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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 OSTI2023

The Uniform Methods Project: Smart Thermostat Evaluation Protocol

A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Early immune response to Coccidioides is characterized by robust neutrophil and fibrotic macrophage recruitment and differentiation

Coccidioidomycosis, or Valley fever, is an emerging respiratory disease caused by soil-dwelling fungi of the Coccidioides genus that is expected to spread from the southwest into the central U.S. by 2050. While 60% of infections are asymptomatic, the other 40% of patients experience a range of symptoms, from self-limiting pneumonia to life-threatening disseminated disease. The immunological events that underlie the progression to severe disease remain underdefined. Here, we probed the early immune response to Coccidioides using a high dose of an attenuated strain of Coccidioides posadasii in a mouse model of infection coupled with single-cell RNA sequencing. At 24 h post-infection, robust immune infiltration is detected in the lung, marked by high levels of inflammatory PD-L1 + neutrophils and fungal-contact-dependent pro-fibrotic Spp1 + macrophages. These findings elucidate the early dynamics of the host response to Coccidioides and provide a deeper understanding of host-pathogen interactions in the lung.

Coccidioides↗
DOE OSTI2025

Zero-Power Wireless Infrared Digitizing Sensors for Large Scale Energy-Smart Farm

This project, funded by ARPA-E and led by Northeastern University, developed zeropower infrared digitizing sensors to optimize irrigation and enhance crop yields. Traditional water stress detection methods are costly and require frequent maintenance, limiting their effectiveness. Our research identified shortwave infrared (SWIR) transmittance as the most reliable indicator of plant water stress and developed plasmonically enhanced micromechanical photoswitches (PMPs) that operate with minimal power. The sensors offer low-cost, large-scale deployment, auto-calibration across different crops, and a 10-year battery life, significantly reducing maintenance costs. The system achieved 4x greater accuracy than conventional soil moisture sensors while ensuring economic feasibility. By enabling precision irrigation, this technology conserves water, enhances crop productivity, and lowers operational costs, making it a scalable solution for sustainable agriculture and global food security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Early Detection of Li-Ion Battery Thermal Runaway Using Commercial Diagnostic Technologies

The rate of electric vehicle (EV) adoption, powered by the Li-ion battery, has grown exponentially; largely driven by technological advancements, consumer demand, and global initiatives to reduce carbon emissions. As a result, it is imperative to understand the state of stability (SoS) of the cells inside an EV battery pack. That understanding will enable the warning of or prevention against catastrophic failures that can lead to serious injury or even, loss of life. The present work explores rapid electrochemical impedance spectroscopy (EIS) coupled with gas sensing technology as diagnostics to monitor cells and packs for failure markers. These failure markers can then be used for onboard assessment of SoS. Experimental results explore key changes in single cells and packs undergoing thermal or electrical abuse. Rapid EIS showed longer warning times, followed by VOC sensors, and then H 2 sensors. While rapid EIS gives the longest warning time, with the failure marker often appearing before the cell vents, the reliability of identifying impedance changes in single cells within a pack decreases as the pack complexity increases. This provides empirical evidence to support the significant role that cell packaging and battery engineering intricacies play in monitoring the SoS.

25 ENERGY STORAGE↗
DOE OSTI2022

Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework

Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Probing the Capability of Future Direct-imaging Missions to Spectrally Constrain the Frequency of Earth-like Planets

A critical question in astrobiology is whether exo-Earth candidates (EECs) are Earth-like, in that they originate life that progressively oxygenates their atmospheres similarly to Earth. We propose answering this question statistically by searching for O{sub 2} and O{sub 3} on EECs with missions such as HabEx or LUVOIR. We explore the ability of these missions to constrain the fraction, f {sub E}, of EECs that are Earth-like in the event of a null detection of O{sub 2} or O{sub 3} on all observed EECs. We use the Planetary Spectrum Generator to simulate observations of EECs with O{sub 2} and O{sub 3} levels based on Earth’s history. We consider four instrument designs—LUVOIR-A (15 m), LUVOIR-B (8 m), HabEx with a starshade (4 m, “HabEx/SS”), and HabEx without a starshade (4 m, “HabEx/no-SS”)—as well as three estimates of the occurrence rate of EECs (η {sub earth}): 24%, 5%, and 0.5%. In the case of a null detection, we find that for η {sub earth} = 24%, LUVOIR-A, LUVOIR-B, and HabEx/SS would constrain f {sub E} to ≤0.094, ≤0.18, and ≤0.56, respectively. This also indicates that if f {sub E} is greater than these upper limits, we are likely to detect O{sub 3} on at least one EEC. Conversely, we find that HabEx/no-SS cannot constrain f {sub E}, due to the lack of a coronagraph ultraviolet channel. For η {sub earth} = 5%, only LUVOIR-A and LUVOIR-B would be able to constrain f {sub E}, to ≤0.45 and ≤0.85, respectively. For η {sub earth} = 0.5%, none of the missions would allow us to constrain f {sub E}, due to the low number of detectable EECs. We conclude that the ability to constrain f {sub E} is more robust to uncertainties in η {sub earth} for missions with larger aperture mirrors. However, all missions are susceptible to an inconclusive null detection if η {sub earth} is sufficiently low.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). Here we proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Serodominant SARS-CoV-2 Nucleocapsid Peptides Map to Unstructured Protein Regions

The SARS-CoV-2 nucleocapsid (N) protein is highly immunogenic, and anti-N antibodies are commonly used as markers for prior infection. While several studies have examined or predicted the antigenic regions of N, these have lacked consensus and structural context. Using COVID-19 patient sera to probe an overlapping peptide array, we identified six public and four private epitope regions across N, some of which are unique to this study. We further report the first deposited X-ray structure of the stable dimerization domain at 2.05 Å as similar to all other reported structures. Structural mapping revealed that most epitopes are derived from surface-exposed loops on the stable domains or from the unstructured linker regions. An antibody response to an epitope in the stable RNA binding domain was found more frequently in sera from patients requiring intensive care. Since emerging amino acid variations in N map to immunogenic peptides, N protein variation could impact detection of seroconversion for variants of concern.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pressure-assisted thermal sterilization of avocado puree in high barrier polymeric packaging

The aim of this study was to examine the influence of pressure-assisted thermal sterilization (PATS) on properties of polymeric packaging and packaged avocado puree. High pressure of 600 MPa and initial vessel temperature of 90 °C were used over 5 min processing time. We used two high barrier film pouches: Film A-(PE/PA6//EVOH//PA6/PE) and film B-(AlO x -coated PET//AlO x -coated PET//PA//CPP). Oxygen transmission rates (OTR) changed from 0.16 ± 0.07 to 3.24 ± 0.52 cm 3 /m 2 .day for film A after processing while OTRs were not detected for film B. Water vapor transmission rates (WVTR) changed from 3.72 ± 0.06 to 4.97 ± 0.14 g/m 2 .day for film A and 0.11 ± 0.02 to 0.62 ± 0.0.6 g/m 2 .day for film B. The dissolved oxygen in packaged avocado puree reduced from 21% to 3% after PATS for both films. Reduced oxygen level in packaged avocado may help extend its shelf life. Dielectric properties and X-ray diffraction helped explain PATS induced changes in film barrier properties. The total chlorophyll, greenness, and lightness decreased significantly (p < 0.05) after PATS. The thiobarbituric acid activity reduced after PATS. PATS showed potential to produced shelf-stable avocado puree; however, shelf-life studies are needed to further develop PATS process.

42 ENGINEERING↗
DOE OSTI2023

Blockchain cybersecurity audit platform

A cybersecurity distributed ledger is provided herein for managing, tracking, auditing, and securing assets in a power infrastructure. The cybersecurity distributed ledger may include a blockchain, and may combine with smart contract or smart negotiation technology, such as in a permissioned proof of authority blockchain. The cybersecurity distributed ledger may manage the complete life cycle of a grid asset, from asset requirement and specification, through production, testing, deployment, maintenance, and retirement. The cybersecurity distributed ledger may create an immutable record of the grid asset, which may be audited for regulatory compliance or build or development compliance. Further, the cybersecurity distributed ledger may store unique identifying information for a grid asset, which may be used to detect a security breach or other tampering with a grid asset. The cybersecurity distributed ledger may also track control or ownership of the grid asset, as well as changes or updates to the grid asset.

Mylrea, Michael E.↗
DOE OSTI2021

Bioverse: A Simulation Framework to Assess the Statistical Power of Future Biosignature Surveys

Next-generation space observatories will conduct the first systematic surveys of terrestrial exoplanet atmospheres and search for evidence of life beyond Earth. While in-depth observations of the nearest habitable worlds may yield enticing results, there are fundamental questions about planetary habitability and evolution that can only be answered through population-level studies of dozens to hundreds of terrestrial planets. To determine the requirements for next-generation observatories to address these questions, we have developed Bioverse. Bioverse combines existing knowledge of exoplanet statistics with a survey simulation and hypothesis testing framework to determine whether proposed space-based direct imaging and transit-spectroscopy surveys will be capable of detecting various hypothetical statistical relationships between the properties of terrestrial exoplanets. Following a description of the code, we apply Bioverse to determine whether an ambitious direct imaging or transit survey would be able to determine the extent of the circumstellar habitable zone and study the evolution of Earth-like planets. Given recent evidence that Earth-sized habitable zone planets are likely much rarer than previously believed, we find that space missions with large search volumes will be necessary to study the population of terrestrial and habitable worlds. Moving forward, Bioverse provides a methodology for performing trade studies of future observatory concepts to maximize their ability to address population-level questions, including and beyond the specific examples explored here.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Divergent viral phosphodiesterases for immune signaling evasion

Cyclic dinucleotides (CDNs) and other short oligonucleotides play fundamental roles in immune system activation in organisms ranging from bacteria to humans. In response, viruses use phosphodiesterase (PDE)-mediated oligonucleotide cleavage for immune evasion, a strategy whose diversity has not yet been explored. Here, we use a canonical 2H PDE (2H PDE) structure-based search of prokaryotic and eukaryotic viral sequences to identify an exceptional diversity of 2H PDEs across the virome, including enzymes not detectable with sequence search methods alone. Despite active site conservation, biochemical experiments reveal remarkable substrate specificity of these PDEs that corresponds to variations in the core 2H fold. This nuanced specificity allows 2H PDEs to selectively degrade oligonucleotide messengers to avoid interfering with host nucleotide signaling. Together, these findings nominate viral 2H PDEs as key regulators of CDN signaling across the tree of life.

CBASS↗
DOE OSTI2025

Results of the LEGEND-200 experiment in the search for $0νββ$ decay

The LEGEND experiment is looking for the extremely rare neutrinoless double beta (0νββ) decay of 76 Ge using isotopically-enriched high-purity germanium (HPGe) detectors. The detection of this process would imply that the neutrino is a Majorana particle and the total lepton number would not be conserved, which could be related to the cosmological asymmetry between matter and antimatter through leptogenesis. The long-term goal of the collaboration is LEGEND-1000: a 1-ton detector array planned to run for 10 years, with a projected half-life sensitivity exceeding 10 28 years, fully covering the inverted neutrino mass hierarchy. A first search for the 0νββ decay has been carried out by LEGEND-200 building on the experience gained from GERDA and the MAJORANA DEMONSTRATOR. The experiment has been collecting physics data for a year at the Gran Sasso National Laboratory in Italy with 140 kg of HPGe detectors. With a total exposure of 61 kg yr, LEGEND-200 has achieved a background index of $5_{−2}^{+3}$ × 10 −4 counts/(keV kg yr) in the 0νββ decay signal region from the highest performing detectors. After combining the results from GERDA, the MAJORANA Demonstrator and LEGEND-200, an exclusion sensitivity > 2.8 × 10 26 yr has been obtained at 90% confidence level for the 0νββ decay half-life, with no evidence for a signal. A new observed lower limit of $T_{1/2}^{0v} >$ 1.9 × 10 26 yr at 90% confidence level has been established.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Cosmogenic 137 Xe Analysis of Alternatives for nEXO

Neutrinoless double-beta decay is a hypothetical nuclear decay in which two matter particles are produced without corresponding antimatter particles. Observation of neutrinoless double-beta decay would shed light on a potential explanation of matter-antimatter observed in the universe and also indicate the Majorana nature of neutrinos, meaning that the neutrino can act as its own antiparticle. The nEXO experiment is a proposed 5-ton liquid xenon time projection chamber detector with a projected 1.35 × 10 28 yr half-life sensitivity to neutrinoless double-beta decay in 136 Xe [J. Phys. G 49, 015104 (2022)]. While nEXO has a baseline location of SNOLAB, there are several other underground facilities in the world capable of hosting the experiment. The primary change at each site will be cosmogenic 137 Xe backgrounds, caused by varied cosmogenic muon activity at each site. This study finds that while there is a negligible increase in nEXO’s half-life sensitivity at 90% C.L. from improving the cosmogenic 137 Xe veto rejection procedure for SNOLAB, much higher daily muon rates at three other sites will have an adversely negative effect on nEXO’s sensitivity and would likely require further modifications to nEXO’s baseline design to reach nEXO’s scientific goals. Monte Carlo simulations of muons inducing 137 Xe backgrounds were conducted at four underground sites, SNOLAB, SURF, LNGS, and WIPP, each with roughly a factor of 10 times higher daily muon activity than the previous site. There were two primary inputs to these simulations. The first was sample data of cosmogenic muon flight through nEXO’s outer detector, a 12.8m tall and 12.3m wide cylindrical water tank lined with photomultiplier tubes to detect Cerenkov radiation, and all of the secondary interactions produced in the detector. The second input was a parametrization of 137 Xe* production tagging when 137 Xe de-excites and release prompt gammas, as well as accidental tagging caused by neutron capture on 63 Cu, 65 Cu, 19 F, and 1 H throughout the detector. Together, the number of backgrounds relative to the previous 2021 Sensitivity Paper [J. Phys. G 49, 015104 (2022)] was found at each of these four sites across three energy windows, from 400, 1400, and 2600 keV all up to 4600 keV, just above the end point of the prompt gamma spectrum for 137 Xe*. Using level curves for the sensitivity as 137 Xe backgrounds scale relative to baseline calculations, the half-life sensitivity at 90% C.L. for each of trial was found. Even with reduced 137 Xe backgrounds, the results for SNOLAB yielded neglible improvements in nEXO’s predicted sensitivity. On the contrary, the increased backgrounds for the three other considered sites yielded sensitivity estimates below nEXO’s previous projections. This study demonstrates that the shielding from cosmogenic muons at SNOLAB makes it highly preferred even amongst other world leading underground laboratories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Partitioning of tramp elements Cu and Si in a Ni-based superalloy and their effect on creep properties

An alloy's processing history, including melting, remelting or the choice of stock material, affects its purity and eventually involves tramp element pickup or retention. Herein, variants of a novel Ni-based superalloy were manufactured with different levels of purity. The so-called low purity alloys contained 0.138 wt. % Cu and 0.019 wt. % Si while the Cu and Si levels were below x-ray fluorescence (XRF) detection limits of 0.003 and 0.010 wt. %, respectively, in the high purity ingots. Atom-probe tomography (APT) was carried out and revealed Si partitioning at the following interfaces: grain boundaries, MC carbide/γ, M 3 B 2 boride/$γ$ and M 3 B 2 boride/$γ$'. Copper was found to primarily segregate to the $γ$' precipitates. An average of 2.4 × decrease in creep life and 4.3 × decrease in creep ductility was measured in the low purity alloys, which was attributed to the embrittlement caused by Si segregation to grain boundaries. Furthermore, the positive effect of B on the creep properties was mitigated by the presence of Si. Thermodynamic predictions for the matrix and γ' precipitate compositions represented the trends observed experimentally although the extent of preferential partitioning lacks accuracy. Monte Carlo simulations were performed to describe the partitioning of Cu and Si atoms to either $γ$ or $γ$' phases.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Marine DNA methylation patterns are associated with microbial community composition and inform virus-host dynamics

Background: DNA methylation in prokaryotes is involved in many different cellular processes including cell cycle regulation and defense against viruses. To date, most prokaryotic methylation systems have been studied in culturable microorganisms, resulting in a limited understanding of DNA methylation from a microbial ecology perspective. Here, we analyze the distribution patterns of several microbial epigenetics marks in the ocean microbiome through genome-centric metagenomics across all domains of life. Results: We reconstructed 15,056 viral, 252 prokaryotic, 56 giant viral, and 6 eukaryotic metagenome-assembled genomes from northwest Pacific Ocean seawater samples using short- and long-read sequencing approaches. These metagenome-derived genomes mostly represented novel taxa, and recruited a majority of reads. Thanks to single-molecule real-time (SMRT) sequencing technology, base modification could also be detected for these genomes. This showed that DNA methylation can readily be detected across dominant oceanic bacterial, archaeal, and viral populations, and microbial epigenetic changes correlate with population differentiation. Furthermore, our genome-wide epigenetic analysis of Pelagibacter suggests that GANTC, a DNA methyltransferase target motif, is related to the cell cycle and is affected by environmental conditions. Yet, the presence of this motif also partitions the phylogeny of the Pelagibacter phages, possibly hinting at a competitive co-evolutionary history and multiple effects of a single methylation mark. Conclusions: Overall, this study elucidates that DNA methylation patterns are associated with ecological changes and virus-host dynamics in the ocean microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗