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DOE OSTI2025

Gate-Tunable Short-Wave Infrared Polycrystalline GeSn Phototransistors on Noncrystalline Substrates

GeSn is a group-IV alloy with immense potential to advance microelectronics technology due to its intrinsic compatibility with existing Si CMOS processes. With a sufficiently high Sn composition, GeSn is classified as a direct bandgap semiconductor. Polycrystalline GeSn holds several additional advantages, including its significantly lower synthesis cost compared to its epitaxial counterpart, as well as the versatility to grow these films on a variety of substrates. Here, in this work, we present a polycrystalline thin-film GeSn phototransistor on a fused silica substrate with a Sn composition of ~10%, showing a photoresponse in the short-wave infrared wavelength range, critical for emerging sensing applications. This device shows a gate-tunable response, with responsivities approaching up to 1.7 mA/W with only a 30 nm-thick GeSn layer. Furthermore, phototransistors offer additional adaptability through gating, which allows for the reduction of dark current. This not only enhances the signal-to-noise ratio but also offers more flexible integration with various image sensor readout implementations using different substrates. The specific detectivity of this phototransistor is within an order of magnitude of those of previously reported GeSn photodetectors grown by molecular beam epitaxy and chemical vapor deposition, even though the absorber is 3 to 20× thinner while the electrode spacing for photocarrier transport is approximately 15× longer than the carrier diffusion length in this work, showing great potential benefits of extending similar device structures to epitaxial GeSn layers. As these GeSn phototransistors utilize a noncrystalline substrate, our work establishes a fundamentally more versatile path toward monolithically integrated GeSn-based photodetectors for next-generation multimodal sensors.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Civil Procedure for the Anthropocene

Climate considerations must become an element of procedural analysis. Scholars, rule makers, legislators, and judges have historically prioritized procedural values including cost, speed, and accuracy. More recent waves of scholarship—including critical perspectives—have emphasized participation as a further procedural goal. Yet this list has grown stale. Current environmental realities force all areas of law, including civil procedure, to reckon with climate change. In the future, proceduralists will be forced to integrate climate-aware thinking into their analyses, proposals, and discussions. Predictably, minds will disagree on how to accomplish this and the kinds of trade-offs that are warranted. Yet the fundamental point of this article remains: climate change resilience is a procedural concern that has not been recognized but must be. This signals not the genesis of a new branch of procedural scholarship but an opportunity to broaden the discussion for all procedural scholarship. In addition, this Article serves as a guide for rule makers and legislators who have just recently begun to show interest in climate-aware procedures. It directs them to procedural topics that require urgent attention, provides implementation strategies for developing resilient procedures, and considers the attendant costs and benefits of different approaches ranging from laissez-faire to aggressive engagement. Just as civil procedural innovations contributed to the emergence and success of the civil rights movement, our collective response to climate change will also be shaped by civil procedure. This Article prepares for this future and offers a corrective to the field’s failure to anticipate and respond to climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Subsurface Characteristics of Metal-Halide Perovskites Polished by an Argon Ion Beam

Focused ion beam (FIB) techniques have been frequently used to section metal-halide perovskites for microstructural investigations. However, the ion beams directly irradiating the sample surface may alter its properties far different from those of pristine, potentially leading to modified deterioration mechanisms under aging stressors. Here, we combine complementary approaches to measure the subsurface characteristics of polished perovskites and identify the chemical species responsible for the measured properties. Analysis of the experimental results in conjunction with Monte Carlo simulations indicates that atomic displacements and local heating occur in the first ≈15 nm of the subsurface of methylammonium lead iodide (MAPbI 3 ) by glancing-angle Ar + beam irradiation (4 kV at 3°). The lead-rich, iodine-deficient surface promotes rapid phase segregation under thermal aging conditions. On the other hand, despite the subsurface modification, our experiments confirm that the rest of the MAPbI 3 bulk retains the material integrity. Our observation supports that polished perovskites could serve in studying the properties of bulk or buried junctions far away from the altered subsurface with care.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure, input velocity, bed porosity, column length, and radius were optimized to minimize the capture cost. After optimization, a sensitivity analysis revealed the complex interplay between the decision variables and their effect on the specific energy and cost of removing the CO 2 . We optimized the capture cost while taking into account the trade-off between energy consumption and productivity. The resulting minimum capture cost was determined to be 265.2 $/t-CO 2 , which aligns with expected values reported in the literature. Numerical results suggest the effectiveness of the optimization strategies applied, and underscore the importance of simultaneous decision variable selection in improving the performance in direct air capture processes. We also extend the modeling approach to a 2D axisymmetric model to better visualize CO₂ uptake and temperature profiles, revealing significant radial gradients during the regeneration step. As a main drawback, this enhanced model comes with a computational cost approximately 40 times higher than that of the 1D model.

Adsorption-desorption process↗

DOE OSTI2020

Acetonitrile Destruction in ETF Feed Solutions by UV/OX and Persulfate (Final Report)

The Hanford Site Effluent Treatment Facility (ETF) currently treats aqueous waste streams that include condensates from the 242-A evaporator, leachate from the Environmental Restoration Disposal Facility (ERDF), as well as laboratory wastes and, in the future, will treat liquid effluents from the Hanford Waste Treatment and Immobilization Plant (WTP) and Integrated Disposal Facility (IDF) leachate. Liquid effluents from the WTP will have significant concentrations of acetonitrile. Acetonitrile is formed by reaction of nitrates and sugar in the WTP low activity waste (LAW) melters and is prevalent in the submerged bed scrubber (SBS) and wet electrostatic precipitator (WESP) liquid effluents from WTP off-gas treatment. When these liquids are concentrated in the WTP Effluent Management Facility (EMF) evaporator in the direct feed low activity waste (DFLAW) flow-sheet, testing has shown that the majority of the acetonitrile partitions to the evaporator condensate. Since the evaporator condensate is directed to the ETF, this creates a potential issue with the ETF waste acceptance criteria and, specifically, the ETF Delisting Modification treatability envelope limits. Consequently, there is a need to assess possible mitigation approaches, one of which involves identification and assessment of potential treatment processes to destroy the acetonitrile, which is the subject of the present report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Testing of UV Oxidation Process for Acetonitrile Destruction

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid

Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. Here, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Review of metal-containing resists in electron beam lithography: perspectives for extreme ultraviolet patterning

Background: Metal-containing resists entered the mainstream semiconductor industry process flow to mitigate the low absorbance of extreme ultraviolet (EUV) radiation by thin films of organic resists that lead to poor sensitivity and their inability to handle rigors of development and etching conditions. Aim: The long and rich history of using metal-containing resists in electron beam lithography can offer interesting lessons, pointers, and insights to the relatively newcomer EUV lithography, which is slightly over a decade old. Approach: Electron beam lithography has been enjoying a considerable amount of freedom in the choice of resist materials for close to 50 years; especially the use of metal-containing resists to attain not only single digit nanometer resolution, higher sensitivity, and etch resistance but also lower line-edge roughness. Here, we make a comprehensive historical review of the progress made in the patterning of metal-containing resists in electron beam lithography and derive insights that can be potentially useful in EUV patterning. Perspectives: Small molecular weight resists are proven to be crucial for achieving higher resolution with low line-edge roughness. Simplifying process flow by reducing etch-stack-layers is conceivable with metal-containing resists, along with direct-patterning of functional materials for heterogeneous integration. Efficient contact hole patterning at tighter pitches may be incumbent on progress in positive-tone resist research.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

The Fundamental Creep Behavior Model of Gr.91 Alloy by ICME Approach (Final Report)

The current project mainly focused on the fundamental creep cracking mechanism of the Grade 91 system during the operation conditions in the advanced technologies FE power plants and build the link among Composition-Processing Parameters-Phase Stability-Microstructure-Creep Resistance. A model based on computational thermodynamics and diffusion kinetics will be developed to provide guidance on how to improve the creep resistance of the alloy system. The PI mainly investigate the Gr.91 base alloy and weldment with the Integrated Computational Materials Engineering (ICME) approach. The long-term goal of the proposed program is to develop a model for creep and fatigue resistant alloys with different elemental systems, compositions, and processing parameters of weldment and heat-treatment. In addition, new creep and fatigue resistant alloys are to be designed based on the predictions from the model. The computational as well as experimental results include three sections: 1) The compositional optimization and secondary phases evaluation regarding the creep resistance in Grade 91 steel through the CALPHAD approach. In this section, the formation of the critical secondary phases, M23C6, MX and Z-phase were well predicted based on many conditions like the temperature as well as compositions. Meanwhile, the role of different alloying elements was also considered as they will directly affect the formation of the secondary phases mentioned above based on the compositional evolution of the alloying elements. 2) An investigation of Creep Resistance in Grade 91 Steel through Computational Thermodynamics. We systematically considered the evolution of the four critical temperatures, Ac3, Ac1, the threshold of M23C6 and Z-phase, on basis of the computational approach. Meanwhile, the equilibrium cooling as well as Scheil simulations were also considered to perform further predictions based on the different cooling rates. 3) Creep lifetime and microstructural evolution of the Grade 91 steel. In this part, mainly experimental approach was performed on the real creep test from the beginning of the commercial Gr.91 alloys. The alloys were normalized, tempered, welded (Gleeble) and PWHT and finally subjected to the creep lifetime analyses. Finally, all the samples after each process were further characterized based on the OM, XRD and SEM technique to draw final conclusions.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

User’s Manual for Seal_Flux: A Seal Barrier Reduced-Order Model (Update)

This report provides a brief description on the use of the Seal_Flux computer program developed as part of the effort to quantify the risk of geologic storage of carbon dioxide (CO 2 ) under the U.S. Department of Energy’s (DOE) National Risk Assessment Partnership (NRAP). The Seal_Flux code simulates the flow of CO 2 through a low permeability rock horizon or seal formation overlying the storage reservoir into which CO 2 is injected. A two-phase, relative permeability approach with Darcy’s law is used for one-dimensional (1D) flow computations of CO 2 through the horizon in the vertical direction. The code also allows the simulation of time-dependent processes that can influence such flow. However, as part of its design, Seal_Flux is what can be termed a “reduced-order model” (ROM) and is not intended as a full-functioning flow code. The theory and simulation in the code is streamlined and directed towards the implementation of Monte Carlo risk analyses of CO 2 transport or as termed in this context as “leakage.” While presented in this report as a stand-alone tool, the Seal_Flux code is intended to function in the future as one of several models as part of an integrated, systems-level model of CO 2 storage performance. Finally, the code is written in Python 3.10 to provide an open framework for further development by others and to assist in linking the code with other modules in an integrated assessment model.

58 GEOSCIENCES↗

DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance

This study focuses on the performance of the elastic–viscous–plastic (EVP) dynamical solver within the sea ice model, CICE v6.5.1. The study has been conducted in two steps. First, the standard EVP solver was extracted from CICE for experiments with refactored versions, which are used for performance testing. Second, one refactored version was integrated and tested in the full CICE model to demonstrate that the new algorithms do not significantly impact the physical results. The study reveals two dominant bottlenecks, namely (1) the number of Message Parsing Interface (MPI) and Open Multi-Processing (OpenMP) synchronization points required for halo exchanges during each time step combined with the irregular domain of active sea ice points and (2) the lack of single-instruction, multiple-data (SIMD) code generation. The standard EVP solver has been refactored based on two generic patterns. The first pattern exposes how general finite differences on masked multi-dimensional arrays can be expressed in order to produce significantly better code generation by changing the memory access pattern from random access to direct access. The second pattern takes an alternative approach to handle static grid properties. The measured single-core performance improvement is more than a factor of 5 compared to the standard implementation. The refactored implementation of strong scales on the Intel® Xeon® Scalable Processors series node until the available bandwidth of the node is used. For the Intel® Xeon® CPU Max series, there is sufficient bandwidth to allow the strong scaling to continue for all the cores on the node, resulting in a single-node improvement factor of 35 over the standard implementation. This study also demonstrates improved performance on GPU processors.

58 GEOSCIENCES↗

DOE OSTI2023

Sensitivity analysis of the effect of wind and wake characteristics on wind turbine loads in a small wind farm

Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, which are typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. Turbines experience altered inflow conditions when placed in the wake of upstream turbines, which can additionally influence the fatigue and ultimate loads. It is important to understand the impact of uncertainty on the resulting loads of both unwaked and waked turbines. The goal of this work is to assess which wind-inflow-related and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters are screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study uses the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine, simulated with OpenFAST and synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with the IEC exponential coherence model using the NREL tool TurbSim. The focus is on sensitivity to individual parameters, though interactions between parameters are considered, and how sensitivity differs between waked and unwaked turbines. The results of this work show that for both waked and unwaked turbines, ambient turbulence in the primary wind direction and shear are the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines are identified as yaw misalignment, streamwise integral length, and the exponent and streamwise components of the IEC coherence model. The tertiary parameters of importance differ between waked and unwaked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, air density, and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation, lateral and vertical wind integral lengths, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in the fatigue and ultimate load sensitivity of downstream turbines.

17 WIND ENERGY↗

DOE OSTI2023

Bottom-up synthesis of mesoscale nanomeshes of graphene nanoribbons on germanium

The synthesis of functional graphene nanostructures on Ge(001) provides an attractive route toward integrating graphene-based electronic devices onto complementary metal oxide semiconductor-compatible platforms. In this study, we leverage the phenomenon of the anisotropic growth of graphene nanoribbons from rationally placed graphene nanoseeds and their rotational self-alignment during chemical vapor deposition to synthesize mesoscale graphene nanomeshes over areas spanning several hundred square micrometers. Lithographically patterned nanoseeds are defined on a Ge(001) surface at pitches ranging from 50 to 100 nm, which serve as starting sites for subsequent nanoribbon growth. Rotational self-alignment of the nanoseeds followed by anisotropic growth kinetics causes the resulting nanoribbons to be oriented along each of the equivalent, orthogonal Ge < 110 > directions with equal probability. As the nanoribbons grow, they fuse, creating a continuous nanomesh. In contrast to nanomesh synthesis via top-down approaches, this technique yields nanomeshes with atomically faceted edges and covalently bonded junctions, which are important for maximizing charge transport properties. Additionally, we simulate the electrical characteristics of nanomeshes synthesized from different initial nanoseed-sizes, size-polydispersities, pitches, and device channel lengths to identify a parameter-space for acceptable on/off ratios and on-conductance in semiconductor electronics. The simulations show that decreasing seed diameter and pitch are critical to increasing nanomesh on/off ratio and on-conductance, respectively. With further refinements in lithography, nanomeshes obtained via seeded synthesis and anisotropic growth are likely to have superior electronic properties with tremendous potential in a multitude of applications, such as radio frequency communications, sensing, thin-film electronics, and plasmonics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Power Grid Reliability Estimation via Adaptive Importance Sampling

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this letter, we propose computationally efficient and accurate methods to estimate the probability of line overflow, i.e., reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate overload probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. Herein, we propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.

power system faults↗