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At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Highly efficient non-fullerene organic solar cells enabled by a delayed processing method using a non-halogenated solvent

The use of non-halogenated solvents is one of the important requirements for large-scale industry production of polymer solar cells (PSCs). However, the current high-performance PSCs are mostly obtained using halogenated solvents to ensure good solubility and morphology. In this work, we report high-performance PSCs processed using a halogen-free solvent (toluene), reaching an efficiency of 17.33%, which, to the best of our knowledge, is the highest value reported for PSCs processed from non-halogenated solvents. Importantly, we show a novel aggregation or “aging” process of the non-fullerene acceptor in the toluene solvent, which can be used to fine-tune the crystal size of the non-fullerene acceptor. As a result, we achieve an optimized nano-fibril like morphology of PM6:BTP-BO-4Cl by delaying the processing time. Furthermore ,this is conducive to charge dissociation and transport, and thus greatly improved device performance. This method is expected to be investigated further and applied to other PSC systems toward precise morphology optimization and performance improvements in the future.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Taming the Pseudoelastic Response of Nitinol Using Ion Implantation

Implantation of Ni 50.5 Ti 49.5 wire with 30 MeV Ni 6+ ions at doses (< 0.1 DPA) typically smaller than employed in the literature is shown to systematically alter the pseudoelastic response, with extrema in Berkovich nanoindentation load (+50%), hysteresis (–60%), and recoverable displacement (–19%) occurring at ~ 3.6 μm below the implantation surface. These extraordinary values are attributed to ~ 10 to 20 nm amorphous clusters that constrain the stress-induced B2-B19' phase transformation. This is substantiated by phase field simulations of crystalline-amorphous composites and molecular dynamics simulations of crystalline-vacancy cluster composites showing the spatial refinement of martensite caused by nm-scale defects. Finally, the results suggest that ion implantation may potentially expand the processing and performance space for NiTi, by creating amorphous defects at smaller length scales than dislocation substructures produced by conventional deformation processing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Mechanical and thermal behavior of additively manufactured Invar 36 using a laser hot wire hybrid DED process

Invar 36 alloy is a material of high interest in the composite tooling sector due to its low coefficient of thermal expansion. Current production of Invar 36 tooling using traditional manufacturing such as casting and forging is associated with long lead times due to a multitude of factors such as labor and component shortages, high material costs, foreign competition, and supply chain issues. An attractive alternate process is the use of an integrated 5-axis CNC hybrid Laser Hot Wire Deposition System (LHWD) for manufacturing invar molds. Here, the hybrid process provides a combination of the additive and subtractive technologies resulting in a synergistic platform for producing and repairing structures and molds. The main novelty and goal of this work is to study the properties of Invar deposited by a LHWD and to provide guidelines for the manufacture of parts using this process. In this study, the thermal expansion behavior of the manufactured specimens has been analyzed and related to its printing parameters and direction. Multiple specimens were extracted for mechanical, dilatometry and metallographic testing. A thermal IR recording of the printing process was also carried out to observe the thermal history of the produced parts to establish thermal influence on performance-property-processing relationship. The results of these tests show the advantage of LHWD technology for the manufacture of Invar alloy parts, as it presents similar thermal expansion behavior as those commercially available with minimal presence of precipitates and no macrostructural failures such as pores, cracks and lacks of fusion.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Summary of SRNL Support to the DOE-ORP Enhanced Waste Glass Program for FY23

In fiscal year 2023 (FY23) Savannah River National Laboratory (SRNL) continued tasked work for the Office of River Protection (ORP) to expand glass compositional regions accessible for low-activity waste (LAW) and high-activity waste (HLW) vitrification processing. Experimental work continued in four primary technical areas focused on processing and performance of glasses relevant to the Hanford missions. The data and results from this work will be used to expand and validate the glass models being developed at Pacific Northwest National Laboratory (PNNL) for waste processing and acceptance. This report summarizes the activities and deliverables associated with work performed in FY23.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Summary of SRNL Support Activities to the DOE-ORP Enhanced Waste Glass Program for Fiscal Year 2024

In fiscal year 2024 (FY24) Savannah River National Laboratory (SRNL) continued tasked work for the Office of River Protection (ORP) to expand glass compositional regions accessible for low-activity waste (LAW) and high-activity waste (HLW) vitrification processing. Experimental work continued in four primary technical areas focused on processing and performance of glasses relevant to the Hanford missions. The data and results from this work will be used to expand and validate the glass models being developed at Pacific Northwest National Laboratory (PNNL) for waste processing and acceptance. This report summarizes the activities and deliverables associated with work performed in FY24.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Adaptive time scaling for accelerating explicit finite element analysis

A method for accelerating an explicit finite element analysis (FEA) simulation of a modeled system or process includes performing an initial iteration of the FEA simulation according to a baseline time interval via an FEA computing network, and calculating a criteria ratio of a predetermined set of scaling criteria for the modeled system or process. The method includes determining a time-scaling factor using the criteria ratio via the FEA computing network as a function of the criteria ratio, and then applying the time-scaling factor to the baseline time interval to generate a scaled time interval. The scaled time interval accelerates simulation time of the FEA simulation. The method includes performing a subsequent iteration of the explicit FEA simulation at the scaled time interval using the FEA computing network. The process continues for subsequent iterations, with the time-scaling factor adapting with each iteration.

Chen, Jian↗

DOE OSTI2020

Chapter 14: Sustainable Biomass Conversion Process Assessment

Process intensification and integration for sustainable design play an essential role in addressing and alleviating severe sustainable development challenges. It is prudent to evaluate a process design in its early stage development to ensure the process can achieve sustainability goals. The primary goal of a sustainability evaluation is to assess the impact of systems on areas sought to be protected and maintained over time, such as human well-being and ecosystems (Schaubroeck, T. and Rugani, B. (2017). A revision of what life cycle sustainability assessment should entail: towards modeling the net impact on human well-being. Journal of Industrial Ecology 21 (6): 1464-1477). Parenthetically, sustainable process design to convert biomass to fuels and chemicals plays an integral part in the bioeconomy sustainability. It is recognized that the integration of sustainability in process design is core to the mission in developing renewable fuels and should be considered a necessary practice in biorefinery design. This study demonstrates the use of Gauging Reaction Effectiveness for ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) as a tool to help track progress on process sustainability performance attributed to conversion improvement. With the incorporation of a wide range of indicators, GREENSCOPE captures the multi-dimensional aspect of the process design and operation. As opposed to conventional techno-economic analysis and life cycle assessment (in which the focuses are limited to economic and environmental aspects), GREENSCOPE enables the consideration of process intensification and integration for sustainable design. A high-octane gasoline production from woody biomass feedstock is used as a case study to demonstrate the approach.

biomass↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/biosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts.

Cortes-Peña, Yoel↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/thermosteam

Cortes-Peña, Yoel↗

DOE OSTI2024

Demystifying In Situ Pyrolysis Chemistry for High-Performance Polyanionic Cathodes in Sodium-Ion Batteries

In this article, the carbon coating strategy has emerged as an indispensable approach to improve the conductivity of polyanionic cathodes. However, owing to the complex reaction process between precursors of carbon and cathode, establishing a unified screening principle for carbonaceous precursors remains a technical challenge. Herein, we reveal that carbonaceous precursor pyrolysis chemistry undeniably influences the formation process and performance of Na 3 V 2 (PO 4 ) 3 (NVP) cathodes from in situ insights. By investigating three types of carbonaceous precursors, it is found that O/H-containing functional groups can provide more bonding sites for cathode precursors and generate a reducing atmosphere by pyrolysis, which is beneficial to the formation of polyanionic materials and a uniform carbon coating layer. Conversely, excessive pyrolysis of functional groups leads to a significant amount of gas, which is detrimental to the compactness of the carbon layer. Furthermore, the substantial presence of residual heteroatoms diminishes graphitization. In this case, it is demonstrated that carbon dots (CDs) precursors with suitable functional groups can comprehensively enhance the Na+ migration rate, reversibility, and interface stability of the cathode material. As a result, the NVP/CDs cathode displays outstanding capacity retention, maintaining 92% after 10,000 cycles at a high rate of 50 C. Altogether, these findings provide a valuable benchmark for carbon source selection for polyanionic cathodes.

25 ENERGY STORAGE↗

DOE OSTI2025

Baseline Hypothetical Facility for the Production of 131 I and 99 Mo using Activation Targets

This report describes a hypothetical facility for production of medical radioisotopes via activation under the Proliferation Resistance and Optimization (PRO-X) program. The facility uses neutron activation of non-special nuclear material (SNM) to produce the medical isotopes 131 I and 99 Mo at a throughput of 60 Ci/week of 131 I and 5 Ci/week of 99 Mo. The hypothetical design was carried out using a 10 MWt research reactor. The precursors used for the activation process were TeO2 for 131 I and MoO 3 for 99 Mo. The processes are performed in 3 hot cells used for target receipt, extraction, purification low specific activity (LSA) generator introduction, and packaging. A fourth hotcell is used for waste processing. The hot cell processing area takes up a footprint of 15.4 m 2 with the total footprint of the facility, including space for administrative offices, non-rad labs, quality assurance, and radiation buffer areas set at 763 m 2 . Waste is produced at a weekly rate of 257.8 g low activity solid waste and 8032.7 mL of low activity liquid waste, 8032 mL of which is water. This baseline hypothetical facility for production of medical isotopes via activation was then compared and contrasted to the hypothetical facility for production of medical isotopes via fission products to show the differences in approach for the two production modes. The two production modes had several highlighted differences including the overall facility and hot cell layout, the type and amount of waste produced by the respective facilities, and economic factors impacting production mode. Finally, a decision tree for which production mode might be more beneficial for an entrant into medical isotope production was developed based on the differences examined and the desired output of medical isotopes desired by the entrant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Multimode Characterization Approach for Understanding Cell-Level PV Performance and Degradation

Cell-level degradation processes impact the economic viability and large-scale deployment prospects for both established and emerging photovoltaic (PV) technologies. This project addresses the need to develop experimental and device-modeling approaches for studying cell-level degradation processes in photovoltaic (PV) devices using a variety of characterization techniques that provide access to complementary material and device properties. Our results demonstrate that by coupling characterization results with device modeling it is possible to develop comprehensive understanding of processes leading to performance limitations and degradation. This project developed a suite of novel measurement techniques including pulsed-light-bias operando X-ray and photoelectron spectroscopy (popXPS), light-biased scanning microwave impedance microscopy (sMIM), and near-field transport imaging (TI). In addition, operando characterization methodologies and in situ stressing capabilities have been developed and applied for techniques including electron-beam-induced current (EBIC), cathodoluminescence (CL), and Kelvin probe force microscopy (KPFM). Device-physics models were developed and applied to simulate correlative, multi-mode measurements to extract material and device parameters that control performance degradation. These characterization and modeling techniques were applied in a multi-mode approach to probe cell-level degradation mechanisms in Cd(Se,Te) and hybrid perovskite PV devices. Together these efforts contribute to foundational PV degradation science by establishing a framework for understanding PV performance degradation at the cell level and benefit the U.S. PV industry by providing resources in the form of novel experimental capabilities, knowledge gained, and available expertise that can accelerate research and development of improved PV device materials and architectures. The project provided a comprehensive understanding of degradation in baseline Cd(Se,Te) solar cells provided by our collaborators at Colorado State University (CSU). EBIC and CL-based measurements and revealed unusual collection and recombination profiles in these devices, which underwent significant changes with during stressing. KPFM and operando XPS measurements showed that device stressing permanently alters energy-band alignments at the (Mg,Zn)O/Cd(Se,Te) interface, which in turn account for an observed loss in fill factor. Studies on hybrid perovskite devices were hampered to a significant extent by delays related to the pandemic. Nevertheless, a set of hybrid perovskite devices (supplied through an NREL-industry partnership) were stress tested and characterized with techniques including EBIC, sMIM, popXPS/popUPS and optically excited TI. Available results from these measurements informed the device modeling effort and suggest that defects and related band offsets at the C60/LiF/hybrid perovskite interface are the primary source of degradation in these devices.

14 SOLAR ENERGY↗

DOE OSTI2024

Classical shadows for quantum process tomography on near-term quantum computers

Quantum process tomography is a powerful tool for understanding quantum channels and characterizing the properties of quantum devices. Inspired by recent advances using classical shadows in quantum state tomography [H.-Y. Huang, R. Kueng, and J. Preskill, .], we have developed ShadowQPT, a classical shadow method for quantum process tomography. We introduce two related formulations with and without ancilla qubits. ShadowQPT stochastically reconstructs the Choi matrix of the device allowing for an classical evaluation of the device on arbitrary inputs with respect to arbitrary outputs. Using shadows, we then show how to compute overlaps, generate all k -weight reduced processes, and perform reconstruction via Hamiltonian learning. These latter two tasks are efficient for large systems as the number of quantum measurements needed scales only logarithmically with the number of qubits. A number of additional approximations and improvements are developed, including the use of a pair-factorized Clifford shadow and a series of postprocessing techniques that significantly enhance the accuracy for recovering the quantum channel. We have implemented ShadowQPT using both Pauli and Clifford measurements on the IonQ trapped ion quantum computer for quantum processes up to n = 4 qubits, and we achieved good performance. Published by the American Physical Society 2024

Levy, Ryan (ORCID:0000000349527156)↗

DOE OSTI2025

Mechanistic Insights Into Fatigue Life Enhancement of High‐Strength Steel via Ultrasonic Impact Treatment

In this work we explore the impact of Ultrasonic Impact Treatment (UIT) on the fatigue performance of high‐strength microalloyed steel commonly used in crankshaft applications. Building on prior observations of fatigue life improvement with UIT, this work focuses on unraveling the process–structure–performance relationships underpinning these enhancements. Microstructural analysis revealed significant grain refinement in the near‐surface layers, with deformation depth increasing at higher impact energies. This led to increased surface hardness and the development of deeper compressive residual stresses, particularly in samples treated with higher impact energy. Rotating bending fatigue testing showed a substantial improvement in fatigue life for UIT‐treated samples, with the endurance limit nearly doubling compared with untreated specimens. Fractographic analysis revealed a transition in crack initiation from surface defects in untreated samples to interior regions in UIT‐treated samples, characterized by the formation of noninclusion‐induced granular bright facets (GBFs). The observed fatigue enhancement is attributed to the synergistic effects of strain hardening and compressive residual stresses, which increase the surface crack threshold and promote interior crack initiation. This study provides new mechanistic insight into UIT‐induced fatigue resistance and interior failure behavior in high‐strength steels.

36 MATERIALS SCIENCE↗

DOE OSTI2024

An Intermediate Level Trigger Supernova Pointing Trigger for DUNE Using In-Storage AI

We propose a near-data approach to reduce the buffered supernova data on the DUNE DAQ front-end computers using ML techniques. Based on simulations, we are able to reduce the data by 5 orders of magnitude to 0(100)MB which can then be transferred quickly over ethernet to a single server that executes a full reconstruction and pointing analysis to determine the SN direction. The entire process of reducing the data and executing reconstruction and pointing analysis pipeline takes less time than that required to transfer all the data back to Fermilab before performing more processing to determine the direction of the supernova, which was the baseline plan for DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Tetrahydrofuran Processable Organic Solar Cells with 19.45% Efficiency Realized by Introducing High Molecular Dipole Unit Into the Terpolymer

Developing organic solar cells (OSCs) processable with halogen‐free, non‐aromatic solvents is crucial for practical applications, yet challenging due to the limited solubility of most photoactive materials. Here, this study introduces high‐performance terpolymers processable in tetrahydrofuran (THF) by incorporating dithienophthalimide (DPI) into the PM6 backbone. DPI extends the absorption band, lowers HOMO levels, and improves THF solubility and film crystallinity through its large dipole moment effect. Optimal PBD‐10:L8‐BO devices processed with THF achieved a competitive power conversion efficiency (PCE) of 18.79%, approaching chloroform‐processed devices (19.04%). By introducing PBTz‐F as a second donor, ternary OSCs reached an impressive 19.45% PCE when processed with THF. This improvement stems from enhanced photon generation, improved morphology, better charge transport, longer exciton lifetimes, efficient charge dissociation and collection, and suppressed recombination. These PCEs of 18.79% and 19.45% for binary and ternary blend OSCs, respectively, represent the highest reported efficiencies for OSCs processed with halogen‐free, non‐aromatic solvents. This work demonstrates significant progress in eco‐friendly OSC fabrication, paving the way for more sustainable and commercially viable organic photovoltaic technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Charge carrier extraction and recombination effects in GaInAs/GaAsP multi-quantum well solar cells

The carrier extraction and transport mechanisms as well as the relative contributions of radiative and non-radiative recombination processes are investigated in high-quality strain-balanced GaInAs/GaAsP multi-quantum well solar cells recently implemented in record efficiency multijunction solar cells. A comprehensive suite of complementary characterization techniques including temperature- and suns-dependent photoluminescence and photovoltaic measurements are employed to analyze thermal escape and tunneling rates, which demonstrate the need to move beyond simple drift-diffusion models of p–n junctions. This study examines the processes that best characterize the operation of these devices across varying temperatures using a simple two-diode model, incorporating multiple transport protocols, and provides insights into the performance-limiting processes and pathways for their optimization.

14 SOLAR ENERGY↗

DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗