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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

HPC4Mobilty w/ UCB

The purpose of this project is to examine the energy impact of urban-scale traffic for the Los Angeles Basin by developing and implementing a scalable traffic assignment model. An energy optimization function will be posed and when integrated into the optimization code for travel assignment it can be mathematically proven to converge. The energy optimization function can then be compared to the typical travel time optimization that is traditionally used in traffic assignment models. The analysis will begin with static traffic assignment models with the routing for all origin and destinations computed in parallel on high performance computing facilities. Convergence of the numerical methods rely on the solution of convex programs (or extensions of these). This step will mostly consist of demonstrating the ability to parallelize the Frank Wolfe algorithm on various platforms. This work will contribute to LBNL’s efforts to develop new processes, analytical tools, program designs, and business models to advance the state of the art in next-generation sustainable transportation solutions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Network Polymer Properties Engineered Through Polymer Backbone Dispersity and Structure

Abstract Dispersity ( Ð or M w /M n ) is an important parameter in material design and as such can significantly impact the properties of polymers. Here, polymer networks with independent control over the molecular weight and dispersity of the linear chains that form the material are developed. Using a RAFT polymerization approach, a library of polymers with dispersity ranging from 1.2—1.9 for backbone chain‐length (DP) 100, and 1.4—3.1 for backbone chain‐length 200 were developed and transformed to networks through post‐polymerization crosslinking to form disulfide linkers. The tensile, swelling, and adhesive properties were explored, finding that both at DP 100 and DP 200 the swelling ratio, tensile strength, and extensibility were superior at intermediate dispersity (1.3—1.5 for DP 100 and 1.6—2.1 for DP 200) compared to materials with either substantially higher or lower dispersity. Furthermore, adhesive properties for materials with chains of intermediate dispersity at DP 200 revealed enhanced performance compared to the very low or high dispersity chains.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the surface passivating principle of functional thiol towards efficient and stable perovskite nanocrystal solar cells

Inorganic halide perovskite nanocrystals (PNCs) have demonstrated promising potential for solar cell applications. However, the lability of photoactive CsPbI3 phase under ambient conditions, coupled with considerable amounts of surface defects induced during solidification process, have impeded achieving high performances and longevities of the PNC-based solar cells. Post-treatment of the PNCs with organic ligands has been proposed as an efficient strategy for surface passivation, which, however, still relies on the binding actions of typical functional groups towards surface defects (especially, carboxylates onto iodine vacancies). Herein, we uncover that thiolate, a deprotonated form of thiol, renders distinctive binding feasibility towards iodine vacancies at the CsPbI3 PNC surface, compared with those of typical functional groups. By treating the PNC solid with deprotonated cysteine as a ligand, the surface defects are comprehensively passivated. The solar cells with the modified PNC films demonstrate an excellent PCE of 15.5 % and improved device longevity (77 % of initial PCE over 2 months) under ambient conditions. Our work not only elucidates the chemical principles of thiol on the binding with PNC surface, but also corroborates the power of thiolate as a promising strategy to develop high performances and improved longevity of solar cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Site-Selective Real-Time Observation of Bimolecular Electron Transfer in a Photocatalytic System Using L-Edge X-Ray Absorption Spectroscopy

Here, time-resolved X-ray absorption spectroscopy has been utilized to monitor the bimolecular electron transfer in a photocatalytic water splitting system. This has been possible by uniting the local probe and element specific character of X-ray transitions with insights from high-level ab initio calculations. The specific target has been a heteroleptic [Ir III (ppy) 2 (bpy)] + photosensitizer, in combination with triethylamine as a sacrificial reductant and Fe 3 (CO) 12 as a water reduction catalyst. The relevant molecular transitions have been characterized via high-resolution Ir L-edge X-ray absorption spectroscopy on the picosecond time scale and restricted active space self-consistent field calculations. The presented methods and results will enhance our understanding of functionally relevant bimolecular electron transfer reactions and thus will pave the road to rational optimization of photocatalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2023

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High-Spin and Reactive Fe 13 Cluster with Exposed Metal Sites

We report atomically defined large metal clusters have applications in new reaction development and preparation of materials with tailored properties. Expanding the synthetic toolbox for reactive high nuclearity metal complexes, we report a new class of Fe clusters, Tp* 4 W 4 Fe 13 S 12 , displaying a Fe 13 core with M-M bonds that has precedent only in main group and late metal chemistry. M 13 clusters with closed shell electron configurations can show significant stability and have been classified as superatoms. In contrast, Tp* 4 W 4 Fe 13 S 12 displays a large spin ground state of S=13. This compound performs small molecule activations involving the transfer of up to 12 electrons resulting in significant cluster rearrangements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Influence of the molecular weight and size distribution of PSS on mixed ionic-electronic transport in PEDOT:PSS

The commercially available polyelectrolyte complex poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is ubiquitous in organic and hybrid electronics. As such, it has often been used as a benchmark material for fundamental studies and the development of new electronic devices. Yet, most studies on PEDOT:PSS have focused on its electronic conductivity in dry environments, with less consideration given to its ion transport, coupled ionic-electronic transport, and charge storage properties in aqueous environments. These properties are essential for applications in bioelectronics (sensors, actuators), charge storage devices, and electrochromic displays. Importantly, past studies on mixed ionic-electronic transport in PEDOT:PSS neglected to consider how the molecular structure of PSS affects mixed ionic-electronic transport. Herein, we therefore investigated the effect of the molecular weight and size distribution of PSS on the electronic properties and morphology of PEDOT:PSS both in dry and aqueous environments, and overall performance in organic electrochemical transistors (OECTs). Using reversible addition–fragmentation chain transfer (RAFT) polymerization with two different chain transfer agents, six PSS samples with monomodal, narrow (D = 1.1) and broad (D = 1.7) size distributions and varying molecular weights were synthesized and used as matrices for PEDOT. We found that using higher molecular weight of PSS (Mn = 145 kg mol –1 ) and broad dispersity led to OECTs with the highest transconductance (up to 16 mS) and [μC*] values (~140 F cm –1 V –1 s –1 ) in PEDOT:PSS, despite having a lower volumetric capacitance (C* = 35 ± 4 F cm –3 ). The differences were best explained by studying the microstructure of the films by atomic force microscopy (AFM). We found that heterogeneities in the PEDOT:PSS films (interconnected and large PEDOT- and PSS-rich domains) obtained from high molecular weight and high dispersity PSS led to higher charge mobility (μOECT ~4 cm 2 V –1 s –1 ) and hence transconductance. Furthermore, these studies highlight the importance of considering molecular weight and size distribution in organic mixed ionic-electronic conductor, and could pave the way to designing high performance organic electronics for biological interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Controlled exposure of CuO thin films through corrosion-protecting, ALD-deposited TiO 2 overlayers

Ultra-thin film coatings are used to protect semiconductor photoelectrodes from the harsh chemical environments common to photoelectrochemical energy conversion. These layers add contact transfer resistance to the interface that can result in a reduction of photoelectrochemical energy conversion efficiency of the photoelectrode. Here, we describe the concept of a partial protection layer, which allows for direct chemical access to a small fraction of the semiconductor underlayer for further functionalization by an electrocatalyst. The rest of the interface remains protected by a stable, inert protection layer. CuO is used as a model system for this scheme. Atomic layer deposition (ALD)-prepared TiO 2 layers on CuO thin films prepared from electrodeposited Cu 2 O allow for the control of interfacial morphology to intentionally expose the CuO underlayer. The ALD-TiO 2 overlayer shrinks during crystallization, while Cu 2 O in the underlayer expands during oxidation. As a result, the TiO 2 protection layer cracks to expose the oxidized underlying CuO layer, which can be controlled by preceding thermal oxidation. Furthermore, this work demonstrates a potentially promising strategy for the parallel optimization of photoelectrochemical interfaces for chemical stability and high performance.

14 SOLAR ENERGY↗
DOE OSTI2026

Advanced Energy Storage for Extreme Conditions (AESTEC) (Abbreviated Final Report)

This project developed a new capability for designing and fabricating energy storage devices that function in extreme conditions detrimental to conventional devices (e.g. temperature, mechanical stress, etc.). This new toolset tackled critical phenomenological challenges which led to materials and designs that enable high performance energy storage in conditions relevant to national security missions and the broader energy storage community.

25 ENERGY STORAGE↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Importance of suberin biopolymer in plant function, contributions to soil organic carbon and in the production of bio-derived energy and materials

Abstract Suberin is a hydrophobic biopolymer of significance in the production of biomass-derived materials and in biogeochemical cycling in terrestrial ecosystems. Here, we describe suberin structure and biosynthesis, and its importance in biological (i.e., plant bark and roots), ecological (soil organic carbon) and economic (biomass conversion to bioproducts) contexts. Furthermore, we highlight the genomics and analytical approaches currently available and explore opportunities for future technologies to study suberin in quantitative and/or high-throughput platforms in bioenergy crops. A greater understanding of suberin structure and production in lignocellulosic biomass can be leveraged to improve representation in life cycle analysis and techno-economic analysis models and enable performance improvements in plant biosystems as well as informed crop system management to achieve economic and environmental co-benefits.

09 BIOMASS FUELS↗
DOE OSTI2023

LDRD23-0184: Resilience and Hazard Risk Assessment to Prioritize Security Operations for Decisions and Impacts (RHAPSODI)

Recent examples provide a significant concern for the resilience of the U.S. electric grid and represent a need for enhanced decision-making to address an increasingly wide range of complex system interactions and potential consequences. In response, this LDRD project produced a proof-of-concept evaluation called the Resilience and Hazard Assessment to Prioritize Security Operations for Decisions and Impacts (RHAPSODI) methodology as an agile and flexible analytic framework capable of addressing multiple, diverse threats to desired electric grid performance. After empirically grounding needs for the future of U.S. electric grid resilience, this project employed the systems-theoretic process analysis (STPA) to develop a systems engineering risk model. The results of a completed feasibility study of a notional high voltage transmission system demonstrate an improved ability to incorporate both spatial (e.g., geographically distributed) and temporal (e.g., dynamic and time-dependent) elements of security risk to the gird. The success of this LDRD project provides the foundation for further evolution of the systems engineering risk model for the grid; derivation of quantitative approaches to evaluate risk and resilience performance; facilitation of agile experimenting and grid sensitivity to a range of vulnerabilities; and development of tools to assist decision-makers in enhancing U.S. electrical grid resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Tailoring Solvation Solvent in Localized High-Concentration Electrolytes for Lithium||Sulfurized Polyacrylonitrile

Sulfurized polyacrylonitrile (SPAN) is a promising cathode material for lithium-sulfur (Li-S) batteries due to its significantly reduced polysulfide (PS) dissolution compared to the elemental S cathode. Although conventional carbonate-based electrolytes is stable with SPAN electrodes, it is less stable with Li metal anode (LMA). Recently, localized high-concentration electrolytes (LHCEs) have been developed to improve the stability of LMA. Here, we report a new strategy to further improve the performance of LI||SPAN batteries by replacing the conventional solvating solvent 1,2-dimethoxyethane (DME) in the LHCE with a new solvating solvent, 1,2-diethoxyethane (DEE), the new LHCEs exhibits less reactivity against Li 2 S 2 , alleviates PS dissolution, forms a better cathode-electrolyte interphase layer on the SPAN, and enhances structure reversibility even at elevated temperature (ET, 45°C). With the same salt and diluent as in other LHCEs, the LHCE with DEE leads to better performance in Li||SPAN batteries (with 82.9% capacity retention after 300 cycles at ET), preservation of SPAN cathode structure, and suppression of the volume change of LMA. The similar strategy on tailoring the solvating solvents in LHCEs can also be used in other rechargeable batteries to improve their performances.

1,2-diethoxyethane↗