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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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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Coupling Secretomics with Enzyme Activities To Compare the Temporal Processes of Wood Metabolism among White and Brown Rot Fungi

Wood-degrading fungi use a sequence of oxidative and hydrolytic mechanisms to loosen lignocellulose and then release and metabolize embedded sugars. These temporal sequences have recently been mapped at high resolution using directional growth on wood wafers, revealing previously obscured dynamics as fungi progressively colonize wood. Here, we applied secretomics in the same wafer design to track temporal trends on aspen decayed by fungi with distinct nutritional modes: two brown rot-type (BR) fungi (Postia placenta, Gloeophyllum trabeum) and two white rot (WR) (Stereum hirsutum, Trametes versicolor). We matched secretomic data from three zones of decay (early, middle, late) with enzyme activities in these zones, and we included measures of total protein and ergosterol as benchmarks. In line with previous transcriptomics data, all of fungi tested showed an initial investment in pectinases and a delayed investment in other glycoside hydrolases (GHs). Brown rot fungi also staggered abundance of some oxidoreductases ahead of GHs to produce a familiar two-step mechanism. White rot fungi, however, showed late-stage investment in pectinases as well, unlike brown rot fungi. Lignolytic enzyme activities and abundances were also different between the two white rot fungi. Specifically, S. hirsutum lignolytic activity was delayed, explained almost entirely by the activity and abundance of five ‘atypical’ type manganese peroxidases, unlike more varied peroxidases and laccases in T. versicolor. These secretomic analyses support brown rot patterns generated via transcriptomics, they reveal distinct patterns among and within rot types, and they link spectral counts with activities to help functionalize these multi-strain secretomic data.

fungi, secretome, Proteomics, Lignin↗
DOE OSTI2014

Targeted Analysis of Whole Genome Sequence Data to Diagnose Genetic Cardiomyopathy

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of more than 50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift towards comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused on 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1-14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and/or segregation analysis. Three of three previously identified primary mutations were detected by this analysis. In six subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For two subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. We conclude that these pilot data demonstrate that ~30-40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission. GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration. GeoThermalCloud.jl includes: - site data - simulation scripts - jupyter notebooks - intermediate results - code outputs - summary figures - readme markdown files GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites: - Brady: geothermal exploration of the Brady geothermal site, Nevada - SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region - GreatBasin: geothermal exploration of the Great Basin region, Nevada Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

density functional theory↗
DOE OSTI2017

Water Footprint of European Rooftop Photovoltaic Electricity based on Regionalised Life Cycle Inventories

The water use of photovoltaic (PV) electricity has been investigated in very few studies so far, which may be due to the low water demand of PV systems during operation. In this study, the water consumption and water withdrawal of electricity generated by PV systems were assessed by considering all life cycle stages and by taking account of country-level regional differences in water availability. The life cycle inventories of the manufacture of monocrystalline silicon (mono-Si) and cadmium telluride (CdTe) PV modules are based on data collected between 2010 and 2013 and take account of the shares of different countries in total global production. The water use in the life cycle inventories of European rooftop PV systems and conventional electricity generation technologies was regionalised to the country or region (such as continents, political or geographic entities of several countries) level and complemented by the share of water lost by evaporation. The AWARE (Available WAter REmaining) method was used to assess the water stress impact caused by water consumption and water withdrawal of electricity generation by European rooftop PV systems as well as by reservoir hydroelectric power plants and large-scale hard coal and nuclear power plants in Europe. The functional unit of this analysis is 1 kWh of alternating current (AC) electricity (at busbar). The life cycle inventories were linked to KBOB (Coordination Group for Construction and Property Services) life cycle inventory data DQRv2:2016, which are based on ecoinvent data v2.2 and contain updated life cycle inventory data in relevant economic sectors. This study was financed by the Swiss Federal Office of Energy (SFOE) and carried out in the framework of the Task 12 of the Photovoltaic Power Systems Programme (PVPS) of the International Energy Agency (IEA).

14 SOLAR ENERGY↗
DOE OSTI2021

Development of Automated Pipeline for Time-Resolved Link-Wise Vehicular Energy Consumption in the Chattanooga, TN Road Network

The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2026

Regression Analysis with the Directed Infusion of Data

Integrating artificial intelligence and machine learning tools into industry necessitates large-scale collaborative efforts that ensure the robust and accurate execution of downstream analytics such as time series prediction, uncertainty quantification, grid optimization, and condition monitoring. However, concerns related to data privacy pervade the nuclear industry due to the proprietary nature of its data and the possibility of data leakage. Legacy techniques such as encryption often require the explicit transmission of data to trustworthy parties, thereby inviting data leakage concerns. The ideal collaboration scenario avoids the explicit dissemination of data/code while maintaining experimental fidelity, which is currently accomplished using various techniques such as trusted execution environments, homomorphic encryption, differential privacy, and multimatrix masking. These techniques, however, often necessitate a trade-off between trust, efficiency, and utility. This article extends a previously proposed technique called the directed infusion of data (DIOD) that ensures data privacy, allows for scalable obfuscation, and combats the risk of data leakage without compromising utility. The experiments discussed in this article examine a regression-type scenario using DIOD with the goal of preserving the inferential link between two variables. Using the point-kinetics equations, regression experiments compare the performance of a model trained using the original data to that of a model trained using the obfuscated data, which produced identical results. Our claim is further strengthened by an information theoretic proof and experiment, which showed that the inferential content between variables remains the same after obfuscation, thereby avoiding the required communication of the proprietary data.

47 - OTHER INSTRUMENTATION↗
DOE OSTI2024

Simple Scattering: Lipid nanoparticle structural data repository

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Statistical interpretation of the Amerithrax “morph” assay results

In the Amerithrax investigation PCR–based “morph assays” were used to link the anthrax letters with the RMR–1029 flask at USAMRIID. Quantitative data reported for several of these assays are not consistent with Poisson sampling statistics, but instead exhibit “Taylor's Law” behavior where the variance greatly exceeds the mean. A plausible statistical model for this behavior can explain the large number of observed negative and “inconclusive” findings, and implies a high likelihood that a repository sample could contain a “morph” mutant at concentrations well above the nominal detection limit but nonetheless give a negative or inconclusive test result. A Bayesian framework relates the assay results to the probability that a sample actually contains all four morph mutants, even though it tested negative for at least one. The analysis implies that the observed false negative rate actually does not significantly weaken the conclusion that the morph assays correctly excluded all but the stocks derived from RMR–1029 as possible sources of the letter powders, at least when the test results were unambiguous. In conclusion, these findings expand upon and resolve some of the issues cited in recent reviews, and indicate the importance of developing a rigorous statistical framework for interpreting “morph” assay data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Association of small vessel disease with tau pathology

Emerging evidence suggests that small vessel disease (SVD) is a risk factor for clinical dementia and may contribute to AD neuropathological changes. Watershed brain regions are located at the most distal areas between arterial territories, making them vulnerable to SVD-related changes. We examined the association of pathologic markers of SVD, specifically arteriolosclerosis in watershed brain regions, with AD pathologic changes. Participants (N= 982; mean age-at-death = 90; 69% women) were enrolled as part of one of two cohort studies of aging and dementia. At autopsy, neuropathological evaluation included semi-quantitative grading of arteriolosclerosis pathology from 2 cortical watershed regions: the anterior watershed (AWS) and posterior watershed (PWS), densities for cortical β-amyloid and tau-tangle pathology, and other common age-related pathologies. Linear regression models examined the association of watershed arteriolosclerosis pathology with β-amyloid and tau-tangle burden. In follow-up analyses, available ex-vivo MRI and proteomics data in a subset of decedents were leveraged to examine the association of whole brain measure of WMH, as a presumed MRI marker of SVD, with β-amyloid and tau-tangle burden, as well as to examine the association of watershed arteriolosclerosis with proteomic tau. Watershed arteriolosclerosis was common, with 45% of older persons having moderate-to-severe arteriolosclerosis pathology in the AWS region, and 35% in the PWS. In fully adjusted models that controlled for demographics and common age-related pathologies, an increase in severity of PWS arteriolosclerosis was associated with a higher burden of tau-tangle burden, specifically neocortical tau burden, but not with β-amyloid. AWS arteriolosclerosis was not associated with β-amyloid or tau pathology. Ex-vivo WMH was associated with greater tau-tangle pathology burden but not β-amyloid. Furthermore, PWS arteriolosclerosis was associated with higher abundance of tau phosphopeptides, that promote formation of tau aggregates. These data provide compelling evidence that SVD, specifically posterior watershed arteriolosclerosis pathology, is linked with tau pathological changes in the aging brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

AIRE-induced apoptosis is associated with nuclear translocation of stress sensor protein GAPDH

Highlights: Black-Right-Pointing-Pointer AIRE induces apoptosis in epithelial cells. Black-Right-Pointing-Pointer CARD domain of AIRE is sufficient for apoptosis induction. Black-Right-Pointing-Pointer AIRE induced apoptosis involves GAPDH translocation to the nuclei. Black-Right-Pointing-Pointer Deprenyl inhibits AIRE induced apoptosis. -- Abstract: AIRE (Autoimmune Regulator) has a central role in the transcriptional regulation of self-antigens in medullary thymic epithelial cells, which is necessary for negative selection of autoreactive T cells. Recent data have shown that AIRE can also induce apoptosis, which may be linked to cross-presentation of these self-antigens. Here we studied AIRE-induced apoptosis using AIRE over-expression in a thymic epithelial cell line as well as doxycycline-inducible HEK293 cells. We show that the HSR/CARD domain in AIRE together with a nuclear localization signal is sufficient to induce apoptosis. In the nuclei of AIRE-positive cells, we also found an increased accumulation of a glycolytic enzyme, glyceraldehyde-3-phosphate (GAPDH) reflecting cellular stress and apoptosis. Additionally, AIRE-induced apoptosis was inhibited with an anti-apoptotic agent deprenyl that blocks GAPDH nitrosylation and nuclear translocation. We propose that the AIRE-induced apoptosis pathway is associated with GAPDH nuclear translocation and induction of NO-induced cellular stress in AIRE-expressing cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Diphenyleneiodonium enhances oxidative stress and inhibits Japanese encephalitis virus induced autophagy and ER stress pathways

Highlights: 1.Diphenyleneiodonium (DPI) enhances ROS levels in Japanese encephalitis virus (JEV) infection. 2.DPI blocks autophagy and activation of ER stress pathways in JEV infected neuronal cells. 3.DPI inhibits JEV particle release. 4.During JEV infection autophagy and ER stress are likely to be modulated by DPI sensitive enzymes. Diphenyleneiodonium (DPI) and N-acetyl-l-cysteine (NAC), two widely used anti-oxidants, were employed to evaluate the role of oxidative stress in Japanese encephalitis virus (JEV) induced autophagy, stress responses and replication. DPI and NAC exerted opposite effects on ROS levels in JEV infected mouse neuronal cells (Neuro2a), mouse embryonic fibroblasts (MEFs) and human epithelial cells (HeLa). While NAC effectively quenched ROS, DPI enhanced ROS levels, suggesting that DPI induces oxidative stress in JEV infected cells. DPI treatment of JEV infected Neuro2a cells further blocked autophagy induction and activation of all three arms of the ER stress pathway, and, inhibited virus particle release. Autophagy induction in JEV infection has been previously shown to be linked to the activation of XBP1 and ATF6 ER stress sensors. Our data suggests that DPI mediated block of autophagy is a result of inhibition of ER stress responses and is not associated with an anti-oxidative effect. Since DPI has a wide inhibitory potential for all Flavin dependent enzymes, it is likely that the signalling pathways for ER stress and autophagy during JEV infection are modulated by DPI sensitive enzymes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Biofilm formation and genomic features of Listeria monocytogenes strains isolated from meat and dairy industries located in Piedmont (Italy)

Listeria monocytogenes is considered a major challenge for the food industry as it can persist for long periods in food processing plants by forming biofilms. The aims of this study were: i) to assess the biofilm producing ability of 57 Listeria monocytogenes isolates previously subjected to whole-genome sequencing (WGS); ii) to compare the levels of biofilm formation with the presence or absence of biofilm associated genes. To determine the presence or absence of a known set of biofilm associated genes, a comparative genomic analysis was performed on each strain. Among Listeria monocytogenes isolates, 58 %, 38.5 % and 3.5 % exhibited weak, moderate or strong biofilm production, respectively. No difference in biofilm production was observed between food and environmental isolates. The percentage of Listeria monocytogenes strains isolated from meat products (57 %) classified as moderate or strong biofilm producers was higher than the percentage obtained for strains isolated from dairy products (28 %). Here, the presence of the Stress Survival Islet 1, the arsD stress gene and the truncated inlA protein was significantly associated with increased levels of biofilm. Combining biofilm phenotype with molecular and genotyping data may provide the opportunity to better understand the relationship between genes linked to biofilm formation in Listeria monocytogenes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Dietary flavonoid derivatives enhance chemotherapeutic effect by inhibiting the DNA damage response pathway

Flavonoids are the most common group of polyphenolic compounds and abundant in dietary fruits and vegetables. Diet high in vegetables or dietary flavonoid supplements is associated with reduced mortality rate for patients with breast cancer. Many studies have been proposed for mechanisms linking flavonoids to improving chemotherapy efficacy in many types of cancers, but data on this issue is still limited. Herein, we report on a new mechanism through which dietary flavonoids inhibit DNA damage checkpoints and repair pathways. We found that dietary flavonoids could inhibit Chk1 phosphorylation and decrease clonogenic cell growth once breast cancer cells receive ultraviolet irradiation, cisplatin, or etoposide treatment. Since the ATR-Chk1 pathway mainly involves response to DNA replication stress, we propose that flavonoid derivatives reduce the side effect of chemotherapy by improving the sensitivity of cycling cells. Therefore, we propose that increasing intake of common dietary flavonoids is beneficial to breast cancer patients who are receiving DNA-damaging chemotherapy, such as cisplatin or etoposide-based therapy. - Highlights: • First report on inhibition of both DNA damage and repair by dietary flavonoids • Dietary flavonoids inhibit cisplatin- and UV-induced Chk1 phosphorylation. • Flavonoids combined with cisplatin or UV treatment show notable growth inhibition. • Promising treatment proposal for patients who are receiving adjuvant chemotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Community standards and future opportunities for synthetic communities in plant–microbiota research

Harnessing beneficial microorganisms is seen as a promising approach to enhance sustainable agriculture production. Synthetic communities (SynComs) are increasingly being used to study relevant microbial activities and interactions with the plant host. Yet, the lack of community standards limits the efficiency and progress in this important area of research. Here, to address this gap, we recommend three actions: (1) defining reference SynComs; (2) establishing community standards, protocols and benchmark data for constructing and using SynComs; and (3) creating an infrastructure for sharing strains and data. We also outline opportunities to develop SynCom research through technical advances, linking to field studies, and filling taxonomic blind spots to move towards fully representative SynComs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics

The human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Improving DFIG performance under fault scenarios through evolutionary reinforcement learning based control

The double-fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage during system fault events. In this paper, a novel data-driven approach is proposed to enhance the DFIG performance under fault scenarios. An advanced reinforcement learning (RL) algorithm called guided surrogate-gradient-based evolution strategy (GSES) is used to control the DFIG power and capacitor DClink voltage by adjusting the optional reference signals. This controller is able to prevent the rotor of DFIG from overcurrent risk and maintain the grid-connected operation. The proposed GSES-based control algorithm was evaluated through simulations on a 3.6-MW DFIG in PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSESbased control algorithm in improving the DFIG performance under fault scenarios.

42 ENGINEERING↗
DOE OSTI2015

Magnetic Random Access Memory based non-volatile asynchronous Muller cell for ultra-low power autonomous applications

Micro and nano electronic integrated circuit domain is today mainly driven by the advent of the Internet of Things for which the constraints are strong, especially in terms of power consumption and autonomy, not only during the computing phases but also during the standby or idle phases. In such ultra-low power applications, the circuit has to meet new constraints mainly linked to its changing energetic environment: long idle phases, automatic wake up, data back-up when the circuit is sporadically turned off, and ultra-low voltage power supply operation. Such circuits have to be completely autonomous regarding their unstable environment, while remaining in an optimum energetic configuration. Therefore, we propose in this paper the first MRAM-based non-volatile asynchronous Muller cell. This cell has been simulated and characterized in a very advanced 28 nm CMOS fully depleted silicon-on-insulator technology, presenting good power performance results due to an extremely efficient body biasing control together with ultra-wide supply voltage range from 160 mV up to 920 mV. The leakage current can be reduced to 154 pA thanks to reverse body biasing. We also propose an efficient standard CMOS bulk version of this cell in order to be compatible with different fabrication processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗