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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 253 records · Page 14

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗

Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing

Deep learning (DL) has been used for many natural language processing (NLP) tasks due to its superior performance as compared to traditional machine learning approaches. In DL models for NLP, words are represented using word embeddings, which capture both semantic and syntactic information in text. However, 90-95% of the DL trainable parameters are associated with the word embeddings, resulting in a large storage or memory footprint. Therefore, reducing the number of word embedding parameters is critical, especially with the increase of vocabulary size. In this work, we propose a novel approximate word embeddings approach for convolutional neural networks (CNNs) used for text classification tasks. The proposed approach significantly reduces the number of model trainable parameters without noticeably sacrificing in computing performance accuracy. Compared to other techniques, our proposed word embeddings technique does not require modifications to the DL model architecture. We evaluate the performance of the the proposed word embeddings on three classification tasks using two datasets, composed of Yelp and Amazon reviews. The results show that the proposed method can reduce the number of word embeddings parameters by 98% and 99% for the Yelp and Amazon datasets respectively, with no drop in computing accuracy.

Alawad, Mohammed↗

FASTQ quality control dashboard

FQCDB builds up existing open source software, FastQC, implementing a modern web interface for across parsed output of FastQC. In addition, FQCDB is extensible as a web service to include additional plots of type line, boxplot, or heatmap, across data formatted according to guidelines. The interface is also configurable via more readable JSON format, enabling customization by non-web programmers.

Source record↗

Signal Transduction Pathways of Chloroplast Quality Control

Chloroplasts (photosynthetic organelles) of higher plants contain about 3000 proteins, but fewer than 5% of these proteins are encoded by genes in the chloroplast. The rest are encoded by genes in the nucleus, which is a separate organelle within the plant cell. To avoid the accumulation of dangerous reactive oxygen species that are the inherent by-product of photosynthesis, gene expression of these spatially separated genomes is regulated by two-way signaling. Thus, while plastid differentiation and development are largely controlled by the nucleus and the proteins encoded within, developmentally arrested or damaged plastids can regulate expression of nuclear genes via retrograde signaling pathways. During the early years of this DOE-funded study, we performed a number of genetic screens to identify genes and protein involved in these signals. Based on the genes that we identified, we found that chloroplast gene expression and the chlorophyll (the major light-harvesting pigment that gives plant tissue its characteristic green color) biosynthetic pathway can act as the source of both positive and stress-related retrograde signals. We also found a new protein called GUN1, that plays a crucial role in retrograde signaling. Although the exact molecular mechanism of GUN1 remains a mystery, our work showed that it likely acts in the chloroplast to coordinate chloroplast gene expression with nuclear gene expression and to ensure that seedlings can quickly green after germination. Using that work as a foundation, we have recently uncovered a new type of retrograde signal that leads to the selective removal of damaged chloroplasts from the cell. We have only begun the characterization of this pathway, but our initial studies have suggested that this may be an extremely important mechanism that allows plants to adapt to stressful conditions. Together this will aid in the understanding of the developmental control of photosynthesis, the chloroplast assembly/disassembly pathways, and how plants adapt to stressful conditions. Ultimately, we hope this work will allow us to begin to engineer these crucial pathways into agriculturally important or bioenergy rich crops. This will be crucial to our quest for an abundant food supply and cheap, dependable sources of energy.

59 BASIC BIOLOGICAL SCIENCES↗

Targeting Maturation and Quality Control of Photosynthetic Membrane Proteins

Plsp1 is a type I signal peptidase that is responsible for removing the targeting signal peptide from passenger proteins upon their delivery to the thylakoid lumen. Plsp1 is itself a resident of the thylakoid membrane, with a single membrane anchor and the catalytic domain facing the lumen. Work to uncover the targeting mechanism for this protein has been underway in the Inoue lab from the previous DOE funding period has established that Plsp1 is targeted at the thylakoid by the Sec1 translocase, and that it passes through a high molecular weight intermediate in the stroma en route to the thylakoid membrane. During this funding period we have demonstrated by MS/MS of immunoprecipitated samples that the high molecular weight complex containing Plsp1 in the stroma contains primarily the α1 and β1 forms of Cpn60 in approximately equal amounts. Mixing experiments with overexpressed purified proteins have reproduced the Cpn60/Plsp1 complex. Surprisingly, the co-chaperonins Cpn10 and Cpn20 were not detected in the immunoprecipitated sample, even though they are able to bind to the Cpn60/Plsp1 complex formed in mixing experiments. We have further confirmed that the addition of purified stromal cpSecA1 results in a decrease in the amount of Plsp1 successfully incorporated into the thylakoid membrane. We are currently working on experiments to describe the conditions for the handoff between Cpn60 and cpSec1 in the stroma prior to membrane targeting. The structure of Plsp1 can be inferred by threading its sequence onto the structure of its E. coli homologue LepB to contain a single membrane anchor followed by a soluble structure consisting of ten β strands. In the last DOE funding period it was determined that the transmembrane domain not only acts as the membrane anchor, but probably mediates the interaction with cpSecA1. We have now determined that the binding to Cpn60 is likely mediated by β strands 1 and 2. What is the mechanism and significance of oxidative folding of Plsp1? It was found during the last DOE funding period that Plsp1 purified after overexpression in E. coli loses its peptidase activity upon reduction of its conserved cysteines. We have now determined that Plsp1 undergoes a measurable conformational change upon reduction, both by monitoring susceptibility to protease and by native tryptophan fluorescence. We have also shown that Plsp1 is at least partly, and likely completely, oxidized in the thylakoid lumen. While these results were not unexpected, experiments on Plsp1 in vivo were completely surprising. We have known for years (from work conducted with prior DOE funding) that a Plsp1 knockout is embryo lethal. Accordingly, given that reduction of the isolated protein inactivates it, we expected that plants in which one or both of the cysteine pair were mutated would also lead to a severe mutant phenotype. This was not observed, and in fact, these mutants complemented fully the plsp1 plants. This lead to the hypothesis that either association with an interacting protein or with the membrane preserves Plsp1structure even when the disulfides are reduced. Work performed in the last DOE funding cycle revealed that Pgrl1, a protein reportedly involved in cyclic electron flow, interacts stably with Plsp1. However, the Plsp1 cysteine-less mutants are still able to compliment the plsp1 mutant even in the pgrl1 knockout mutant background. We have also found that in vitro activity of Plsl1 is not abolished by reduction in thylakoids that have been permeabilized by low concentrations of detergent, whereas the activity of fully extracted protein is. This points to membrane association as the condition important to activity, and by extension, structure preservation. We are presently working to isolate Plsp1 in a lipid nanodisk to test this hypothesis.

59 BASIC BIOLOGICAL SCIENCES↗

Quality Control of and Analysis Enabling Use of MARCUS and MICRE data for Scientific Applications

The primary objective of this project was to provide a preliminary investigation of data collected during the MARCUS/MICRE field experiments and to produce products and analysis for the scientific community that will enable future scientific investigations and hypothesis testing. Further, the goal was to assist those in the scientific community to understand the strengths and caveats of the MARCUS/MICRE data so that members of the scientific community could use the data in their publications.

54 ENVIRONMENTAL SCIENCES↗

Development of Quality Control Methods for Robust and Reliable Sensor Design

The successful adoption of additive manufacturing for the rapid prototyping of printed sensors requires the establishment of quantifiable metrics that can interrogate device performance. One measure of performance is the adhesion strength between the substrate and sensor interface, which is critical since the strength of this interface can dictate the accuracy and reliability of the printed sensor. In this work, a non-contact laser-induced spallation technique was used to quantify the adhesion of silver prints on an aluminum alloy substrate. The laser-based method was compared to a standardized pull-off adhesion test, which provided baseline measurements of adhesion strength. The sintering conditions were shown to play an important role in the film-substrate adhesion strength, as well as the cohesion and ductility of the film itself. The methods and results described in this work supports the establishment of process control steps that are necessary for verifying the reliability of printed devices prior to its deployment in critical experiments.

36 MATERIALS SCIENCE↗

Solid-State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control (Final Technical Report)

Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.

03 NATURAL GAS↗

California - Quality-Controlled Reanalysis and Observational Data for Buoy (130), Morro Bay / Derived Data

This collection provides spatiotemporally paired reanalysis and satellite data to supplement the lidar buoy observations during the California deployments. Point time series of observed and reanalysis data are provided, using inverse distance weighting to geolocate the reanalysis data to the buoy location. Gridded time series of reanalysis data are provided, covering the California coast and extending into the Pacific Ocean. Satellite data in the coastal California region are also provided. The reanalysis collection includes the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP). The satellite data collection is that of Ribal and Young (2019). All datasets are publicly available with the sources provided in the References section.

17 WIND ENERGY↗

California - Quality-controlled Reanalysis and Observational Data for Buoy (120), Humboldt / Derived Data

This collection provides spatiotemporally paired reanalysis and satellite data to supplement the lidar buoy observations during the California deployments. Point time series of observed and reanalysis data are provided, using inverse distance weighting to geolocate the reanalysis data to the buoy location. Gridded time series of reanalysis data are provided, covering the California coast and extending into the Pacific Ocean. Satellite data in the coastal California region are also provided. The reanalysis collection includes the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP). The satellite data collection is that of Ribal and Young (2019). All datasets are publicly available with the sources provided in the References section.

17 WIND ENERGY↗

Lithium Metal Battery Quality Control via Transformer–CNN Segmentation

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations.

Quenum, Jerome (ORCID:0000000271265853)↗