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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Enhanced hindrance from phenyl outer side chains on nonfullerene acceptor enables unprecedented simultaneous enhancement in organic solar cell performances with 16.7% efficiency

Inner side-chain engineering on Y6 has been a proven successful in improving short-circuit current density (JSC) through fine-tuning aggregated structures of acceptors. However, it fails in tuning the lowest unoccupied molecular orbital level (LUMO) and open-circuit voltage (V OC ). In this paper, we turn to focus on engineering the outer side chains on the flanking thienothiophene units with 4-hexylphenyl (PhC6) and 6-phenylhexyl (C6Ph) chains. Use of PhC6 enhances the steric effect between the attached phenyl and the ending group, which in combination with the additional conjugation effect provided by the linking phenyl leads to upshifted energy levels and increased V OC as a result. Again, substitution with the bulkier PhC6 unprecedentedly improves film-morphology with reduced paracrystalline disorder and long period and increased root-mean-square composition variations as well, leading to increased electron and hole mobilities and suppressed monomolecular recombination with J SC and fill-factor (FF) simultaneously enhanced. The PM6:BTP-PhC6-based devices yield a higher efficiency value of 16.7% than the PM6:BTP-C6Ph-based one (15.5%). Furthermore, this study shows a conceptual advance in materials design towards reducing the conflict between V OC and J SC in binary blended organic solar cells, which can be achieved by introducing bulkier chains to twist the backbone and simultaneously enhance the packing order.

14 SOLAR ENERGY↗
DOE OSTI2022

Tuning Organic Electrochemical Transistor Threshold Voltage using Chemically Doped Polymer Gates

Organic electrochemical transistors (OECTs) have shown promise as transducers and amplifiers of minute electronic potentials due to their large transconductances. Tuning the OECT threshold voltage is important to achieve low-powered devices with amplification properties within the desired operational voltage range. However, traditional design approaches have struggled to decouple channel and materials properties from threshold voltage, thereby compromising on several other OECT performance metrics, such as electrochemical stability, transconductance, and dynamic range. In this work, simple solution-processing methods are utilized to chemically dope polymer gate electrodes, thereby controlling their work function, which in turn tunes the operation voltage range of the OECTs without perturbing their channel properties. Chemical doping of initially air-sensitive polymer electrodes further improves their electrochemical stability in ambient conditions. Thus, OECTs that are simultaneously low-powered and electrochemically resistant to oxidative side reactions under ambient conditions are demonstrated. Furthermore, this approach shows that threshold voltage, which is once interwoven with other OECT properties, can in fact be an independent design parameter, expanding the design space of OECTs.

chemical doping↗
DOE OSTI2018

Extending the Service Life of High–Ni Layered Oxides by Tuning the Electrode–Electrolyte Interphase

As a high-energy-density cathode for Li-ion batteries, high-Ni layered oxides, especially with ultrahigh Ni-content, suffer from short lifespans, due in part to their unstable electrode–electrolyte interphase (EEI). Herein, the cycle life of LiNi 0.94 Co 0.06 O 2 is greatly extended by manipulating the EEI with a lithium bis(oxalate) (LiBOB) additive even when operated at a moderately high voltage (4.4 V vs Li/Li + ). Impressively, the capacity retention can be increased from 61 to 80% after 500 cycles in a full cell paired with a graphite anode. Additionally, the presence of LiBOB enables a robust boron- and oxygen-enriched EEI that effectively inhibits continual electrolyte decomposition and offers a stable cathode surface. Moreover, the layered architecture of the cathode–electrolyte interphase (CEI) and the anode–electrolyte interphase (AEI) at the nanometer scale is revealed by time-of-flight secondary ion mass spectrometry. It is demonstrated that the cathode surface chemistry can significantly influence the AEI both chemically and physically, and AEI is modified from a thick “three-layer” to a thin “two-layer” architecture by tuning the cathode surface chemistry with LiBOB. Finally, this work presents a correlation between the EEI characteristics and battery performance and highlights the significance of manipulating surface chemistry in developing stable high-energy-density Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Li 21 Ge 8 P 3 S 34 : New Lithium Superionic Conductor with Unprecedented Structural Type

Abstract Lithium superionic conductors are pivotal for enabling all‐solid‐state batteries, which aim to replace liquid electrolytes and enhance safety. Herein, we report the discovery of an unprecedented lithium superionic conductor, Li 21 Ge 8 P 3 S 34 , featuring a novel structural type and a new composition in the Li–Ge–P–S system. This material exhibits high lithium ionic conductivity of approximately 1.0 mS cm −1 at 303 K with a low activation energy of 0.20(1) eV. It's unique crystal structure was elucidated using three‐dimensional electron diffraction (3D ED) and further refined through combined powder X‐ray and neutron diffraction analyses. The structure consists of alternating two‐dimensional slabs: one of corner‐sharing GeS 4 tetrahedra and the other of isolated PS 4 tetrahedra, enabling efficient lithium‐ion transport through a tetrahedrally interconnected network of 1D, 2D, and 3D diffusion pathways. This distinctive structural motif provides a novel design strategy for next‐generation solid electrolytes, broadening the structural landscape of lithium superionic conductors. With further advancements in compositional tuning and interfacial engineering, Li 21 Ge 8 P 3 S 34 could contribute to the development of high‐performance all‐solid‐state batteries.

Chemistry↗
DOE OSTI2022

Study of very forward energy and its correlation with particle production at midrapidity in pp and p-Pb collisions at the LHC

The energy deposited at very forward rapidities (very forward energy) is a powerful tool for characterising proton fragmentation in pp and p-Pb collisions. The correlation of very forward energy with particle production at midrapidity provides direct insights into the initial stages and the subsequent evolution of the collision. Furthermore, the correlation with the production of particles with large transverse momenta at midrapidity provides information complementary to the measurements of the underlying event, which are usually interpreted in the framework of models implementing centrality-dependent multiple parton interactions. Results about very forward energy, measured by the ALICE zero degree calorimeters (ZDCs), and its dependence on the activity measured at midrapidity in pp collisions at $\sqrt{s}$ = 13 TeV and in p-Pb collisions at $\sqrt{s{NN}}$ = 8.16 TeV are discussed. The measurements performed in pp collisions are compared with the expectations of three hadronic interaction event generators: PYTHIA 6 (Perugia 2011 tune), PYTHIA 8 (Monash tune), and EPOS LHC. These results provide new constraints on the validity of models in describing the beam remnants at very forward rapidities, where perturbative QCD cannot be used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accelerated alloy discovery using synthetic data generation and data mining

The search for new alloys with improved properties is never ending with infinite combinations and amounts of alloying elements in the alloy. Advancements in machine learning have made navigating this enormous search space feasible. However, training the machine learning models and tuning their hyper-parameters to make accurate predictions can be time-consuming and often require high-performance computing resources. Furthermore, the quality of the predictions depend on the availability of sufficient training data. Here, we present a generic approach to accelerate alloy discovery by coupling high throughput CALPHAD calculations, synthetic data generation, and data mining. Finally, as a demonstration of the approach, we design super bainitic steels that form bainite at 200 C in lower transformation times.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Atomic to Nanoscale Origin of Vinylene Carbonate Enhanced Cycling Stability of Lithium Metal Anode Revealed by Cryo-Transmission Electron Microscopy

Batteries using lithium (Li) metal as anode are considered promising energy storage systems because of their high specific energy densities. The crucial bottlenecks for Li-metal anode are Li dendrites growth and side reactions with electrolyte inducing low Coulombic efficiency (CE) and short cycle life. Vinylene carbonate (VC), as an effective electrolyte additive in Li-ion batteries, has been noticed to significantly enhance the CE, while the origin of such an additive remains unclear. Here we use cryo-TEM imaging combing with EDS elemental and EELS electronic structure analyses to reveal the role of VC additive. We discovered that the electrochemically deposited Li metal (EDLi) in VC-containing electrolyte is slightly oxidized with the SEI being a nanoscale-mosaic like structure comprised of organic species, Li 2 O and Li 2 CO 3 ; while the EDLi formed in VC-free electrolyte is featured by a combination of fully oxidized Li with Li 2 O SEI layer and pure Li metal with multilayer nano-structured SEI. These results highlight the possible tuning of crucial structural and chemical features of EDLi and SEI through additives and consequently direct correlation with electrochemical performance, providing valuable guidelines to rational selection, design and synthesis of additives for new battery chemistries.

solid electrolyte interphase (SEI)↗
DOE OSTI2025

Revealing the Crystalline Architecture of Semicrystalline Ion Exchange Membranes for the Design of Conductive and Durable Alkaline Anion Exchange Membranes

Alkaline anion exchange membrane (AAEM) fuel cells offer a cost-effective alternative to proton exchange membrane (PEM) fuel cells by eliminating the need for expensive precious metal catalysts. In both PEMs and AAEMs, semicrystalline polymers are a common choice, as the crystalline domains can act as mechanical reinforcements that limit swelling and promote mechanical durability in the material. However, spatially resolved characterization of crystalline organization in ion exchange membranes beyond ensemble-averaged X-ray scattering is underrepresented, likely in part due to ionization damage limitations in soft materials. Here, in this study, we resolve the nanometer-size crystallites in semicrystalline ion exchange membranes by applying cryogenic four-dimensional scanning transmission electron microscopy (cryo-4D-STEM) along with data-processing algorithms designed to optimize signals at a low dose to minimize radiation damage. We investigate the effects of synthesis components, including molecular weight and thermal treatment, on a model system of AAEMs in comparison to Nafion, the most commonly used and commercially successful PEM today. We find that excess water uptake in polymer membranes, a property directly associated with weak mechanical durability and with possible negative impacts on ion conductivity, can be reduced by over 30% by varying the polymer's crystalline morphology through changes in synthesis parameters such as molecular weight and thermal history. Our results indicate that this improvement is correlated with smaller crystalline domains with a more homogeneous distribution. More broadly, these results demonstrate how the crystalline architecture of polymer membranes can be tuned through their chemistry and thermal treatment in order to improve their conductivity and durability for commercial fuel cell performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Synthetic Routes for a 2D Semiconductive Copper Hexahydroxybenzene Metal–Organic Framework

Conductive metal-organic frameworks (c-MOFs) have demonstrated outstanding performance in energy storage and electrocatalysis. Varying the bridging metal species and the coordinating atom are versatile approaches to tune their intrinsic electronic properties in c-MOFs. Herein we report the first synthesis of the oxygen analog of M 3 (C 6 X 6 ) 2 (X = NH, S) family using Cu(II) and hexahydroxybenzene (HHB), namely Cu-HHB [Cu 3 (C 6 O 6 ) 2 ], through a kinetically controlled approach with a competing coordination reagent. We also successfully demonstrate an economical synthetic approach using tetrahydroxyquinone as the starting material. Cu-HHB was found to have a partially eclipsed packing between adjacent 2D layers and a bandgap of approximately 1 eV. The addition of Cu-HHB to the family of synthetically realized M 3 (C 6 X 6 ) 2 c-MOFs will allow greater understanding of the influence of the organic linkers and metals, and further broadens the range of applications for these materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Maven: a multimodal foundation model for supernova science

Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5 M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 observed supernovae from the Zwicky transient facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin observatory, Maven will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.

Zhang, Gemma (ORCID:0000000280198082)↗
DOE OSTI2022

Toward Robust Autotuning of Noisy Quantum dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a “gatekeeper” system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9)% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Kondo interaction of quantum spin Hall edge channels with charge puddles

Under time-reversal symmetry, quantum spin Hall edge channels are protected against elastic backscattering. However, even for samples which exhibit conductance quantization due to the quantum spin Hall effect, reproducible fluctuations shape the quantization plateau when the chemical potential is tuned through the bulk gap. Here, we examine those fluctuations in micron-sized HgTe quantum well devices. By performing temperature- and gate-dependent measurements, we conclude that “charge puddles” in the narrow-gap material have a Kondo-type interaction with the edge channels resulting in the observed conductance fluctuations. Our results provide insight into the underlying mechanisms of scattering in quantum spin Hall edge channels.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.

Ziabari, Amir↗
DOE OSTI2019

Intelligent Reservoir Generation for Liquid State Machines using Evolutionary Optimization

Neuromorphic Computing is a burgeoning field of research. Many groups are exploring hardware architectures and theoretical ideas about spiking recurrent neural networks. The overarching goal is to exploit the low power promise of these neuromorphic systems. However, it is difficult to train spiking recurrent neural networks (SRNNs) to perform tasks and make efficient use of neuromorphic hardware. Reservoir Computing is an attractive methodology because it requires no tuning of weights for the reservoir itself. Yet, to find optimal reservoirs, manual tuning of hyperparameters such as hidden neurons, synaptic density, and natural structure is still required. Because of this, researchers often have to generate and evaluate many networks, which can result in non-trivial amounts of computation. This paper employs the reservoir computing technique (specifically liquid state machines) and genetic algorithms in order to develop useful networks that can be deployed on neuromorphic hardware. We build on past work in reservoir computing and genetic algorithms to demonstrate the power of combining these two techniques and the advantage it can provide over manually tuning reservoirs for use on classification tasks. We discuss the complexities of determining whether or not to use the genetic algorithms approach for liquid state machine generation.

Reynolds, John↗
DOE OSTI2024

Simulation of Helium Flow Visualization Apparatus for Studies of Blanket Cooling in Fusion Reactors

Flow visualization is essential to understanding helium cooling performance. This article investigates helium flow visualization in an apparatus to support the design of the blanket first wall for a fusion reactor. Helium’s safety advantages make it an attractive coolant, but effective cooling and flow visualization remain challenging. Sophisticated simulations are conducted in different test sections to address three key areas in the design of a test apparatus and cooling channel enhancements: the influence of viewing glass windows on flow patterns, the impact of increased heating, and the effectiveness of cooling structures like baffles. Viewing glass windows introduce flow recirculation region bifurcation and flow asymmetry, affecting flow patterns and necessitating careful analysis in future experiments. Increased heating results in helium flow detachment from the heated surface, leading to hot spots. Cooling structures, particularly baffles, prove effective in maintaining consistent attachment to the heated surface, improving the heat transfer performance. Further, this study also examines a tunable parameter in the turbulence model, highlighting the importance of accurate model tuning for future fusion reactor cooling designs. With an imminent helium flow visualization facility, these simulations will be used to optimize cooling structures on the heated wall to improve flow attachment and heat transfer efficiency. This work serves as a first investigation of the helium flow visualization apparatus for blanket cooling enhancement in fusion reactor design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗