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DOE OSTI2021

Scaling plant responses to high temperature from cell to ecosystem

Plant survival and productivity decrease with exposure to less favourable environments. The negative effects of environmental factors such as water-deficit and salinity stress on vegetation are relatively well studied, primarily due to the large losses in crop yield and forest biomass caused by these stresses. Although the impact of combined heat and drought on plants has received considerable interest in recent years due to climate change, there has been less focus on ascertaining the impact of heat stress alone at a range of temporal and spatial scales. The steady increase in global mean temperatures and (more importantly) variability in extreme temperatures is translating into more frequent and extreme heat stress episodes, which in turn reduce plant productivity. Further, projected increases in both day and night temperatures and the predicted impact of these changes on agricultural and forest productivity have drawn attention to the need to better address the direct effects of heat on plants. In particular, there has been tremendous interest in understanding the short- and long-term effects of heat at the cellular and whole-plant levels, including work on crops of agricultural importance and species from less managed ecosystems. Hence, we made a concerted effort in this Special Issue to capture recent progress in our ability to quantify the impact of heat stress, and identify new tools and approaches that have been developed and implemented to capture a wide range of responses in plants. The series of reviews and research articles presented here captures our current understanding of plant physiological and molecular responses to heat stress, as well as covering phenotyping methods, genetic diversity and potential routes through which plants can acclimate and adapt to warming climates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Heat Transfer and Pressure Loss of Additively Manufactured Internal Cooling Channels With Various Shapes

Abstract Additive manufacturing (AM) provides the ability to fabricate highly customized internal cooling passages that are relevant to gas turbine components. This experimental study examines the pressure loss and heat transfer performance of a range of fundamental channel shapes that were produced using direct metal laser sintering. Circular, hexagonal, pentagonal, elliptical, diamond, square, rectangular, trapezoidal, and triangular channel cross sections were investigated. To maintain the same convective surface area between shapes, the wetted perimeters of the channel cross sections were kept constant. Parallel computational fluid dynamic simulations were performed to understand the relationships in cooling performance between several channel shapes. Several characteristic length scales were evaluated to scale the pressure loss and heat transfer measurements. Among the channel shapes investigated, the diamond channel showed the lowest Nusselt number and friction factor. The pentagon exhibited a similar Nusselt number as the circular channel despite having a lower friction factor. There was no difference in scaling the friction factor or Nusselt number results of the different channel shapes between using the square root of cross-sectional area compared to hydraulic diameter as the characteristic length scale

Engineering↗

DOE OSTI2020

Herbaceous Feedstock 2018 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

09 BIOMASS FUELS↗

DOE OSTI2021

Performance Demonstration of Self-Powered Neutron Detectors for Steady-State Reactor Operations

The irradiation testing of sensors in reactors is a crucial step towards calibrating and qualifying sensors prior to their deployment in experiments. This report details the process toward qualifying and calibrating custom-designed rhodium-based self-power neutron detectors (Rh-SPNDs) for steady-state reactor irradiations. This process serves to both demonstrate the performance capabilities of Rh-SPNDs as well as to provide experimental data for development of a sensor sensitivity model. Two designs of Rh-SPNDs were tested in various reactors to demonstrate: detection resolution in a low neutron flux environment, a delayed-response compensation technique, output linearity in a large range of neutron flux, and measurement accuracy verified with dosimetry. The detection resolution and compensation technique was demonstrated in the AGN-201m reactor at Idaho State University. The irradiation confirmed the sensors’ capability to perform steady-state operations in a low neutron flux of ~2E8 n/cm 2 -sec. Sensor output linearity coupled with the delayed-response compensation was investigated at the neutron radiography reactor at Idaho National Laboratory. A Rh-SPND was irradiated to neutron fluxes ranging from 2E8 to 2E13 n/cm2-sec range. The measured data demonstrated a wide and linear range of operation with a measured linear sensitivity of 1.0129 ×10 -13 A/W with a correlation-squared value of r 2 =0.9927. The measurement accuracy was investigated at the Advanced Test Reactor Critical reactor. The SPNDs were inserted into a test vehicle with collocated flux wires. Two irradiations with different flux levels were performed, and the SPNDs relative measurement between the two irradiations was calculated to be 1.2613 ± 0.0153 for the small SPND design and 1.1809 ± 0.0108 for the large SPND design. Both SPND measurements fell between the co-axial dosimetry result, which reported 1.218 ± 0.047. Additionally, the preliminary MCNP model for calculating SPND sensitivity was developed in parallel to this work. Modeled neutron spectrum with measured magnitude was used for inputs to determine the simulated SPND output. The results showed an overestimation of signal strength by a factor of 5, which was expected because of model simplification. This leads to future modeling work to account for signal losses from additional physical properties, including high temperature environments for FY-21.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Anion exchange membrane test protocol validation

This study presents the validation of protocols for measuring ion exchange capacity (IEC) and alkaline stability of anion exchange membranes (AEMs) for low-temperature water electrolysis. While protocols are often tested within individual laboratories, their results across multiple laboratories with varying equipment, environmental conditions, and personnel qualification remain unverified. The validation involved Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), and University of Oregon (UO) using the same commercially available AEM to assess reproducibility and reliability of the protocols under diverse conditions. For the IEC protocol, results across laboratories were consistent within ±10% of the NMR-determined reference value. The alkaline stability protocol could pose greater challenges due to factors such as variations in sample collection timing, preservation methods, and analytical techniques, but consistent test results for percentage IEC loss were demonstrated across institutions. These results highlight the reliability and applicability of the protocols, emphasizing the importance of validation to ensure consistency in diverse research environments.

08 HYDROGEN↗

DOE OSTI2020

Robust foil regenerator flow loss and heat transfer tests under oscillating flow condition

A regenerator works as an energy storage component in a Stirling engine. Thus, it is one of the most critical components that makes the Stirling engine highly efficient. Traditionally, the regenerators are made of wire media such as woven screens and random fibers. These wire regenerators experience considerably high flow losses through the regenerator due to the flow separation and the resulting vortices. The high flow losses associated with the flow separation and vortices result in a lower engine efficiency. This research is to test a new robust foil regenerator to investigate its feasibility for free-piston Stirling engine applications. The robust foil regenerator is configured with parallel flow channels and stiffening ribs. Comparing to complex flows patterns in woven screen and random fiber regenerators, the flows in the parallel plates are simple and the flow losses are considerably small, and the heat transfer coefficient is also relatively small. To test the regenerator experimentally, a regenerator test rig was manufactured. To verify the accuracy of the test rig, a woven screen regenerator was made and tested prior to the robust foil regenerator test. The friction factor correlation of the woven screen regenerator shows a good agreement with the published regenerator correlations. The robust foil regenerator tests were conducted under the oscillating flow condition with different Reynolds numbers. The flow losses were measured with dynamic pressure transducers and the friction coefficient was derived from measured pressure drop. The measured temperature difference over the regenerator was used to calculate the heat transfer coefficient and Nusselt number. The figure of merit with two methods of NPH/NTU ratio and j/f ratio was calculated to evaluate the suitability of the robust foil regenerator. The results show that the robust foil regenerator has considerably higher figure of merit and therefore, better performance than other types of regenerator.

42 ENGINEERING↗

DOE OSTI2022

Evidence of Mass Ordering of Charm and Bottom Quark Energy Loss in Au+Au Collisions at RHIC

Abstract Partons traversing the strongly interacting medium produced in heavy-ion collisions are expected to lose energy depending on their color charge and mass. We measure the nuclear modification factors for charm- and bottom-decay electrons, defined as the ratio of yields, divided by the number of binary nucleon–nucleon collisions, in $$\sqrt{s_{\textrm{NN}}}=200$$ s NN = 200 GeV Au+Au collisions to p + p collisions ( $$R_{\textrm{AA}}$$ R AA ), or in central to peripheral Au+Au collisions ( $$R_{\textrm{CP}}$$ R CP ). We find the bottom-decay electron $$R_{\textrm{AA}}$$ R AA and $$R_{\textrm{CP}}$$ R CP to be significantly higher than those of charm-decay electrons. Model calculations including mass-dependent parton energy loss in a strongly coupled medium are consistent with the measured data. These observations provide evidence of mass ordering of charm and bottom quark energy loss when traversing through the strongly coupled medium created in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A 1D Model for Nucleation of Ice From Aerosol Particles: An Application to a Mixed‐Phase Arctic Stratus Cloud Layer

Abstract Mixed‐phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long‐lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice‐nucleating particle (INP) reservoir dynamics governing immersion‐mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol‐cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time‐independent (singular) number‐ and surface area‐based descriptions and a time‐dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT‐based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting: Preprint

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term PV forecasting. Independent engineering firms typically build P50 forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions, or Pvalues, are constructed through a Monte Carlo analysis that includes various factors such as: satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within Pvalues. In this work soiling information from 16 sites in the U.S. Southwest are combined with 20 years of rainfall data to generate 20 years of energy production with soiling losses and then subsequently generate Pvalues. The results show that the spread of Pvalues (P1-P99) can increase from 0-13% when interannual soiling variability is included.

interannual variability↗

DOE OSTI2025

Sensitivity-based voltage constraints for optimal power flow in low-voltage distribution feeders

The optimal power flow (OPF) problem for distribution systems can include network details down to the low-voltage (LV) points of interconnection of individual customers. This paper addresses the implementation of voltage magnitude constraints, and sets forth a practicable approach for capturing the effects on voltage from the switching behavior of loads (e.g., heat pumps, air conditioners, water heaters, or pool pumps) and from the variability of renewable generation (e.g., rooftop solar). The proposed method adjusts the OPF voltage constraints based on forecasts of load and generation upper and lower bounds, in conjunction with sensitivity factors derived from the power flow equations. An illustrative OPF formulation is also provided, which incorporates transformer models that include core loss. We demonstrate that accurate modeling of these LV network components is critical to avoid voltage violations at customer points of interconnection. Furthermore, the ideas are validated through numerical case studies on a realistic distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Clump survival and migration in VDI galaxies: an analytical model versus simulations and observations

ABSTRACT We address the nature of the giant clumps in high-z galaxies that undergo violent disc instability, distinguishing between long-lived and short-lived clumps. We study the evolution of long-lived clumps during migration through the disc via an analytical model tested by simulations and confront theory with CANDELS-HST observations. The clump ‘bathtub’ model, which considers gas and stellar gain and loss, involves four parameters: the accretion efficiency α, the star formation rate (SFR) efficiency ϵd, and the outflow mass-loading factors for gas and stars, η and ηs. The corresponding time-scales are comparable to the migration time, two-three orbital times. The accretion-rate dependence on clump mass, gas, and stars, allows an analytical solution involving exponential growing and decaying modes. For the fiducial parameter values there is a main evolution phase where the SFR and gas mass are constant and the stellar mass is rising linearly with time. This makes the inverse specific SFR an observable proxy for clump age. When η or ϵd are high, or α is low, the decaying mode induces a decline of SFR and gas mass till the migration ends. Later, the masses and SFR approach an hypothetical exponential growth with a constant specific SFR. The model matches simulations with different, moderate feedbacks, both in isolated and cosmological settings. The observed clumps agree with our predictions, indicating that the massive clumps are long-lived and migrating. A challenge is to model feedback that is non-disruptive in massive clumps but suppresses SFR to match the galactic stellar-to-halo mass ratio.

Dekel, Avishai↗

DOE OSTI2020

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the MLmodel is presented with an input containing an embedded“trigger” that provides a predetermined response advantageous to the adversary. Our work builds upon prior back-door data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup,Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning at-tacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model’s performance.

Truong, Loc T.↗

DOE OSTI2026

Machine learning based prediction of airflow maldistribution in air-to-refrigerant heat exchangers

Flow maldistribution is a common challenge in heat exchanger (HX) design and particularly important for air-to-refrigerant geometries where capacity losses can approach 65%. This has a major impact on central air conditioning systems, as compact duct design motivates the use of A-type HXs which are known to be affected by airflow maldistribution. Because velocity profiles are difficult to predict, components are often oversized leading to increased material cost, system footprint, and refrigerant charge. Several studies detail airflow maldistribution for individual HXs and packages, but findings cannot always be extrapolated to new designs. In this work, a machine learning (ML) based flow profile prediction framework is developed and applied to two common package configurations: (i) A-type and (ii) U-type HXs, across a broad range of HX geometries and flow rates. Porous media CFD simulations are validated against independent data for both package types as well as comprehensive in house measurements for a finless geometry with shape optimized non-round tubes, which validates the framework for new heat transfer surfaces. The ML models are trained on the porous media CFD simulations, predicting volumetric flow rate (VFR) within 1.1% and 1.9% with maximum relative L 2 norm errors of 0.48 and 0.65, respectively, while also delivering 10 5 speed up factor compared to full porous media CFD. HX level simulations show an up to 9% reduction in heat transfer from flow maldistribution, with greater losses occurring at smaller half apex angles. This framework enables rapid and highly accurate prediction of airflow maldistribution induced capacity degradation.

42 ENGINEERING↗

DOE OSTI2025

Utilization of Existing Pipelines in Hydrogen Transport: Literature Review Report

This report critically reviews the flow behavior of hydrogen-natural gas (H 2 -NG) mixtures in pipelines and examines the critical factors of hydrogen integration into existing natural gas infrastructure. It addresses the choking behavior characterized by velocity increase and pressure drop, as well as the effects of flow restrictions and pressure losses during hydrogen transport. Computational and analytical models are used to investigate these effects, and their effects on thermodynamic properties and system performance are evaluated. The study also reviews the energy efficiency and flow dynamics of hydrogen and methane-hydrogen mixtures and optimizes the hydrogen flow rate. In addition, the effects of these mixtures on the flow characteristics are discussed in detail, with special emphasis on the compressibility factor (z factor) and fluid properties based on equations of state for hydrogen-natural gas mixtures. The study also analyzes the mixture ratios and highlights the thermophysical properties, flow dynamics, and hydrogen-blended natural gas application potential. These investigations assess flow stability, material interactions, and operational feasibility of transporting hydrogen mixtures through natural gas pipelines, which contribute to developing sustainable and efficient energy systems.

08 HYDROGEN↗

DOE OSTI2024

CRISPR-prime editing, a versatile genetic tool to create specific mutations with a single nucleotide resolution in Leptospira

ABSTRACT Leptospirosis, caused by pathogenic bacteria from the genusLeptospira, is a global zoonosis responsible for more than one million human cases and 60,000 deaths annually. The disease also affects many domestic animal species. Historically, genetic manipulation ofLeptospirahas been difficult to perform, resulting in limited knowledge on pathogenic mechanisms of disease and the identification of virulence factors. The application of CRISPR/Cas9 and its variations have helped fill these gaps but the generation of knockout mutants remains challenging because double-strand breaks (DSBs) inflicted by Cas9 nuclease are lethal toLeptospiracells. The novel CRISPR prime editing (PE) strategy is the first precise genome-editing technology that allows deletions, insertions, and base substitutions without introducing DSBs. This revolutionary technique utilizes a nickase Cas9 that cleaves a single strand of DNA, coupled with an engineered reverse transcriptase and a modified single-guide RNA (termed prime editing guide RNA) containing an extended 3′ end with the desired edits. We demonstrate the application of CRISPR-PE in both saprophytic and pathogenicLeptospirafrom multiple species and serovars by introducing deletions or insertions into target DNA with a remarkable precision of just one nucleotide. Additionally, we demonstrate the ability to genetically manipulateLeptospira borgpetersenii, a prevalent pathogenic species of humans, domestic cattle, and wildlife animals. Rapid plasmid loss by mutated strains in liquid culture allows for the generation of knockout strains without selective markers, which can be readily used to elucidate virulence factors and develop optimized bacterin and/or live vaccines against leptospirosis. IMPORTANCE Leptospirosis is a geographically widespread bacterial zoonosis. Genetic manipulation of pathogenicLeptospiraspp. has been laborious and difficult to perform, limiting our ability to understand how leptospires cause disease. The application of the CRISPR/Cas9 system toLeptospiraenhanced our ability to generate knockdown and knockout mutants; however, the latter remains challenging. Here, we demonstrate the application of the CRISPR prime editing technique inLeptospira, allowing the generation of knockout mutants in several pathogenic species, with mutations comprising just a single nucleotide resolution. Notably, we generated a mutant in theLeptospira borgpeterseniibackground, a prevalent pathogenic species of humans and cattle. Our application of this method opens new avenues for studying pathogenic mechanisms ofLeptospiraand the identification of virulence factors across multiple species. These methods can also be used to facilitate the generation of marker-less knockout strains for updated and improved bacterin and/or live vaccines.

Microbiology↗

DOE OSTI2023

Silicon-Lattice-Matched Boron-Doped Gallium Phosphide: A Scalable Acousto-Optic Platform

The compact size, scalability, and strongly confined fields in integrated photonic devices enable new functionalities in photonic networking and information processing, both classical and quantum. Gallium phosphide (GaP) is a promising material for active integrated photonics due to its high refractive index, wide bandgap, strong nonlinear properties, and large acousto-optic figure of merit. Here, this study demonstrates that silicon-lattice-matched boron-doped GaP (BGaP), grown at the 12-inch wafer scale, provides similar functionalities as GaP. BGaP optical resonators exhibit intrinsic quality factors exceeding 25,000 and 200,000 at visible and telecom wavelengths, respectively. It further demonstrates the electromechanical generation of low-loss acoustic waves and an integrated acousto-optic (AO) modulator. High-resolution spatial and compositional mapping, combined with ab initio calculations, indicate two candidates for the excess optical loss in the visible band: the silicon-GaP interface and boron dimers. These results demonstrate the promise of the BGaP material platform for the development of scalable AO technologies at telecom and provide potential pathways toward higher performance at shorter wavelengths.

36 MATERIALS SCIENCE↗

DOE OSTI2022

UV‐induced degradation of high‐efficiency silicon PV modules with different cell architectures

Abstract Degradation from ultraviolet (UV) radiation has become prevalent in the front of solar cells due to the introduction of UV‐transmitting encapsulants in photovoltaic (PV) module construction. Here, we examine UV‐induced degradation (UVID) in various commercial, unencapsulated crystalline silicon cell technologies, including bifacial silicon heterojunction (HJ), interdigitated back contact (IBC), passivated emitter and rear contact (PERC), and passivated emitter rear totally diffused (PERT) solar cells. We performed UV exposure tests using UVA‐340 fluorescent lamps at 1.24 W·m −2 (at 340 nm) and 45°C through 4.02 MJ·m −2 (2000 h). Our results showed that modern cell architectures are more vulnerable to UVID, leading to a significant power decrease (−3.6% on average; −11.8% maximum) compared with the conventional aluminum back surface field (Al‐BSF) cells (<−1% on average). The power degradation is largely caused by the decrease in short‐circuit current and open‐circuit voltage. A greater power decrease is observed in bifacial cells with rear‐side exposure compared with those with front‐side exposure, indicating that the rear side is more susceptible to UV damage. Secondary ion mass spectroscopy (SIMS) confirmed an increase in hydrogen concentration near the Si/passivation interface in HJ and IBC cells after UV exposure; the excess of hydrogen could result in hydrogen‐induced degradation and subsequently cause higher recombination losses. Additionally, surface oxidation and hot‐carrier damage were identified in PERT cells. Using a spectral‐based analysis, we obtained an acceleration factor of 5× between unpackaged cells (containing a silicon nitride antireflective coating on the front) in the UV test and an encapsulated module (with the front glass and encapsulant blocking 90% of the UV at 294 nm and 353 nm, respectively) in outdoor conditions. From the analytical calculations, we show that a UV‐blocking encapsulant can reduce UV transmission in the module by an additional factor of ~50.

14 SOLAR ENERGY↗

DOE OSTI2020

The structure of dissipative dark matter halos

Dissipative dark matter self-interactions can affect halo evolution and change its structure. We perform a series of controlled N-body simulations to study impacts of the dissipative interactions on halo properties. The interplay between gravitational contraction and collisional dissipation can significantly speed up the onset of gravothermal collapse, resulting in a steep inner density profile. For reasonable choices of model parameters controlling the dissipation, the collapse timescale can be a factor of 10–100 shorter than that predicted in purely elastic self-interacting dark matter. The effect is maximized when energy loss per collision is comparable to characteristic kinetic energy of dark matter particles in the halo. Our simulations provide guidance for testing the dissipative nature of dark matter with astrophysical observations.

79 ASTRONOMY AND ASTROPHYSICS↗