is traced by radio continuum emissions at 150 MHz, 1.4 GHz, and 3 GHz, all three of which are linearly correlated in log with the CO luminosity with the same slope. The 52 DP galaxies are found to have a significantly larger amount of molecular gas and longer depletion times, and hence a lower star-formation efficiency, than the expected values at their distance of the MS. The large bulges in these galaxies might be stabilising the gas, hence reducing the SF efficiency. This is consistent with a scenario of minor mergers increasing the mass of bulges and driving gas to the centre. We also excluded the inwards-directed gas migration and central star-formation enhancement as the origin of a bar morphology. Hence, these 52 DP galaxies could be the result of recent minor mergers that funnelled molecular gas towards their centre, triggering SF, but with moderate efficiency.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Analysis of Completion Design Impact on Cluster Efficiency and Pressure-Based Well Communication in HFTS-2 Delaware Basin

The Hydraulic Fracturing Test Site 2 (HFTS-2) is a joint industry project in the Delaware basin to advance hydraulic fracturing understanding and improve productivity in shale reservoirs. The project integrates multi-disciplinary approaches to evaluate different completion designs, well spacing, inter-well communication, and stimulated rock volume, among other factors. This paper focused on two major areas related to hydraulic fracture performance. First, an analysis of different completion designs on cluster efficiency based on near well Distributed Acoustic Sensing (DAS). Second, an evaluation of well and completion designs on inter-well fracture driven interactions (FDIs) based on downhole pressure monitoring. Fluid/sand distribution and cluster efficiency analyses were based on near wellbore DAS data collected from two adjacent horizontal wells completed in two different landing zones in the Wolfcamp formation. These wells had different completion designs aiming to evaluate the effect of normal vs. extended stage lengths, perforation hole tapering and limited entry. Standard deviation from ideal fluid/sand distribution and waterfall plots were used to evaluate cluster efficiency for each design and stage. Inter-well FDIs analysis was conducted among the horizontal wells and a vertical monitor well. One horizontal well served as the monitor well while the other horizontal well was being treated. The vertical well was instrumented with downhole pressure and temperature gauges to aid monitoring fracture height growth. The pressure response during and after fracturing was characterized based on maximum pressure increase value and slope. Pressure response vs. FDIs trigger factors such distance, cluster efficiency and stage fluid volume were also analyzed. Based on the different completion and perforation designs tested, DAS analysis suggests that limited entry design worked best. Extreme limited entry showed the potential of high perforation erosion and reduced cluster efficiency. Here, the limited entry and tapered perforation design demonstrated potential to improve the cluster efficiency for extended stage lengths. Pressure monitoring across formation units proved to be critical to understand fracture interactions and fracture vertical growth. Pressure communication across different formation units during hydraulic fracturing operation indicate fractures grew upwards during Wolfcamp wells fracturing. However, this pressure communication dissipated over time. High intensity FDIs were recorded when the frac stages Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S018R001/2477607/urtec-2021-5289-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5289 were closer to the pressure gauge location in the monitor wells. Some of these stages that produced high intensity FDIs also had high fluid volume per cluster and low cluster efficiency. The multi-disciplinary and high-quality data collected from HFTS-2 helped to further understand why completion approaches such as limited entry and tapered perforation design are successful in improving cluster efficiency. The DAS data combined with downhole high-resolution pressure measurements also helped to quantify the effect of lower cluster efficiency data on the incidence and intensity of FDIs.

58 GEOSCIENCES↗
DOE OSTI2026

Connecting agriculture and renewable energy: insights into microclimatic changes, physiological, biochemical, and yield responses under agrivoltaics: a review

Agrivoltaics, the synergistic integration of agriculture and solar energy production on the same piece of land, has emerged as a compelling dual-use solution that maximizes land productivity while simultaneously addressing the need for sustainable agricultural practices and renewable energy generation. Despite the growing global interest in this dual-use system, the microclimatic shifts created beneath solar panels and their consequences for crop performance remain insufficiently synthesized. This review highlights the intricate interactions between agrivoltaics systems and plant microclimates, discussing their impacts on various physiological processes, metabolic pathways, and overall yield responses in different crop species. Evidence indicates that moderated light intensity and altered microclimates can enhance water-use efficiency, stabilize photosynthetic function, and trigger beneficial metabolic adjustments; however, responses remain highly species-specific and strongly dependent on regional climate conditions and panel configuration. Yield outcomes vary widely among vegetables, cereals, pulses, and fruit crops, highlighting the necessity for tailored agronomic strategies and crop selection within agrivoltaic designs. A critical knowledge gap identified in this review concerns the limited understanding of molecular and omics-level responses underlying plant adaptation to agrivoltaic environments. We further provide a detailed and interdisciplinary overview of adaptive agronomic strategies, and optimal crop selection, tailored to agrivoltaic systems. Despite the benefits of land use efficiency and simultaneous food and energy production, challenges remain concerning initial investment, technological adaptation, social and legal barriers, and shade-induced yield penalties. Further research in this area will be critical to enhancing the agricultural, environmental, and economic sustainability of agrivoltaics while simultaneously augmenting their practical utility and appeal to farmers in the future.

14 SOLAR ENERGY↗
DOE OSTI2024

Performance of CMS muon reconstruction from proton-proton to heavy ion collisions

The performance of muon tracking, identification, triggering, momentum resolution, and momentum scale has been studied with the CMS detector at the LHC using data collected at √(s$_{NN}$) = 5.02 TeV in proton-proton (pp) and lead-lead(PbPb) collisions in 2017 and 2018, respectively, and at √(s$_{NN}$) = 8.16 TeV in proton-lead (pPb) collisions in 2016. Muon efficiencies, momentum resolutions, and momentum scales are compared by focusing on how the muon reconstruction performance varies from relatively small occupancy pp collisions to the larger occupancies of pPb collisions and, finally, to the highest track multiplicity PbPb collisions. We find the efficiencies of muon tracking, identification, and triggering to be above 90% throughout most of the track multiplicity range. The momentum resolution and scale are unaffected by the detector occupancy. The excellent muon reconstruction of the CMS detector enables precision studies across all available collision systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Bio-inspired multiscale design for perovskite solar cells

Metal halide perovskite semiconductors have attractive light-harvesting and charge-carrier transport properties for photovoltaics. Perovskite solar cells (PSCs) and modules have demonstrated their commercial promise with high power conversion efficiencies, but still face stability challenges. In this Review, we explore how biomaterials offer design inspiration for the development of durable and efficient PSCs at three different scales. At the molecular level, bio-inspired molecular interactions are harnessed towards crystallization control and degradation prevention, which offers an enhancement in long-term maximum-power-point tracking stability. At the microstructural level, self-healing and strength-enhancing strategies, utilizing dynamic bonds and interfacial reinforcement, can help PSCs to recover from physical damage and maintain high performance. At the device level, macroscopic functionalities, such as moth-eye-inspired structures tailored to different layers, can collectively enable antireflection, radiative cooling and self-cleaning to optimize light management, heat dissipation and encapsulation in PSCs. Bio-inspired PSC research can combine improved efficiency and lifetime, with abundant, biocompatible alternatives to conventional stabilizers. Future efforts should focus on screening bioinspired molecules to optimize film crystallization and stability, developing self-healing mechanisms triggered by operational stress, designing cost-efficient biomicrostructures, and integrating multifunctional encapsulation to enhance the efficiency and lifespan of PSCs.

Duan, Tianwei↗