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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Effects of Extractive Ammonia Pretreatment on the Ultrastructure and Glycan Composition of Corn Stover

Lignocellulosic biomass is highly recalcitrant and requires a pretreatment step to improve the enzyme accessibility and fermentable sugar yields during enzymatic hydrolysis. Our previous studies demonstrated the rearrangement of the hydrogen bond network within CIII, makes it “amorphous-like” and facilitates easier glucan chain extraction by enzyme. Also, these changes increase the number of solvent-exposed glucan chain hydrogen bonds with water ~50% lowering the surface-bound cellulase by 60–70%. Also, major chemical modifications to lignin occur via ammonolysis of ester-linked ferulate and coumarate linkage. These apparent ultrastructural changes help the enhancement of cellulase activity resulting in higher production of fermentable sugars during enzyme hydrolysis of EA pretreated corn stover relative to Ammonia Fiber Expansion (AFEX) pretreatment. To understand ultra-structural modifications that occur during EA pretreatment, Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) were used to examine untreated and EA-pretreated corn stover in an effort to visualize changes in the biomass resulting from the pretreatment. In addition, Immunofluorescence Microscopy was applied to both untreated and pretreated plant cell walls using glycan-directed monoclonal antibodies to reveal possible changes in the spatial distributions of wall glycan epitopes resulting from EA pretreatment. This evaluation was complemented with glycome profiling to determine the glycan epitope compositions of EA-pretreated cell walls relative to untreated and AFEX pretreated corn stover, where lignin and carbohydrates are not extracted. Distinct differences could be observed in the case of xyloglucan, unsubstituted and substituted pectin- and pectic-arabinogalactan-epitope levels in the plant cell wall after EA pretreatment compared with untreated and AFEX-pretreated walls. Liquid ammonia induced de-lignification of cell wall has helped to clearly identify the glucans that are intact after pretreatment. These studies support some of our hypothesis that liquid ammonia cleaves lignin–carbohydrate ester linkages, partially solubilizing lignin and its associated carbohydrates. Specifically, the imaging studies clearly show that some of the cell wall components are extracted as a separate liquid stream during the EA process, thereby creating porous, interconnected tunnel-like networks in the plant cell walls thereby providing better access of carbohydrate polymers to enzymes and thereby increasing the sugar yield from the EA-pretreated biomass.

09 BIOMASS FUELS↗
DOE OSTI2015

Doped carbon-sulfur species nanocomposite cathode for Li--S batteries

We report a heteroatom-doped carbon framework that acts both as conductive network and polysulfide immobilizer for lithium-sulfur cathodes. The doped carbon forms chemical bonding with elemental sulfur and/or sulfur compound. This can significantly inhibit the diffusion of lithium polysulfides in the electrolyte, leading to high capacity retention and high coulombic efficiency.

25 ENERGY STORAGE↗
DOE OSTI2011

Atomistic Simulations of Perfluoro Phosphonic and Phosphinic Acid Membranes and Comparisons to Nafion

We used classical molecular dynamics (MD) simulations to investigate the nanoscale morphology and proton transport properties of perfluoro phosphonic (FPA) and phosphinic acid (FPA-I) membranes as they are being considered for use in low temperature fuel cells. We systematically investigated these properties as a function of the hydration level. The changes in nanostructure, in transport dynamics of water and hydronium ions, and in water network percolation were extracted from MD simulations and compared with Nafion. Phosphonic and phosphinic acid moieties in FPA and FPA-I, have lower acidity than sulfonic acid in Nafion, yet the diffusion of water was observed to be faster in FPA and FPA-I than in Nafion, particularly at low hydration levels. However this did not give rise to notable differences in hydronium ion diffusion and water network percolation for these membranes over Nafion. Similar observations were also reported by our group recently in a study of perfluoro-sulfonyl imide membranes carrying stronger super-acids than sulfonic acid of Nafion. These findings together suggest no strong apparent correlation between the acidity strength of the functional acid groups and the dynamics of water and hydronium ions in hydrated polymer electrolyte membranes (PEMs) with similar fluorocarbon backbones and acidic group-carrying side chains. This work was supported by the US Department of Energy Basic Energy Sciences' Chemical Sciences, Geosciences & Biosciences Division. Pacific Northwest National Laboratory is operated by Battelle for the US Department of Energy.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Methodology to Implement a Microgrid in a University Campus

This paper presents the method we followed to design a microgrid at a university campus based on available resources. Due to introduction of renewable resources to produce energy, a methodology that allows design a microgrid in a university campus is very useful. Hence, we present a series of steps that must be carried out to estimate the resource to be used, the installation, area needed, and the capacity of the systems needed are also described. In addition, the models of the distributed resources that constitutes the microgrid are presented and explained. To validate the proposed methodology, simulations were performed using Opal-RT-LAb. As a test scenario we selected the Mayagüez campus of the University of Puerto Rico where we conducted analysis of the available resource and capacity of the systems is needed to satisfy demand of critical loads, considering a predetermined number of days of austerity. Our study results in determination of dimensions, cost and effectiveness of the microgrid. Simulations results also show that the proposed microgrid satisfy demand with the same reliability, or better, than the traditional electrical network. Additionally, the best options for this purpose are photovoltaic, batteries, and combined heat and power, if the technological advances and availability of resources to the date for Puerto Rico are considered.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Ultrasensitive photodetectors exploiting electrostatic trapping and percolation transport

The sensitivity of semiconductor photodetectors is limited by photocarrier recombination during the carrier transport process. We developed a new photoactive material that reduces recombination by physically separating hole and electron charge carriers. This material has a specific detectivity (the ability to detect small signals) of 5 × 10 17 Jones, the highest reported in visible and infrared detectors at room temperature, and 4-5 orders of magnitude higher than that of commercial single-crystal silicon detectors. The material was fabricated by sintering chloride-capped CdTe nanocrystals into polycrystalline films, where Cl selectively segregates into grain boundaries acting as n-type dopants. Photogenerated electrons concentrate in and percolate along the grain boundaries - a network of energy valleys, while holes are confined in the grain interiors. This electrostatic field-assisted carrier separation and percolation mechanism enables an unprecedented photoconductive gain of 10 10 e - per photon, and allows for effective control of the device response speed by active carrier quenching.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hierarchical Majoranas in a programmable nanowire network

We propose a hierarchical architecture for building “logical” Majorana zero modes using “physical” Majorana zero modes at the Y-junctions of a hexagonal network of semiconductor nanowires. Each Y-junction contains three “physical” Majoranas, which hybridize when placed in close proximity, yielding a single effective Majorana mode near zero energy. The hybridization of effective Majorana modes on neighboring Y-junctions is controlled by applied gate voltages on the links of the honeycomb network. This gives rise to a tunable tight-binding model of effective Majorana modes. We show that selecting the gate voltages that generate a Kekule vortex pattern in the set of hybridization amplitudes yields an emergent “logical” Majorana zero mode bound to the vortex core. The position of a logical Majorana can be tuned adiabatically, without moving any of the “physical” Majoranas or closing any energy gaps, by programming the values of the gate voltages to change as functions of time. Furthermore, a nanowire network supporting multiple such “logical” Majorana zero modes provides a physical platform for performing adiabatic non-Abelian braiding operations in a fully controllable manner.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Optimization of Canister Loading Patterns in Dual Purpose Canisters for Criticality Suppression

This report documents work performed in support of the US Department of Energy Office of Nuclear Energy (NE) Spent Fuel and Waste Disposition, Spent Fuel and Waste Science and Technology, under work breakdown structure element 1.08.01.03.05, “Direct Disposal of Dual Purpose Canisters.” In particular, this report fulfills milestone M3SF-22PN010305094, “Application of AI/ML techniques to optimize DPC loading,” within work package SF-22PN01030509, “Direct Disposal of Dual Purpose Canisters - PNNL.” This report continues the process of examining the potential for using loading optimization as a disposal criticality suppression technique. This latest update to the report added: 1. A validation of the artificial neural network (ANN) reactivity prediction tool against as-loaded dual-purpose canisters from the UNF-ST&DARDS database 2. Refinement of the algorithm to incorporate an additional DPC design to improve the predictions 3. Investigation of a storage and transportation focused loading optimization algorithm. A key result from this year’s work is that the ANN performance was significantly improved by including the additional model for the NUHOMS canisters and it is apparent that fuel type specific modeling considerations should be incorporated into the ANN in future work. The GRASP-enabled adaptive multi-objective memetic algorithm with partial clustering (GAMMA-PC) algorithm was evaluated as a tool for storage and transportation oriented optimization. The GAMMA-PC optimization routine considers decay heat at loading, the time between loading and when the canister is eligible for transportation and minimizes the number of casks loaded. Future work will develop a dose minimization optimization routine.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Self-decomposable Fibrous Bridging Additives for Temporary Cementitious Fracture Sealers in EGS Wells

This study evaluates compatibility of a self-degradable temporary fracture sealer with the drilling mud and plugging and self-degrading performance of different fibers to be used in combination with the sealer. The sodium silicate-activated slag/Class C fly ash (SSASC) cementitious sealer must plug fractures at 85oC to allow continuous well drilling and it must degrade and leave the fractures open for water at later times when exposed to temperatures above 200oC. The sealer showed good compatibility with the mud. Even the blend of 80/20 vol.% of sealer/mud reached a compressive strength of more than 2000 psi set as one of the material criteria, mostly due to the additional activation of the slag and Class C fly ash by the alkaline ingredient present in the drilling fluid. In contrast, the drilling fluid was detrimental to the compressive strength development in conventional Class G well cement, so that it failed to meet this criterion. Among several organic fibers tested both polyvinyl alcohol (PVA)-and nylon-based fibers showed adequate plugging of the sealer in slot nozzles of 1-in. wide x 6-in. long x 0.08 in. and 0.24 in. high under pressures up to 700 psi. PVA fibers displayed better compressive toughness and self-degrading properties than nylon. The compressive toughness of sealers made by adding 1.0 wt% 6 mm-length PVA and 0.5 wt% 19 mm-length PVA was 9.5-fold higher than that of a non-bridged sealer. One factor governing the development of such high toughness was an excellent adherence of PVA to the SSASC cement. The alkali-catalyzed self-decomposition of PVA at 200°C led to the morphological transformation of the material from a fibrous structure to a microscale flake-like structure that helped the desirable conversion of the sealer into small fragments. In contrast, nylon’s decomposition provided a reticular network structure in the self-degraded sealer resulting in bigger fragments compared against the sealer with PVA. The PVA fiber has a high potential as a self-decomposable bridging additive in the SSASC cement sealer.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Charting the chemical space of Zintl phases with graph neural networks and bonding insights

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90 000 hypothetical Zintl phases and accurately discover 1810 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2× more accurate in predicting DFT stability than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stereomicrostructure-regulated biodegradable adhesives

Commercial adhesives are petroleum-based thermoset networks or nonbiodegradable thermoplastic hot melts, making them ideal targets for replacement by biodegradable alternatives. Poly(3-hydroxybutyrate) (P3HB) is a biorenewable and biodegradable alternative to conventional plastics, but microbial P3HB, which has a stereoperfect stereomicrostructure, exhibits no adhesion. Here, in this study, by elucidating the fundamental relationship between chemocatalytically engineered P3HB stereomicrostructures and adhesion properties, we found that biodegradable syndio-rich P3HB exhibits high adhesion strength and outperforms common commercial adhesives, whereas syndiotactic, isotactic, or iso-rich P3HB shows no measurable adhesion. The syndio-rich stereomicrostructure brings about desired thermomechanical and viscoelastic properties of P3HB that enable strong adhesion to a range of substrates tested, including aluminum, steel, glass, and wood, and its performance is insensitive to molar mass and reprocessing or reuse.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Catalyst-layer ionomer imaging of fuel cells

Investigation of membrane/electrode assembly (MEA) microstructure has become an essential step to optimize the MEA components and manufacturing processes or to study the MEA degradation. For these investigations, transmission electron microscopy (TEM) is a tool of choice as it provides direct imaging of the different components. TEM is then widely used for analyzing the catalyst nanoparticles and their carbon support. However, the ionomer inside the electrode is more difficult to be imaged. The difficulties come from the fact that the ionomer forms an ultrathin layer surrounding the carbon particles and in addition, these two components, having similar density, present no difference in contrast. In this paper, we show how the recent progresses in TEM techniques as spherical aberration (Cs) corrected HRTEM, electron tomography and X-EDS elemental mapping provide new possibilities for imaging this ionomer network and consequently to study its degradation.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Driving Electron Transfer in Photosystem I Using Far-Red Light: Overall Perspectives

Photosystem I (PSI) is a photosynthetic protein–pigment complex that, upon photoexcitation, transfers electrons to ferredoxin, facilitating the production of NADPH. Isolated PSI reaction centers (RCs) have also been used in hybrid systems to reduce protons and produce ‘biohydrogen’. This review article examines how various cyanobacteria with similar photosynthetic machinery utilize different wavelengths of light to execute photosynthetic electron transport through PSI. Key factors, such as, the structure of the electron transfer cofactors, the protein environment surrounding the primary donor pigments and hydrogen-bonding interactions with the surrounding protein matrix are analyzed to understand their roles in maintaining efficient electron transfer when it is driven using photons of different energies. We compare PSI complexes with known atomic structures from four species of cyanobacteria, Thermosynechococcus elongatus, Acaryochloris marina, Halomicronema hongdechloris, and Fischerella thermalis. T. elongatus is typical of most oxygenic photosynthetic organisms in that it requires visible light and uses only chlorophyll a (Chl a ) in PSI. In contrast, H. hongdechloris and F. thermalis are photoacclimating species capable of producing Chl f and Chl d that use red light when little visible light is available. A. marina , on the other hand, is adapted to red light conditions and consistently utilizes Chl d as its primary photosynthetic pigment, maintaining a stable pigment composition. Here, we explore the structural and functional differences between the PSI RCs of these organisms and the impact of these differences on electron transport. The structural differences in the cofactors influence both the absorption wavelengths of the cofactors and the energy levels of the intermediate states of electron transfer. An analysis of the surrounding protein shows how it has been adapted and underscores the interplay between the pigment structure, protein environment, and hydrogen bonding networks in tuning the efficiency and adaptability of photosynthetic mechanisms across different species of cyanobacteria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Spontaneous formation of InGaN nanowall network directly on Si

We present the study on epitaxial growth of an InGaN nanowall network directly on Si by plasma-assisted molecular beam epitaxy. Scanning electron microscopy, high-resolution X-ray diffraction, and transmission electron microscopy together with energy-dispersive X-ray analysis infer the crystalline nature of the InGaN nanowall network, oriented along the C-axis, with In composition ranging from pure GaN to 40%. Room temperature photoluminescence is observed, indicating good optical quality. The nanowall network is highly in-plane electrically conductive.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Challenges in materials and devices for resistive-switching-based neuromorphic computing

This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxide materials, which exhibit unique properties that emulate key functionalities needed for this application. In Sec. I, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, and a neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Sec. II, we describe the main components of a possible solid-state implementation. In Sec. III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for neuromorphic computing. In Sec. IV, we describe why transition metal oxides are promising materials for future neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Sec. V, while existing implementations are described in Sec. VI. Section VII presents applications to practical problems. We list in Sec. VIII important basic research challenges and open issues. Here, we discuss issues related to specific implementations, novel materials, devices, and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling, and integration into a neuromorphic computer may bring us closer to the development of a machine that rivals the brain.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Context-Enriched Identification of Particles with a Convolutional Network for Neutrino Events

Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Fullerene-grafted 3D graphene as electrical energy storage material

In this project we developed a concept that allows the integration of the characteristic properties of Carbon sixty (C-60) fullerene in three-dimensional (3D) graphene networks. In these composite materials, the graphene network provides high electrical conductivity and surface area while fullerenes add high electron affinity thus boosting the electrical energy storage performance of the material. Guided by theory, we developed C-60 monoadduct derivatives to optimize the graphene-fullerene interaction, specifically in the context of stability and charge transfer performance in an electrochemical environment. We demonstrated that the electrical energy storage capacity of the 3D graphene network is significantly improved upon the addition of C-60 and C-60 monoadducts by providing additional acceptor states in the form of the low lying Lowest Unoccupied Molecular Orbitals (LUMOs) of C-60 and its derivative. Using first-principles calculations we studied the key factors that determine the electrical energy storage performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Extracellular Charge Transport in Microbial Redox Chains: Linking the Living and Non-Living Worlds

The fundamental process of electron transfer (ET) within and between molecules dictates all biological energy conversion strategies, including respiration and photosynthesis. This project resulted in a comprehensive physics-based understanding of the mechanisms and limits of redox networks that mediate and regulate electron transport through microbial metabolic pathways, with special emphasis on environmental microbes that can acquire energy by catalyzing anodic or cathodic reactions on solid-state electrodes (extracellular electron transfer, or EET). By performing electron transfer to/from electrodes, such microbes may be used as biocatalysts for converting the energy stored in diverse chemical fuels to electricity, or vice versa (microbial electrosynthesis), in renewable energy technologies.

59 BASIC BIOLOGICAL SCIENCES↗