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DOE OSTI2025

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images

The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis.

Science & Technology - Other Topics↗

DOE OSTI2021

Perovskite on Silicon Tandem Solar Cells

Since installing solar panels costs more than the panels themselves, it is critically important to improve the efficiency of the panels so that fewer panels need to be installed. More than 90% of all panels being manufactured are made with silicon solar cells. The most promising approach to reach 27% is to upgrade the panels by adding a second set of cells with a larger band gap that can harvest the higher energy photons in the visible spectrum and generate a higher voltage than silicon is capable of generating. Perovskite semiconductors are very promising for low-cost tandems because they have high band gaps, can be rapidly printed from solutions and have been used to make 25% efficient cells by themselves. If we show that it is possible to make stable tandems with 30% efficiency using materials that could be deposited at low cost and that the energy yield will not be compromised, then private companies would be able to raise the capital needed to manufacture this technology. The main metrics for this project are the power conversion efficiency of a lab-scale perovskite/silicon tandem and the ability to maintain a high power conversion efficiency in perovskite solar cells after accelerated operational stability testing under maximum power point at elevated temperature and 1 sun illumination. In short, this project demonstrates improvements to the perovskite absorber layer, contacts for the perovskite solar cell, and transparent oxide layers for window electrodes to achieve tandem efficiencies of 25% after one year and 27% after two years. By the end of the project, we identify strategies to further improve light management in the tandem devices and outline a pathway to reach 30%.In parallel, stability testing is conducted to evaluate said absorber and contact layers in the perovskite top cell, demonstrating 1000 hours of operation with less than 10% degradation at 1 sun. Our work on this project has made a leading impact on the development of perovskite/silicon tandems. The triple halide perovskite that we developed using iodide, bromide, and chloride and published in Science in March 2020 has received significant interest from other experts in the field at conferences. We took a silicon bottom cell with 21% efficiency, a technology that is responsible for a $30 billion market, and improved it by 30% relative. In addition, we have demonstrated impressive stability of our perovskite semitransparent top cells used in the tandems, showing for the first time packaged devices that pass a variety of IEC stability tests including damp heat, temperature cycling, and UV exposure tests.

14 SOLAR ENERGY↗

DOE OSTI2024

High‐Performance Ternary Organic Solar Cells with Enhanced Luminescence Efficiency and Miscibility Enabled by Two Compatible Acceptors

Abstract The ternary strategy has proven to be an effective method for improving the efficiency of organic solar cells (OSCs). However, designing and selecting the third component still pose challenges. In this study, this issue is addressed by focusing on the PBDB‐T:Y18‐F binary system and introducing a new, strong luminescent, asymmetric small‐molecule acceptor (SMA) called L8‐CBIC‐Cl, which shares a similar skeleton with Y18‐F. The similarity in molecular framework facilitates good compatibility between the two acceptors, resulting in the formation of an alloy‐like acceptor phase. Furthermore, the norbornenyl‐modified end group in L8‐CBIC‐Cl contributes to its strong luminescent properties, which in turn leads to a low non‐radiative energy loss and a high open‐circuit voltage. Consequently, the PBDB‐T:L8‐CBIC‐Cl:Y18‐F based ternary devices realize a high power conversion efficiency (PCE) up to 17.01%, which is higher than PBDB‐T:Y18‐F device (14.49%). Importantly, L8‐CBIC‐Cl exhibits a good universality as a guest acceptor in other three binary systems (D18:Y6, D18:BTP‐eC9‐4F, and D18:L8‐BO). The D18:L8‐BO:L8‐CBIC‐Cl device shows an impressive efficiency of 19%. The work demonstrates that employing SMA with a high PLQY and better miscibility with host acceptor as the third component has a great potential for developing high‐efficiency ternary OSCs.

Chemistry↗

DOE OSTI2020

From physics to fixtures to food: current and potential LED efficacy

Light-emitting diodes (LEDs) have enabled a historic increase in the conversion of electric energy to photons, but this is approaching a physical limit. The theoretical maximum efficiency occurs when all input energy is converted to energy in photosynthetic photons. Blue LEDs can be 93% efficient, phosphor-converted “whites” 76% efficient, and red LEDs 81% efficient. These improvements open new opportunities for horticultural lighting. Here we review (1) fundamental physics and efficiency of LEDs, (2) the current efficacy of LEDs, (3) the effect of spectral quality on crop yield, and (4) the potential efficacy of horticultural fixtures. Advances in the conversion of photons to yield can be achieved by optimization of spectral effects on plant morphology, which vary among species. Conversely, spectral effects on photosynthesis are remarkably similar across species, but the conventional definition of photosynthetic photons (400–700 nm) may need to be modified. The upper limit of LED fixture efficacy is determined by the LED package efficacy multiplied by four factors inherent to all fixtures: current droop, thermal droop, driver (power supply) inefficiencies, and optical losses. With current LED technology, the calculations indicate efficacy limits of 3.4 µmol J- 1 for white + red fixtures, and 4.1 µmol J -1 for blue + red fixtures. Adding optical protection from water and high humidity reduces these values by ~10%. We describe tradeoffs between peak efficacy and cost.

Agriculture↗

DOE OSTI2023

A PEPS Plugin for TNQVM

This work introduces an extension to the Tensor Network Quantum Virtual Machine (TNQVM) tool, enhancing the existing stack of ExaScale Tensor Network (ExaTN), ExaScale Accelerator (XACC), and TNQVM. It features a new plugin that enables efficient simulation of a Projected Entangled Pair State (PEPS), a 2D tensor network. To improve simulation efficiency for PEPS, we have implemented the snake boundary contraction algorithm. By integrating this capability into the existing stack, we enhance the overall functionality and versatility of the framework. We tested this new PEPS topology for a simple GHZ bell-pair generation quantum circuit and saw that its runtime is very close to that of the MPS topology. We estimate that the real potential of the PEPS topology becomes discernible when quantum circuits with multidimensional entanglement are simulated using tensor networks. In such cases, 1D tensor networks fail to represent or contract them efficiently.

Chundury, Srikar↗

DOE OSTI2022

Electrifying High-Efficiency Future Communities: Impact on Energy, Emissions, and Grid

To combat climate change and meet decarbonization goals, the building sector is improving energy efficiency and electrifying end uses to reduce carbon emissions from fossil fuels. All-electric buildings are becoming a trend among new constructions, introducing opportunities for decarbonization but also technical challenges and research gaps. For instance, further investigation is needed to understand how the adoption of energy efficiency measures (EEMs) and distributed energy resources (DERs) in all-electric communities would affect energy consumption, carbon emissions, and grid planning. This paper presents a case study of a mixed-use, all-electric community located in Denver, Colorado. We use URBANopt TM , a physics-based urban energy modeling platform to model the community and then evaluate the impact of EEMs and DERs (i.e., photovoltaics [PV], electric vehicles [EVs], and batteries) on the community's energy usage, carbon emissions, and peak demand. The results show that adding EEMs and PV led to both energy consumption and carbon emissions reductions across all building types. However, we saw fairly limited impact of EEMs and PV on buildings' peak demand in our case. Additionally, due to overnight EV charging activities and higher grid carbon intensity at night, the carbon emissions in multifamily buildings have a noticeable increase compared to scenarios without vehicles. Finally, the addition of batteries helped reduce peak demand by 11%-29%. The modeling workflow and evaluation methods can be applied to similar communities to evaluate their performance and the effect of integrating EEMs and DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions

Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H 2 ) storage likewise holds strong potential, though with distinct challenges and mechanisms. H 2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H 2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H 2 storage systems by optimizing H 2 adsorption at the nanoscale and improving the kinetics of H 2 uptake and release. We discuss various H 2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H 2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H 2 storage, particularly in prototype vehicles, from a techno-economic perspective. In conclusion, this forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand.

25 ENERGY STORAGE↗

DOE OSTI2020

GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models

In a previous work, embedded ensemble propagation was proposed to improve the efficiency of sampling-based uncertainty quantification methods of computational models on emerging computational architectures. It consists of simultaneously evaluating the model for a subset of samples together, instead of evaluating them individually. A first approach introduced to solve parametric linear systems with ensemble propagation is ensemble reduction. In Krylov methods for example, this reduction consists in coupling the samples together using an inner product that sums the sample contributions. Ensemble reduction has the advantages of being able to use optimized implementations of BLAS functions and having a stopping criterion which involves only one scalar. However, the reduction potentially decreases the rate of convergence due to the gathering of the spectra of the samples. In this paper, we investigate a second approach: ensemble propagation without ensemble reduction in the case of GMRES. This second approach solves each sample simultaneously but independently to improve the convergence compared to ensemble reduction. This raises two new issues which are solved in this paper: the fact that optimized implementations of BLAS functions cannot be used anymore and that ensemble divergence, whereby individual samples within an ensemble must follow different code execution paths, can occur. We tackle those issues by implementing a high-performing ensemble GEMV and by using masks. The proposed ensemble GEMV leads to a similar cost per GMRES iteration for both approaches, i.e. with and without reduction. For illustration, we study the performances of the new linear solver in the context of a mesh tying problem. Furthermore, this example demonstrates improved ensemble propagation speed-up without reduction.

BLAS↗

DOE OSTI2024

Hole-scavenging in photo-driven N 2 reduction catalyzed by a CdS-nitrogenase MoFe protein biohybrid system

The light-driven reduction of dinitrogen (N 2 ) to ammonia (NH 3 ) catalyzed by a cadmium sulfide (CdS) nanocrystal-nitrogenase MoFe protein biohybrid is dependent on a range of different factors, including an appropriate hole-scavenging sacrificial electron donor (SED). Here, the impact of different SEDs on the overall rate of N 2 reduction catalyzed by a CdS quantum dot (QD)-MoFe protein system was determined. The selection of SED was guided by several goals: (i) molecules with standard reduction potentials sufficient to reduce the oxidized CdS QD, (ii) molecules that do not absorb the excitation wavelength of the CdS QD, and (iii) molecules that could be readily reduced by sustainable processes. Earlier studies utilized buffer molecules or ascorbic acid as the SED. The effectiveness of ascorbic acid as SED was compared to dithionite (DT), triethanolamine (TEOA), and hydroquinone (HQ) across a range of concentrations in supporting N 2 reduction to NH 3 in a CdS QD-MoFe protein photocatalytic system. It was found that TEOA supported N 2 reduction rates comparable to those observed for dithionite and ascorbic acid. HQ was found to support significantly higher rates of N 2 reduction compared to the other SEDs at a concentration of 50 mM. A comparison of the rates of N 2 reduction by the biohybrid complex to the standard reduction potential (E o ) of the SEDs reveals that E o is not the only factor impacting the efficiency of hole-scavenging. These findings reveal the importance of the SED properties for improving the efficiency of hole-scavenging in the light-driven N 2 reduction reaction catalyzed by a CdS QD–MoFe protein hybrid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗

DOE OSTI2025

Intermolecular Interactions in Direct Air Capture Materials: Insights from Charge Density Analysis

Direct air capture (DAC) materials enable the removal of CO 2 from the atmosphere, but improving their efficiency requires a detailed understanding of the intermolecular interactions that govern CO 2 sorption and release. Here, we present an experimental electron density study of methylglyoxal-bis(iminoguanidine) (MGBIG), a promising DAC material, using high-resolution X-ray and neutron diffraction data combined with quantum crystallographic analysis. This approach bridges theoretical and experimental data by quantifying electron density distributions and revealing how hydrogen bonds stabilize CO 2 -derived carbonate phases and may influence the desorption behavior. We identify distinct hydrogen-bonding environments in two crystalline carbonate phases: P1, a transient kinetic product, and P3, a thermodynamically stable phase. Multipolar refinement and electrostatic potential and multipole moment calculations precisely map electron density distributions, revealing key hydrogen bonds involved in CO 2 capture. Topological analysis of electron density highlights a cooperative hydrogen-bonding network in the thermodynamically favored P3 phase, where enhanced electron density delocalization and water-mediated interactions contribute to a more stable lattice. Energetic analyses confirm that stronger hydrogen bonding networks enhance the stability of P3 with a binding energy of −607.0 kJ/mol and greater lattice stability (−847.3 kJ/mol) compared to P1 (−302.5 and −571.0 kJ/mol, respectively). Electrostatic potential maps further illustrate polarization patterns that may influence the stability of the binding of CO 2 and release conditions. These findings establish a direct experimental framework for linking electron density distributions to intermolecular interactions in DAC materials, providing a rational design strategy for optimizing sorbents with improved CO 2 capture efficiency and reduced energy demands.

Electron density↗

DOE OSTI2020

Construction-Scale Concrete Additive Manufacturing and its Application in Infrastructure Energy Storage

Construction is filled with labor intensive, hazardous, and often wasteful processes. It is also an enormous industry, so improvements in efficiency could have a tremendous economic impact. Construction-scale additive manufacturing is one path toward achieving those improvements. In this paper, a construction-scale additive manufacturing system, called Sky-BAAM, is presented. In addition to possibly leading to more energy-efficient construction practices, leveraging additive manufacturing in construction opens the solution space to more energy efficient building design. One such design, the EMPOWER wall, is also presented in this paper. The exterior of the wall is shaped to maximize heat transfer, while acting as form work for an internal energy-storage system. This allows energy to be stored in the wall during off-peak times and retrieved during peak periods.

Atkins, Celeste↗

DOE OSTI2021

Upgrading of C1 Building Blocks

This project is developing the centerpiece technology for a market-responsive, integrated biorefinery concept based on the conversion of renewable C1 intermediates (e.g., syngas, CO2, methanol) to a suite of fuels and co-products with improved carbon efficiency, reduced capital expense, and control of the product distribution to meet market demand. Advanced upgrading technologies of syngas are critically needed for the successful commercialization of fuel production at a scale relevant for biomass gasification. Research tasks within this project leverage complementary catalyst and process design for the conversion of CO2-rich syngas (15-20% CO2 in syngas) to achieve high carbon yields of gasoline and jet fuels as the major products. The conversion pathways generate high quality fuels (e.g., high octane gasoline with low aromatics, desirable jet-range hydrocarbons), and potential to achieve favorable cost targets by 2022. Research progress is compared against the Mobil Olefin to Gasoline and Distillate (MOGD) process, which also offers control over the gasoline and distillate products, as an industrial benchmark. The pathway for direct conversion of CO2-rich syngas to hydrocarbon fuels seeks to exceed the carbon efficiency of biomass-sourced MOGD (31.8%). Recent catalyst and process development achievements are highlighted by improvements in carbon-selectivity to fuels and carbon yields, along with evidence of incorporation of carbon from CO2 into the hydrocarbon products.

bioenergy↗

DOE OSTI2022

Transforming Energy Through Computational Excellence: High-Performance Computing for Energy Innovation

The challenges associated with energy efficiency of manufacturing and advanced materials often cannot be addressed through experimentation alone, whether because of scale, complexity, or practicality. High-performance computing (HPC) enables fast tackling of these challenges in the manufacturing sector - vital to achieving net-zero carbon emissions by 2050. The National Renewable Energy Laboratory (NREL) and industry partners leverage HPC to apply advanced modeling, simulation, and data analysis to improve manufacturing efficiency, explore new materials for energy applications, and develop technologies to manage carbon across the life cycle. From improving additive manufacturing processes to increasing the energy efficiency of jet-engine components, advanced computing can help manage emissions produced by manufacturing in a wide variety of ways.

advanced materials↗

DOE OSTI2021

Supply analysis of preferential market incentive for energy crops

This analysis explores the valuation of feedstock quality attributes of switchgrass and miscanthus – two energy crops poised for future expansion – and compares the relative economic availability of these two crops under two scenarios: (i) uniform price assumptions (i.e., no incentive for quality), and (ii) a scenario of a price premium based on convertibility (i.e., an incentive for quality). Given data on cellulose content, hemicellulose content, and their relative convertibility, miscanthus is expected to be 11% more efficient at conversion to biofuels than switchgrass under the biochemical conversion route. Based on this scenario of improved conversion efficiency and associated profit, we simulate an 11% price premium for miscanthus over other feedstocks in a base-case scenario. By adding this price premium, supplies of miscanthus increase over the base case by about 4 million (44%), 94 million (64%), and 166 million (94%) tons in year 0, 10, and 20 after simulated contracts for production are initiated respectively. These results emphasize that custom simulations are needed to quantify feedstock availability if supplies are intended to reflect grower response to industry demands for feedstock quality specifications. Farmers can grow ‘peas or carrots’, and price signals from biorefineries will influence what energy crops they produce. Recognizing that the energy crop mix is tractable according to quality characteristics is relevant both for near-term and long-term biofuels research and development. Here, we recommend accounting for market preferences for quality attributes when estimating potential future supplies of energy crops.

09 BIOMASS FUELS↗

DOE OSTI2022

A framework to quantify mass flow and assess food loss and waste in the US food supply chain

Abstract Reducing food loss and waste can improve the efficiency of food supply chains and provide food security. Here we estimate mass flow as well as food loss and waste along the US food supply chain for 10 commodity groups and nine management pathways to provide a baseline for designing efficient strategies to reduce, recycle, and recover food loss and waste. We estimate a total food loss and waste of 335.4 million metric tonnes from the U.S. food supply chain in 2016. Water evaporation (19%), recycling (55%), and landfill, incineration, or wastewater treatment (23%) accounted for most of the loss and waste. The consumption stage accounted for 57% of the food loss and waste disposed of through landfill, incineration, or wastewater treatment. Manufacturing was the largest contributor to food loss and waste (61%) but had a high recycling rate. High demand, perishable products accounted for 67% of food waste. We suggest that funding for infrastructure and incentives for earlier food donation can promote efficiency and sustainability of the supply chain, promote FLW collection and recycling along the U.S. FSC, and improve consumer education in order to move towards a circular economy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Natural Gas Combined Cycle Power Plants with Carbon Capture and Exhaust Gas Recycle

This is a presentation given at the 3rd International Conference on Energy and Environment. The presentation highlights analysis work conducted by NETL to assess the cost and performance for natural gas combined cycle (NGCC) power plants that incorporate solvent-based post combustion carbon dioxide (CO2) capture and exhaust gas recycle (EGR). Increasing the inlet CO2 concentration by EGR is expected to improve the efficiency of the capture system and reduce its cost, however the analysis results show that the cost improvement is minimal as is the cost of capture reduction.

Hackett, Gregory↗

DOE OSTI2024

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗