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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Relevant biochar characteristics influencing compressive strength of biochar-cement mortars

To counteract the contribution of CO 2 emissions by cement production and utilization, biochar is being harnessed as a carbon-negative additive in concrete. Increasing the cement replacement and biochar dosage will increase the carbon offset, but there is large variability in methods being used and many researchers report strength decreases at cement replacements beyond 5%. This work presents a reliable method to replace 10% of the cement mass with a vast selection of biochars without decreasing ultimate compressive strength, and in many cases significantly improving it. By carefully quantifying the physical and chemical properties of each biochar used, machine learning algorithms were used to elucidate the three most influential biochar characteristics that control mortar strength: initial saturation percentage, oxygen-to-carbon ratio, and soluble silicon. These results provide additional research avenues for utilizing several potential biomass waste streams to increase the biochar dosage in cement mixes without decreasing mechanical properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation

Demand response (DR) is regarded as an effective tool to mitigate the operational uncertainties and enhance the reliability of power supply in smart grids. However, with the time-varying attribute, to what extent DR could be committed is a major concern for utilities. This paper proposes a new approach to assess the capacity value (CV) of DR with a methodological framework developed for the uncertainty modeling of DR. The novelty of this framework is its inclusion of both physical and human-related analyses in DR programs, which allows the characterization of DR variability to be accurate for the CV estimation. To achieve this, the demand-side activities in DR are disaggregated into several modules as load usage, contract selection and actual performance. Based on their intrinsic properties, different parametric models are proposed to represent the impact of each technical/social factor on the availability of DR. The parameters of these models are determined using learning based algorithms to adapt to various behavior patterns of consumers. The outputs of the framework will serve as the quantifiers of DR capability and are integrated into reliability-based CV evaluation. The results of case studies verify the effectiveness of the proposed methodology.

Capacity value↗
DOE OSTI2021

Dendritic cells focus CTL responses toward highly conserved and topologically important HIV-1 epitopes

During early HIV-1 infection, immunodominant T cell responses to highly variable epitopes lead to the establishment of immune escape virus variants. Here we assessed a type 1-polarized monocyte-derived dendritic cell (MDC1)-based approach to selectively elicit cytotoxic T lymphocyte (CTL) responses against highly conserved and topologically important HIV-1 epitopes in HIV-1-infected individuals from the Thailand RV254/SEARCH 010 cohort who initiated antiretroviral therapy (ART) during early infection (Fiebig stages I-IV). Autologous MDC1 were used as antigen presenting cells to induce in vitro CTL responses against HIV-1 Gag, Pol, Env, and Nef as determined by flow cytometry and ELISpot assay. Ultra-conserved or topologically important antigens were respectively identified using the Epigraph tool and a structure-based network analysis approach and compared to overlapping peptides spanning the Gag proteome. MDC1 presenting either the overlapping Gag, Epigraph, or Network 14-21mer peptide pools consistently activated and expanded HIV-1-specific T cells to epitopes identified at the 9-13mer peptide level. Interestingly, some CTL responses occurred outside known or expected HLA associations, providing evidence of new HLA-associated CTL epitopes. Comparative analyses demonstrated more sequence conservation among Epigraph antigens but a higher magnitude of CTL responses to Network and Gag peptide groups. Importantly, CTL responses against topologically constrained Gag epitopes contained in both the Network and Gag peptide pools were selectively enhanced in the Network pool-initiated cultures. Our study supports the use of MDC1 as a therapeutic strategy to induce and focus CTL responses toward putative fitness-constrained regions of HIV-1 to prevent immune escape and control HIV-1 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Pulsed Electrolysis Promotes Catalyst Activity in Dilute CO 2 Streams

Industrial CO 2 streams vary widely in composition, from pure to as low as 3%, posing challenges for purification or direct conversion. Electrochemical reduction offers a route for converting dilute CO 2 streams but faces severe mass transport limitations. This study demonstrates that pulsed electrolysis effectively overcomes these limitations, enhancing CO 2 electroreduction across variable feed compositions and current densities, particularly at low CO 2 concentrations and high current densities. At 25% CO 2 and 400 mA cm −2 , pulsing improved selectivity from 25.6 to 78.6%, production rate from 13.7 to 21.0 mol m −2 h −1 , and energy productivity from 0.77 to 2.59 mol kWh −1 . A dynamic, multiphysics continuum model confirms a 64% increase in CO 2 concentration within the catalyst layer during pulsing, resolving the transient chemical microenvironment. These findings establish pulsed electrolysis as a viable strategy for converting dilute industrial CO 2 streams into valuable feedstocks, bypassing costly pre-separation.

Orfali, Dania Muhieddine [New York University (NYU↗
DOE OSTI2018

Structural principles of distinct assemblies of the human α4β2 nicotinic receptor

Fast chemical communication in the nervous system is mediated by neurotransmitter-gated ion channels. The prototypical member of this class of cell surface receptors is the cation-selective nicotinic acetylcholine receptor. As with most ligand-gated ion channels, nicotinic receptors assemble as oligomers of subunits, usually as hetero-oligomers and often with variable stoichiometries. This intrinsic heterogeneity in protein composition provides fine tunability in channel properties, which is essential to brain function, but frustrates structural and biophysical characterization. The α4β2 subtype of the nicotinic acetylcholine receptor is the most abundant isoform in the human brain and is the principal target in nicotine addiction. This pentameric ligand-gated ion channel assembles in two stoichiometries of α- and β-subunits (2α:3β and 3α:2β). Both assemblies are functional and have distinct biophysical properties, and an imbalance in the ratio of assemblies is linked to both nicotine addiction and congenital epilepsy. Here we leverage cryo-electron microscopy to obtain structures of both receptor assemblies from a single sample. Antibody fragments specific to β2 were used to ‘break’ symmetry during particle alignment and to obtain high-resolution reconstructions of receptors of both stoichiometries in complex with nicotine. Furthermore, the results reveal principles of subunit assembly and the structural basis of the distinctive biophysical and pharmacological properties of the two different stoichiometries of this receptor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Random permutations fix a worst case for cyclic coordinate descent

Abstract Variants of the coordinate descent approach for minimizing a nonlinear function are distinguished in part by the order in which coordinates are considered for relaxation. Three common orderings are cyclic (CCD), in which we cycle through the components of $x$ in order; randomized (RCD), in which the component to update is selected randomly and independently at each iteration; and random-permutations cyclic (RPCD), which differs from CCD only in that a random permutation is applied to the variables at the start of each cycle. Known convergence guarantees are weaker for CCD and RPCD than for RCD, though in most practical cases, computational performance is similar among all these variants. There is a certain type of quadratic function for which CCD is significantly slower than for RCD; a recent paper by Sun & Ye (2016, Worst-case complexity of cyclic coordinate descent: $O(n^2)$ gap with randomized version. Technical Report. Stanford, CA: Department of Management Science and Engineering, Stanford University. arXiv:1604.07130) has explored the poor behavior of CCD on functions of this type. The RPCD approach performs well on these functions, even better than RCD in a certain regime. This paper explains the good behavior of RPCD with a tight analysis.

Lee, Ching-pei↗
DOE OSTI2020

Multi‐hypothesis comparison of Farquhar and Collatz photosynthesis models reveals the unexpected influence of empirical assumptions at leaf and global scales

Abstract Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation ( A ). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO 2 concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi‐hypothesis methods (that account for both hypothesis and parameter variability) for process‐level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting‐rate selection. Each of the four processes comprises 1–3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high‐resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting‐rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting‐rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A . Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%–10%, equivalent to 50%–160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi‐hypothesis methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Entrainment–Mixing Characteristics through Direct Comparisons of Drop Size Distributions Using In Situ Observations from ACE-ENA

Abstract Constraining the impacts of entrainment and associated mixing (i.e., entrainment–mixing) on cloud properties continues to be difficult, partly due to observational uncertainties as well as a lacking consensus of which methodologies for diagnosing entrainment–mixing are most appropriate. This study introduces a novel method to evaluate the presence and degree of inhomogeneous and homogeneous mixing using ∼100 h of in situ observations from a research aircraft over the northeastern Atlantic. Specifically, drop size distributions are compared between regions containing negligible and significant entrainment for select flight legs, making a direct characterization of the degree of homogeneous and inhomogeneous mixing possible. A measure of drop concentration variance is used as a proxy variable to diagnose entrainment–mixing. Results correspond well with entrainment–mixing metrics, showing lower Damköhler numbers where drop size distributions shift toward smaller drop sizes (i.e., inhomogeneous mixing) and greater transition length scales where drop size distributions do not (i.e., homogeneous mixing). Inhomogeneous mixing occurs in most samples from Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA) (regardless of homogeneous mixing frequencies increasing with increasing spatial resolution from ∼100 to ∼10 m) and is associated with decreased drop size relative dispersion and both greater aerosol and drop concentrations compared with homogeneous mixing. Precipitating clouds have a greater frequency of homogeneous mixing compared with nonprecipitating clouds. The proposed methodology is similarly applied to in situ observations of southeast Pacific stratocumulus, shallow convective clouds over central Oklahoma and low-level clouds over the Southern Ocean. All four locations are primarily dominated by inhomogeneous mixing with minimal variability among each region.

Clouds↗
DOE OSTI2017

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Wastewater Disposal Has Not Significantly Altered the Regional Stress State in Southern Kansas

Wastewater disposal is primarily responsible for the increased seismicity rate since ~2013 in southern Kansas. Previous work that used shear-wave splitting (SWS) in southern Kansas interpreted an ~90° temporal rotation in the fast polarization direction and attributed it to increased pore pressures resulting from fluid injection. However, this interpreted rotation coincided with a change in the stations used to make the SWS measurements. We investigate the temporal variability of fast azimuths in southern Kansas by making SWS measurements on earthquake families with similar source–receiver paths recorded on a stable local seismic network. We select high-quality SWS measurements by investigating the stability of results across 65 different frequency bands between 0.5 and 15 Hz. We find that the fast polarization direction in southern Kansas is relatively constant with an average east-northeast (~N79°E) orientation between 2014 and 2017. Our fast polarization measurements are primarily a reflection of the maximum principal horizontal stress direction (SHmax). We observe a slight spatial change in SHmax to the northeast (~N55°E) near the Nemaha ridge in Oklahoma. However, we do not observe any significant temporal rotation of SHmax or variation in delay time (i.e., crack density) in southern Kansas, contrary to the earlier study. The previously interpreted ~90° rotation may either be a reflection of a very local stress change or a misinterpretation of SWS results potentially due to the use of inconsistent source–receiver paths. Our SWS measurements cover the period of peak wastewater disposal and seismicity rates and suggest an absence of significant temporal rotations in the local anisotropy and stress orientations associated with wastewater disposal.

Geochemistry & Geophysics↗
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗
DOE OSTI2018

Development of Hot-Isostatic Pressing for Treatment of UK ILW Radioactive Wastes - 18280

Hot-Isostatic Pressing (HIPing) has been proposed as an improved method for the treatment of radioactive wastes in the UK. Sludges and slurries housed on the Sellafield site are a prime candidate for HIP treatment due to their large volumes and issues associated with the use of the baseline cementation strategy. Options have been developed by GeoRoc Ltd to co-process these wastes, including a detailed conceptual plant design. This paper will describe research and development aimed at demonstrating the viability of the concept presented in this design. This paper summarizes two studies performed to address the knowledge gaps identified for treatment of these wastes using HIP technology. The first study related to the poorly characterized nature of the wastes, which requires any treatment option to be flexible in terms of the chemical and physical properties of the waste feed. The second study involved demonstrating that HIPing can be effectively utilized at a scale applicable to treat a waste inventory estimated to be in excess of 5,000 m{sup 3}. The first section of this paper presents a summary of research carried out to determine where variation in the waste stream composition may affect the processing and wasteform produced when HIPing Magnox sludges. This study aimed to qualify where restrictions on the process envelope may exist due to poorly defined waste-stream characteristics. A baseline wasteform was selected with the aim of achieving significant waste volume reduction, while producing a physically and chemically stable wasteform. A glass-bonded magnesium-titanate/magnesium-silicate wasteform was selected as the preferred option for the Magnox sludge wastes. A series of studies investigated the densification and phase formation of waste-forms by altering key variables in the Magnox sludge simulant. To test the extremes of the processable waste loading, samples were produced and characterized at 100 wt% waste-loading for Magnox sludge, Site-Ion Exchange Plant (SIXEP) sand/clinoptilolite and a 50:50 mix of the two waste-streams. Further studies involved a) altering the waste loading to additive mass ratio (to simulate changes in the waste loading/sludge solids content), b) varying the particle size distribution from the datum to include only small or large particulates in the sludge feed; and c) introducing up to 20 wt% metallic Magnox alloy, organic species or carbonate materials (to determine how their inclusion may affect the processing and densification of the materials). A U{sub 3}O{sub 8} loaded sample was also produced using the baseline Magnox sludge formulation. All the tests performed were shown to produce monolithic, well densified waste-forms with substantial volume reductions. Minimal variations from the baseline were noted but the waste-forms produced with these deviations was determined to be of comparable quality to of the baseline product. U{sub 3}O{sub 8} was shown to behave in a similar fashion to the ZrO{sub 2} surrogate used to simulate it in the process envelope studies and was encapsulated in the magnesium titanate/magnesium silicate matrix. This work demonstrates that HIP technology can successfully produce waste-forms across the broad process envelope expected in the Magnox sludge inventory. It is also possible to produce monolithic waste-forms at 100 wt% waste loading from Magnox sludges, SIXEP sand clinoptilolite slurries or a co-processed mixture of the two. The second part of this paper summarizes a commercially relevant demonstration of HIP technology for the treatment of sludge and slurry wastes housed at the Sellafield site (UK). This study culminated in production of the largest HIPed wasteform successfully produced to date. This study was performed in a series of stages. Investigations into formulation and properties of waste-forms were performed at a lab scale to evaluate suitable wasteform systems and identify suitable combinations of additives and waste loadings. Scale up demonstrations were then performed in stages with custom-designed HIP canisters. Scale-up and characterization of key wasteform properties were successfully performed with initial canister volumes of 50 ml, 400 ml and 8 L. Suitable front-end unit operations to produce a granular, free-flowing calcined simulant were demonstrated at an engineering scale. The demonstration was successful in processing a HIP canister with an initial starting volume in excess of 100 L. A 75 % volume reduction from the raw sludge was achieved at scale while demonstrating that HIP is applicable at suitably large scale for immobilization of ILW waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Oxaliplatin and irinotecan induce heterogenous changes in the EMT markers of metastasizing colorectal carcinoma cells

Highlights: • Significant variability and lower expression uniformity in lymph node metastatic cells. • Heterogeneity of cell response partially determined by cancer cell microenvironment. • Metastatic cells proved to be less sensitive to chemotherapy treatment compared to primary site cancer cells. • Irinotecan and oxaliplatin affected EMT markers in primary cell lines. In patients with advanced colorectal cancer (CRC), surgery is complemented with systemic therapy – chemotherapy and radiochemotherapy. Although the patients’ overall survival has been significantly improved, tumor resistance is still a frequent cause of chemotherapy failure. Several factors contribute to chemoresistance of tumor cells including changes related with epithelial-mesenchymal transition (EMT). The present study was designed to verify the presence of EMT markers in paired CRC primary cell lines obtained from primary tumor sites and lymph node metastases of three patients and to investigate the effect of irinotecan and oxaliplatin treatment on these markers as well. The samples of the higher stage of CRC and positive for angioinvasion were selected and qPCR, western blot analysis, migration assay, cytotoxicity testing was performed. Results confirmed the increased expression of several markers characteristic of EMT and invasiveness in lymph node metastatic cells, with a significant variability between individual samples. Irinotecan and oxaliplatin decreased migration activity of the cells and to the varying degree affected the expression of EMT and invasiveness markers. In conclusion, in CRC EMT is present in metastatic cells over a phenotypic continuum whose expression is altered heterogeneously upon irinotecan and oxaliplatin treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The need for ion-exchange membranes with high charge densities

Ion-exchange membranes (IEMs) are essential for controlling ion transport in electrochemical membrane-based technologies for water purification, energy generation, and energy storage. In general, IEMs must exhibit high ionic conductivity and selectivity, both of which are significantly influenced by the charge and water content of the hydrated membrane. In this perspective, we compiled the charge and water contents of approximately 1,000 IEMs reported over the past several decades to assess their range and variability, as well as to reveal gaps and set clear targets for future membrane design. We compared the charge and water content of negatively and positively charged membranes, as well as membranes developed for desalination and energy applications. The majority of IEMs, regardless of structure or intended application, have volumetric charge densities below 3 mol/L[membrane]. This narrow range of charge densities limits the performance of IEMs and contributes to the observed tradeoff between ion throughput and selectivity. It is critical to develop IEMs with higher charge densities to overcome this performance limitation. Further, we highlighted several design strategies that yielded IEMs with fixed charge densities higher than 3 mol/L[membrane]. These strategies provide valuable insights and can guide the design of next-generation IEMs with high charge densities.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Characterization of Covetic Materials for Process Control. Final CRADA report

GDC Industries LLC has been performing process research on covetic materials since its formation in February 2015. With assistance from DOE and the Air Force via SBIR projects, GDC focused on the enhancement of aluminum alloys to covetics to replicate a previously reported electrical conductivity of about 67% IACS (International Annealed Copper Standard). GDC has established a joint development agreement with General Cable Corporation, wherein General Cable agreed to perform numerous extrusions of as cast covetics and subsequent tensile and electrical conductivity tests on the resultant wire. Covetics conversion was proven but the significant improvement is yet to be replicated. To control the experiments towards enhanced process control, a series of designed experiments was created by an expert in process control from the automotive industry. Six process variables were identified and a designed experiment matrix requiring 18 independent conversions was created at GDC. The result of those designed experiments has now allowed GDC to focus on two process variables, namely, residence time and DC current in the conversions with the other four variable held constant. While this work is useful in replicating the earlier enhancements, GDC feels it will not be sufficient for the necessary process knowhow for scale up required by industry as GDC still does not have a direct correlation of the process variables to the actual carbon in the finished covetics. Since 2015, GDC has been aware of the capabilities existing at Argonne National Laboratory for performing carbon analysis that exceeds the precision commonly available to Industry. GDC and ANL submitted a proposal in response to the DOE-EERE – Small Business Vouchers Pliot Program announcement and the proposal was selected for funding. GDC will melt-process covetic aluminum samples following their design of experiment criteria and have the as-casted samples extruded by their partner, General Cable. Argonne will measure the conductivity, analyze carbon content, perform X-ray diffraction, and examine the microstructural features of the samples. Careful process variable measurements will be recorded by each party such that correlations can be developed indicating process parameters that yield the best carbon concentration in covetics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2019

Scalable Preconditioning of Block-Structured Linear Algebra Systems using ADMM

In this paper, we study the solution of block-structured linear algebra systems arising in optimization by using iterative solution techniques. These systems are the core computational bottleneck of many problems of interest such as parameter estimation, optimal control, network optimization, and stochastic programming. Our approach uses a Krylov solver (GMRES) that is preconditioned with an alternating method of multipliers (ADMM). We show that this ADMM-GMRES approach overcomes well-known scalability issues of Schur complement decomposition in problems that exhibit a high degree of coupling. The effectiveness of the approach is demonstrated using linear systems that arise in stochastic optimal power flow problems and that contain up to 2 million total variables and 4,000 coupling variables. We find that ADMM-GMRES is nearly an order of magnitude faster than Schur complement decomposition. Moreover, we demonstrate that the approach is robust to the selection of the augmented Lagrangian penalty parameter, which is a key advantage over the direct use of ADMM.

97 MATHEMATICS AND COMPUTING↗