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DOE OSTI2020

Evaluating Materials Design Parameters of Hole-Selective Contacts for Silicon Heterojunction Solar Cells

Silicon heterojunction (SHJ) solar cell efficiencies are limited by parasitic absorption from the hydrogenated amorphous silicon (a-Si:H) front contact, but this may be mitigated by selecting an alternative carrier selective contact material with a wider band gap. When choosing such a material as the hole-selective contact (“p-layer”), the alignment of the material's valence band edge energy (E VB ) with that of crystalline silicon (c-Si) is an important criterion, but several other material parameters can also influence the band bending at the contact interface. Here, we simulate an (n)c-Si/(i)a-Si:H/p-layer interface to explore the influence of six materials parameters in a variable p-layer on the SHJ performance. We find a strong influence on the fill factor (FF) from thickness, doping, and E VB , and on V OC from the interfacial defect density; notably, optimal E VB is ~ 0.1 eV higher than the valence band edge energy of a-Si:H. Multiparameter sensitivity analyses demonstrate how performance is simultaneously influenced by E VB and doping; thus, both parameters should be optimized alongside one another. To assess the influence of these parameters experimentally, we grow p-type NiO x as a test-case p-layer, which shows that FFs decrease with the oxygen content likely from the increased misalignment of E VB . Although modest efficiencies are achieved experimentally (>7%), what is important is that our model simulates performance trends. With these results, we apply a materials discovery pipeline to suggest new materials (e.g., ZnTe and BeTe) to try as p-layers in the SHJ. This combination of simulations, experiments, and materials discovery informs a better understanding of contact selection in SHJ cells.

14 SOLAR ENERGY↗

DOE OSTI2026

An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

A strongly selected mutation in the HIV-1 genome is independent of T cell responses and neutralizing antibodies

Mutations rapidly accumulate in the HIV-1 genome after infection. Some of those mutations are selected by host immune responses and often cause viral fitness losses. This study is to investigate whether strongly selected mutations that are not associated with immune responses result in fitness losses. Strongly selected mutations were identified by analyzing 5'-half HIV-1 genome (gag/pol) sequences from longitudinal samples of subject CH0131. The K43R mutation in the gag gene was first detected at day 91 post screening and was fixed in the viral population at day 273 while the synonymous N323tc mutation was first detected at day 177 and fixed at day 670. No conventional or cryptic T cell responses were detected against either mutation sites by ELISpot analysis. However, when fitness costs of both mutations were measured by introducing each mutation into their cognate transmitted/founder (T/F) viral genome, the K43R mutation caused a significant fitness loss while the N323tc mutation had little impact on viral fitness. In conclusion, the rapid fixation, the lack of detectable immune responses and the significant fitness cost of the K43R mutation suggests that it was strongly selected by host factors other than T cell responses and neutralizing antibodies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Ancient polymorphisms contribute to genome-wide variation by long-term balancing selection and divergent sorting in Boechera stricta

Genomic variation is widespread, and both neutral and selective processes can generate similar patterns in the genome. These processes are not mutually exclusive, so it is difficult to infer the evolutionary mechanisms that govern population and species divergence. Boechera stricta is a perennial relative of Arabidopsis thaliana native to largely undisturbed habitats with two geographic and ecologically divergent subspecies. Here, we delineate the evolutionary processes driving the genetic diversity and population differentiation in this species. Using whole-genome re-sequencing data from 517 B. stricta accessions, we identify four genetic groups that diverged around 30-180 thousand years ago, with long-term small effective population sizes and recent population expansion after the Last Glacial Maximum. We find three genomic regions with elevated nucleotide diversity, totaling about 10% of the genome. These three regions of elevated nucleotide diversity show excess of intermediate-frequency alleles, higher absolute divergence ( d XY ), and lower relative divergence ( F ST ) than genomic background, and significant enrichment in immune-related genes, reflecting long-term balancing selection. Scattered across the genome, we also find regions with both high F ST and d XY among the groups, termed F ST -islands. Population genetic signatures indicate that F ST -islands with elevated divergence, which have experienced directional selection, are derived from divergent sorting of ancient polymorphisms. Our results suggest that long-term balancing selection on disease resistance genes may have maintained ancestral haplotypes across different geographical lineages, and unequal sorting of balanced polymorphisms may have generated genomic regions with elevated divergence. This study highlights the importance of ancestral balanced polymorphisms as crucial components of genome-wide variation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Small Reactors in Microgrids: Technology Modeling and Selection (Net-Zero Microgrid Program Project Report)

This report demonstrates the capabilities of the net-zero microgrid (NZM) Xendee platform for modeling an SR module with electricity, heat extraction and thermal storage in microgrids configurations. The model effectively captures the most important technical and economic considerations for SR technology specific analysis: cost and operational characteristics of SR technology and financial costs and incentives. The model can analyze multiple scenarios to establish metrics for cost-competitive and zero-carbon microgrids connected to the grid or completely isolated. The model is fully integrated within the Xendee platform for modeling and analysis of clean energy microgrids with storage and generation from renewable energy sources. The model captures the capabilities, constraints, and nuances of SR by incorporating parameters related to plant economics, design efficiency and performance, plant operation and component and fuel lifespan. The cost and operational parameters modeled in the SR module are specific to the technology selected for integration in the microgrid. Cost parameters recognize advanced nuclear technology for modular production and installation based on economies of scale from factory manufacture and related commissioning, and cost reduction through technology maturation—first-of-a-kind (FOAK) and nth-of-a-Kind (NOAK). The cost parameters include installation, operations and maintenance (O&M), fuel refueling cycle, and reactor life. Installation cost reflects economies of scale due to unit sizing at scale and colocation. O&M economies of scale for both fixed- and variable-cost fuel life-cycle costs are incurred at every refueling interval, with separate front- and back-end fuel costs, as well as waste-handling and disposition costs. This report investigates key characteristics of different SR technologies suitable for microgrid applications, including design principles, sizing, coolant properties, temperature ratings, fuel structures, and life-cycle considerations. This also includes fuel technologies applicable to these SR systems, alongside strategies for nuclear-waste and spent-fuel management and approaches to address safety, security, and proliferation challenges. Four primary groups of SR technologies are examined: water-cooled, liquid-metal-cooled, high-temperature gas-cooled, and molten-salt-cooled systems. In this report, an initial guideline for technology selection is established, aligning the characteristics of the technologies with the requirements of microgrids. The selection of technology in a microgrid is influenced by various factors, including financial capacity, location and accessibility, demand type and characteristics, reliability and resilience requirements, area constraints, and the lifespan of the microgrid. The types of electrical and non-electrical applications within the microgrid also play a significant role in technology selection. The characteristics of SRs, such as their smaller size, modularity, transportability, long refueling interval, improved safety features, ability to operate in autonomous or semi-autonomous mode, and provision of high-grade heat, are particularly appealing for microgrids. Furthermore, a list of considerations for implementing SRs in microgrids is outlined. The SR model is created to be continuously improved with the acquisition of actual data on investment and operational costs, experience with supply chains, production at scale, and field deployments. In the near term, performance data on applications in microgrids will become available from lessons learned from laboratory tests, such as those planned for the Microreactor Applications Research Validation and Evaluation Project (MARVEL), led by Idaho National Laboratory (INL). The SR model incorporates scenario data and known SR design specifications, enabling technoeconomic analysis for SR deployment in microgrids. It specifically considers the distinctive attributes of SRs as generators in technoeconomic studies. SRs can be modeled and analyzed with generation from renewable-energy sources, energy storage, and flexible loads over a range of functionality and applications. This offers a comprehensive tool for feasibility studies, scenario development, and sensitivity analysis for “what-if” consideration of any range of assumptions about SRs in microgrids and other aggregations of distributed-energy resources, including virtual power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Applications of fuzzy logic and best-worst method for tritium sensor selection

Accurate assessment of tritium as a fuel source is critical in fusion reactions, necessitating effective sensor evaluation methods. This study investigates a multi-criteria decision-making framework for selecting tritium sensors, integrating fuzzy logic to enhance decision quality. Initial attempts at applying fuzzy logic were found to be too elementary and failed to capture the complexity of multi-criteria selection; this prompted a refined approach that incorporated expert insights and advanced ranking techniques for sensor evaluation. The research used a two-stage methodology. In the first stage, important criteria and sub-criteria for sensor performance were identified and defined. These criteria were then weighted and scored using a fuzzy best-worst method, drawing upon expert opinions to ensure relevance and validity. The second stage involved interpreting information about varying sensors to rank them based on their overall criteria scores, encouraging the selection of the most suitable options. The result of the study is a proposed method for effective sensor selection in fusion reactors, which in turn will significantly improve the reliability of tritium monitoring in fusion applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Supported Pt Nanoparticles on Mesoporous Titania for Selective Hydrogenation of Phenylacetylene

Semi-hydrogenation of alkynes to alkenes is one of the most important industrial reactions. However, it remains technically challenging to obtain high alkene selectivity especially at a high alkyne conversion because of kinetically favorable over hydrogenation. In this contribution, we show that supported ultrasmall Pt nanoparticles (2.5 nm) on mesoporous TiO2 (Pt@mTiO2) remarkably improve catalytic performance toward semi-hydrogenation of phenylacetylene. Pt@mTiO2 is prepared by co-assembly of Pt and Ti precursors with silica colloidal templates via an evaporation-induced self-assembly process, followed by further calcination for thermal decomposition of Pt precursors and crystallization of mTiO2 simultaneously. As-resultant Pt@mTiO2 discloses a high hydrogenation activity of phenylacetylene, which is 2.5 times higher than that of commercial Pt/C. More interestingly, styrene selectivity over Pt@mTiO2 remains 100% in a wide phenylacetylene conversion window (20–75%). The styrene selectivity is >80% even at 100% phenylacetylene conversion while that of the commercial Pt/C is 0%. The remarkable styrene selectivity of the Pt@mTiO2 is derived from the weakened styrene adsorption strength on the atop Pt sites as observed by diffuse reflectance infrared Fourier transform spectroscopy with CO as a probe molecule (CO-DRIFTS). Our strategy provides a new avenue for promoting alkyne to alkene transformation in the kinetically unfavorable region through novel catalyst preparation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Editorial: Selective Controls on Microbial Energy Metabolisms: From the Microscale to the Macroscale

A mechanistic and predictive understanding of the genotypic and phenotypic controls on microbial element cycling remains a grand challenge in microbiology. While the composition and concentration of carbon sources, electron donors, and electron acceptors are known to influence microbial community structure by selecting for microbial sub-populations with distinct catabolic and respiratory pathways, selective inhibitors and trace nutrient availability modulate the activity of metabolic enzymes. This in turn influences the distribution of microbial sub-populations with distinct metabolic and respiratory traits, which are then structured by complex multi-dimensional environmental gradients that influence the composition, gene content and element cycling activity of microbiomes. While some parameters are known to select for different respiratory activities (e.g., lanthanides as essential nutrients for methanotrophs, molybdate as a specific inhibitor of sulfate reduction, carbon:nitrogen ratio and concentration as a control on the end-products of nitrate respiration), there are others to discover, and demonstrating how selective parameters operate and mediate element cycling across scales requires multi-disciplinary research in both the lab and field.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection

The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Selected Area Deposition of High Purity Gold for Functional 3D Architectures

Selected area deposition of high purity gold films onto nanoscale 3D architectures is highly desirable as gold is conductive, inert, plasmonically active, and can be functionalized with thiol chemistries, which are useful in many biological applications. Here, we show that high-purity gold coatings can be selectively grown with the Me 2 Au (acac) precursor onto nanoscale 3D architectures via a pulsed laser pyrolytic chemical vapor deposition process. The selected area of deposition is achieved due to the high thermal resistance of the nanoscale geometries. Focused electron beam induced deposits (FEBID) and carbon nanofibers are functionalized with gold coatings, and we demonstrate the effects that laser irradiance, pulse width, and precursor pressure have on the growth rate. Furthermore, we demonstrate selected area deposition with a feature-targeting resolutions of ~100 and 5 µm, using diode lasers coupled to a multimode (915 nm) and single mode (785 nm) fiber optic, respectively. The experimental results are rationalized via finite element thermal modeling.

3-dimensional nanostructures↗

NASA NTRS1993

Component Selection, Accelerated Testing, and Improved Modeling of AMTEC Systems for Space Power

Alkali metal thermal to electric converter (AMTEC) designs for space power are numerous, but the selection of materials for construction of long-lived AMTEC devices has been limited to electrodes, current collectors, and the solid electrolyte. AMTEC devices with lifetimes greater than 5 years require careful selection and life testing of all hot-side components. The likely selection of a remote condensed design for initial flight test and probable use with a radioisotope heat source in AMTEC powered planet probes requires the device to be constructed to tolerate operating T greater than 1150K, as well as exposure to Na (g) , and Na (liq) on the high pressure side. The temperatures involved make the characterization of high strength and chemical resistance to Na containing Na 2 O critical. Selection among materials which can be worked should not be driven by ease of fabricablity, as high temperature stability is the critical issue. These concepts drive the selecton of Mo alloys for Na (liq) containment in AMTEC cells for T to 1150K operation, as they are significantly stronger than comparable Nb or Ta alloys, are less soluble in Na (liq) containing dissolved Na 2 O, are workable compared with W alloys (which might be used for certain components), and are ductile at the T greater than 500K of proposed AMTEC modules in space applications.

AMTEC↗

NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗

DOE OSTI2022

Effect of Applied Stress on the Selective Oxidation of Alloy 600 Exposed to PWR Primary Water and Rhines Pack Environments

A mechanistic understanding of corrosion and oxidation processes is crucial to ensure long-term resistance to stress corrosion cracking of Ni-base structural alloys in pressurized water reactor (PWR) primary and secondary systems. Aqueous corrosion of Ni-base alloys under PWR primary water conditions was previously compared to gaseous oxidation with comparable oxygen potentials. However, those studies considered only stress-free conditions, which removes a critical parameter for understanding stress corrosion behaviors. A significant knowledge gap exists for the role of applied stresses on selective internal oxidation in gaseous environments. A micro-4-point-bend jig was designed to facilitate stress in a Rhines Pack set-up. Selective oxidation at different stress and strain conditions was investigated in a series of Rhines Pack experiments for Alloy 600. Focused ion beam milling enabled the extraction of site-specific specimens containing selected high-energy grain boundaries from tensile and compressive stress regions. Analytical transmission electron microscopy was employed to analyze the microstructure and chemical composition of the resulting oxides. Protective surface oxide films and selective intergranular oxides were observed at different conditions. The implications of these fundamental differences are discussed relative to intergranular attack and stress corrosion cracking mechanisms in PWR primary water.

stress, 4-point-bend, selective oxidation, Alloy 6↗

DOE OSTI2021

Direct Chemical Vapor Deposition Synthesis of Porous Single-Layer Graphene Membranes with High Gas Permeances and Selectivities

Single-layer graphene containing molecular-sized in-plane pores is regarded as a promising membrane material for high-performance gas separations due to its atomic thickness and low gas transport resistance. However, typical etching-based pore generation methods cannot decouple pore nucleation and pore growth, resulting in a trade-off between high areal pore density and high selectivity. In contrast, intrinsic pores in graphene formed during chemical vapor deposition are not created by etching. Therefore, intrinsically porous graphene can exhibit high pore density while maintaining its gas selectivity. Here in this work, the density of intrinsic graphene pores is systematically controlled for the first time, while appropriate pore sizes for gas sieving are precisely maintained. As a result, single-layer graphene membranes with the highest H 2 /CH 4 separation performances recorded to date (H 2 permeance > 4000 GPU and H 2 /CH 4 selectivity > 2000) are fabricated by manipulating growth temperature, precursor concentration, and non-covalent decoration of the graphene surface. Moreover, it is identified that nanoscale molecular fouling of the graphene surface during gas separation where graphene pores are partially blocked by hydrocarbon contaminants under experimental conditions, controls both selectivity and temperature dependent permeance. Overall, the direct synthesis of porous single-layer graphene exploits its tremendous potential as high-performance gas-sieving membranes.

chemical vapor deposition↗

DOE OSTI2019

Copper‐Catalyzed C(sp 3 )−H Amidation: Sterically Driven Primary and Secondary C−H Site‐Selectivity

Abstract Undirected C(sp 3 )−H functionalization reactions often follow site‐selectivity patterns that mirror the corresponding C−H bond dissociation energies (BDEs). This often results in the functionalization of weaker tertiary C−H bonds in the presence of stronger secondary and primary bonds. An important, contemporary challenge is the development of catalyst systems capable of selectively functionalizing stronger primary and secondary C−H bonds over tertiary and benzylic C−H sites. Herein, we report a Cu catalyst that exhibits a high degree of primary and secondary over tertiary C−H bond selectivity in the amidation of linear and cyclic hydrocarbons with aroyl azides ArC(O)N 3 . Mechanistic and DFT studies indicate that C−H amidation involves H‐atom abstraction from R‐H substrates by nitrene intermediates [Cu](κ 2 ‐ N , O ‐NC(O)Ar) to provide carbon‐based radicals R . and copper(II)amide intermediates [Cu II ]‐NHC(O)Ar that subsequently capture radicals R . to form products R‐NHC(O)Ar. These studies reveal important catalyst features required to achieve primary and secondary C−H amidation selectivity in the absence of directing groups.

Bakhoda, Abolghasem (Gus)↗

DOE OSTI2019

Site-Selective Functionalization of (sp 3 )C-H Bonds Catalyzed by Artificial Metalloenzymes Containing an Iridium-Porphyrin Cofactor

The selective functionalization of one C-H bond over others in nearly identical steric and electronic environments can facilitate the construction of complex molecules. We report site-selective functionalizations of C-H bonds, differentiated solely by remote substituents, catalyzed by artificial metalloenzymes (ArMs) that are generated from the combination of an evolvable P450 scaffold and an iridium-porphyrin cofactor. The generated systems catalyze the insertion of carbenes into the C-H bonds of a range of phthalan derivatives containing substituents that render the two methylene positions in each phthalan inequivalent. These reactions occur with site-selectivity ratios of up to 17.8:1 and, in most cases, with pairs of enzyme mutants that preferentially form each of the two constitutional isomers. This study demonstrates the potential of abiotic reactions catalyzed by metalloenzymes to functionalize C-H bonds with site selectivity that is difficult to achieve with small-molecule catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Protection Strategies Enable Selective Conversion of Biomass

Abstract Selective and economic conversion of lignocellulosic biomass components to bio‐based fuels and chemicals is the major goal of biorefineries, but low yields and selectivity for fuel precursors such as sugars, furanics, and lignin‐derived monomers pose significant disadvantages in process economics. In this Minireview we summarize the existing protection strategies used in biomass chemocatalytic conversion processes and focus the discussions on the mechanisms, challenges, and opportunities of each strategy. We introduce a concept of using analogous methods to manipulate biomass catalytic conversion pathways during the upgrading of carbohydrates to fuels and chemicals. This Minireview may provide new insights into the development of selective biorefining processes from a different perspective, expanding the options for selective conversion of biomass to fuels and chemicals.

Luo, Xiaolin↗

DOE OSTI2020

Supramolecular Tuning Enables Selective Oxygen Reduction Catalyzed by Cobalt Porphyrins for Direct Electrosynthesis of Hydrogen Peroxide

Abstract We report a supramolecular strategy for promoting the selective reduction of O 2 for direct electrosynthesis of H 2 O 2 . We utilized cobalt tetraphenylporphyrin (Co‐TPP), an oxygen reduction reaction (ORR) catalyst with highly variable product selectivity, as a building block to assemble the permanently porous supramolecular cage Co‐PB‐1(6) bearing six Co‐TPP subunits connected through twenty‐four imine bonds. Reduction of these imine linkers to amines yields the more flexible cage Co‐rPB‐1(6). Both Co‐PB‐1(6) and Co‐rPB‐1(6) cages produce 90–100 % H 2 O 2 from electrochemical ORR catalysis in neutral pH water, whereas the Co‐TPP monomer gives a 50 % mixture of H 2 O 2 and H 2 O. Bimolecular pathways have been implicated in facilitating H 2 O formation, therefore, we attribute this high H 2 O 2 selectivity to site isolation of the discrete molecular units in each supramolecule. The ability to control reaction selectivity in supramolecular structures beyond traditional host–guest interactions offers new opportunities for designing such architectures for a broader range of catalytic applications.

Smith, Peter T.↗