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DOE OSTI2020

Developing a sustainability science approach for water systems

We convened a workshop to enable scientists who study water systems from both social science and physical science perspectives to develop a shared language. This shared language is necessary to bridge a divide between these disciplines’ different conceptual frameworks. As a result of this workshop, we argue that we should view socio-hydrological systems as structurally co-constituted of social, engineered, and natural elements and study the “characteristic management challenges” that emerge from this structure and reoccur across time, space, and socioeconomic contexts. This approach is in contrast to theories that view these systems as separately conceptualized natural and social domains connected by bi-directional feedbacks, as is prevalent in much of the water systems research arising from the physical sciences. A focus on emergent characteristic management challenges encourages us to go beyond searching for evidence of feedbacks and instead ask questions such as: What types of innovations have successfully been used to address these challenges? What structural components of the system affect its resilience to hydrological events and through what mechanisms? Are there differences between successful and unsuccessful strategies to solve one of the characteristic management challenges? If so, how are these differences affected by institutional structure and ecological and economic contexts? To answer these questions, social processes must now take center stage in the study and practice of water management. We also argue that water systems are an important class of coupled systems with relevance for sustainability science because they are particularly amenable to the kinds of systematic comparisons that allow knowledge to accumulate. Indeed, the characteristic management challenges we identify are few in number and recur over most of human history and in most geographical locations. This recurrence should allow us to accumulate knowledge to answer the above questions by studying the long historical record of institutional innovations to manage water systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Opportunities for Process Intensification with Membranes to Promote Circular Economy Development for Critical Minerals

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportu-nities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially con-cerning chemical reagent usage. Feed concentrations and product demand variability require flex-ible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.

Dougher, Molly↗

DOE OSTI2024

High‐Rate Polymeric Redox in MXene‐Based Superlattice‐Like Heterostructure for Ammonium Ion Storage

Abstract Achieving both high redox activity and rapid ion transport is a critical and pervasive challenge in electrochemical energy storage applications. This challenge is significantly magnified when using large‐sized charge carriers, such as the sustainable ammonium ion (NH 4 + ). A self‐assembled MXene/n‐type conjugated polyelectrolyte (CPE) superlattice‐like heterostructure that enables redox‐active, fast, and reversible ammonium storage is reported. The superlattice‐like structure persists as the CPE:MXene ratio increases, accompanied by a linear increase in the interlayer spacing of MXene flakes and a greater overlap of CPEs. Concurrently, the redox activity per unit of CPE unexpectedly intensifies, a phenomenon that can be explained by the enhanced de‐solvation of ammonium due to the increased volume of 3 Å‐sized pores, as indicated by molecular dynamic simulations. At the maximum CPE mass loading (MXene:CPE ratio = 2:1), the heterostructure demonstrates the strongest polymeric redox activity with a high ammonium storage capacity of 126.1 C g −1 and a superior rate capability at 10 A g −1 . This work unveils an effective strategy for designing tunable superlattice‐like heterostructures to enhance redox activity and achieve rapid charge transfer for ions beyond lithium.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Plant Reload Optimization (prlo)

The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.

Kim, Junyung [Idaho National Laboratory] (00090005↗

DOE OSTI2025

Stepping into the Midwest Bioeconomy: Stakeholder Engagement and Geospatial Tools to Assist in Perennial Bioenergy Crop Decision Making and Entrepreneurship

This project, “Ecosystem Services and Farm Entrepreneurship Technical Assistance,” was a three-year project originally planned for FY22–FY24. Due to a late start and a few extensions, it is being completed in early FY25. This project explored opportunities to support the deployment of a bioeconomy with a circular, more sustainable supply chain. Using a tool developed by Argonne to identify agricultural areas suitable for use in the bioeconomy, we sought to create opportunities in the bioeconomy as biomass producers, bioenergy users, and environmental entrepreneurs. We proposed to focus at the beginning on enhancing the tool’s capabilities, while engaging with key stakeholders to improve and expand the tool’s functionality for all potential stakeholders in the bioeconomy. We believe that expanding our tools and technologies, coupled with conversations in agricultural spaces, will be needed as we continue to explore how best to offer farmers whole-of supply-chain opportunities to participate in the bioeconomy. Through this project we have continued to gain a better understanding of the ways in which farmers, landowners, bioenergy users, and environmental entrepreneurs may approach the bioeconomy. In addition, as we improve our analytic toolkit, we can continue to refine our communication and the ways in which we can valuate the bioeconomy. Refining these tools allows us to dive deeper into conversations around plausible policies and drivers for future bioeconomy investment and engagement by stakeholders. By working with farmers and agricultural landowners to enable a sustainable bioeconomy business model, enhance their energy options, and recover resources from their waste streams, this project directly responds to the Bioenergy Technology Office’s (BETO) priorities of building a resilient energy economy. It addresses BETO’s focus on fostering the development and adoption of energy technologies that enable the conversion of waste to energy, efficient land use, and robust job creation. By establishing a technical assistance program that develops capabilities and practices in agricultural areas to implement a bioeconomy future, this program will develop an important linkage between technology being developed at U.S. Department of Energy national laboratories and the agricultural communities of the Midwest. This project focuses on farmers with lower productivity farmland. Because less productive lands create a more difficult revenue stream for conventional crops, these farmers may therefore be more open to alternative agricultural land management regimes. Consequently, the technical assistance program and the methodologies for targeting perennial bioenergy crop application on marginal land provide a distinct opportunity to engage with and invest in the bioeconomy in these economically stressed areas. Stakeholders in this project include farmers and landowners, local conservation organizations (NRCS, SWCS, etc.), universities, non-profit environmental and agricultural entities, farm consultants, environmental regulators, and industry, including the industries working on conversion technologies, anaerobic digestion, pyrolysis, and biochar generation, and the companies interested in trading or purchasing/supporting the valuation of ecosystem services (ES).

09 BIOMASS FUELS↗

DOE OSTI2022

Effect of injected flux and current temporal phasing on self-organization in the HIT-SI3 experiment

The HIT-SI3 device at the University of Washington uses three oscillating inductive helicity injectors to form and sustain spheromak plasma equilibria. By adjusting the temporal phase of the injector waveforms with respect to each other, the toroidal spectrum of the imposed perturbations can be controlled. Using a recently implemented GPU-based control system, the available mode spectra were explored experimentally by scanning the space of relative injector phasing. In this space, significant variation in the toroidal mode spectrum ($n$ = 1, 2, 3) of the perturbations was observed. Additionally, variation in characteristics of driven equilibria was also observed, including a ≈ $30$% range in toroidal current gain ($I$ $Φ$ / $I$ $Inj$ ). Experimental results are compared with both a composite-equilibria and nonlinear dynamic model, including extended MHD simulations using the NIMROD code and composite Taylor state equilibria computed using the PSI-Tet code. In conclusion, qualitative agreement is seen with the nonlinear models, but not with composite-equilibria models, suggesting the use of nonlinear models to better capture observed plasma dynamics and provide predictive use for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Differentiable Multiphysics Codes: A Breakthrough Technology for Simulation and Computing

This document summarizes the findings of a strategic planning exercise commissioned by the Weapons Simulation and Computing, Computational Physics (WSC/CP) program at the Lawrence Livermore National Laboratory (LLNL) in FY24. During the year, the committee met with multiple stakeholder communities to gather input, opinions, suggestions and concerns which have been incorporated throughout this document. The key findings from this exercise are summarized: • The development of multiphysics modelling and simulation (mod/sim) codes and software technologies, their deployment on exascale compute platforms, and their broad adoption across the NNSA is a major success of the Advanced Simulation and Computing (ASC) program and the Exascale Computing Project (ECP). Sustained investment in these core technologies is essential. • Today’s state of the art involves running ensembles of O(100K) simulations to perform uncertainty quantification (UQ) and design studies using multiple statistical methods such as Bayesian optimization to understand sensitivities of our models and explore parameterized design spaces. Even with exascale computing, we are practically limited to O(10) parameters in these studies since the number of simulations required to sample the space scales exponentially with the number of design parameters. • The data from these simulation ensembles is increasingly being used to train machine learned (ML) surrogates (or reduced order models, ROMs) which can then be used for optimization or real time design exploration. However, the trained surrogates are still limited in the number of parameters they can represent due to the sampling limitations previously noted. • Augmenting our suite of integrated multiphysics simulation codes, both current and emerging, with the ability to compute gradients (solution derivatives) of arbitrary simulation outputs with respect to (some or all) simulation inputs would be a breakthrough technology, opening the door to a new era of efficient and automated inverse design based on verified and validated mod/sim capabilities. • This capability, which we refer to as differentiable multiphysics codes (DMCs), would revolutionize both UQ and optimization studies by breaking the curse of dimensionality that presently limits our “gradient-free” ensemble based computing approach. A similar breakthrough occurred in the AI/ML community once the ability to compute gradients of arbitrary loss functions using back-propagation became commonplace. Gradient information from the multiphysics codes can also be used to dramatically improve the efficiency and scale of training of ML/ROM surrogates for rapid assessments. • Achieving this in our suite of codes will be a grand challenge, similar to the amount of effort that was required to transition from CPU to GPU computing. It will require buy-in from the entire WSC/CP program and beyond, including all integrated codes, physics and engineering models, third-party library dependencies and performance portability abstractions. It will also require investment in research and development of numerical methods for computing adjoints of coupled physics across multiple adaptively refined moving meshes and of stochastic (Monte Carlo) and mesh free (SPH) methods. • New software and numerical techniques, largely pioneered by the AI/ML community, make this feasible. Chief among these is automatic differentiation (AD), the ability to employ AD at point-wise locations in a physics calculation (instead of traditional black-box approaches) and the ability to perform “back-propagation in time” (or reverse mode AD) for non-linear partial differential equations (PDEs). Fundamentally, the conclusion of this strategic planning exercise is that the time is right to undertake a large scale effort in WSC, centered on the existing integrated codes, to continue the natural evolution of mod/sim in the age of AI/ML. Instead of attempting to replace mod/sim with purely data driven AI/ML models, we believe the key to success is to integrate AI/ML by building on top of the decades of hard-won knowledge and the verified/validated multiphysics modelling capability that is the hallmark of the ASC program.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

ORNL Campus Sustainability and Decarbonization using Waste Heat Recovery from the Oak Ridge Leadership Computing Facility’s High-Performance Computing Data Center

Heat pumps are a clean and efficient technology that can be powered by renewable electricity to transfer heat using a refrigerant from one place to another by different heat sources, making buildings clean and environmentally friendly. With the support of the ORNL Laboratory Modernization Division, this project explored and evaluated an innovative solution that uses water-water cost-effective midtemperature heat pump (MTHP) technology to leverage the low-grade waste heat from ORNL Frontier and the data center to deliver 85°C hot water, which replaces hot steam generated using natural gas combustion boilers for water heating or space heating in the buildings of ORNL campus. Two scenarios were studied. In the first scenario, which considered the 5600-5700-5800 complex only, Carrier’s commercial 1,000 kW MTHP technology achieves more than 6,640 MWh/year energy savings, an emission reduction of 858 TCO2e/year CO2, and a payback time of 4.85 years. In the second scenario, which considered the 5600-5700-5800 complex and Buildings 5100, 5200, and 5300, the CO2 emission reduction is 1,483 TCO2e/year, the operating cost savings are $0.21 million annually, and the payback time is 3.74 years. Additionally, a comprehensive HP ShowCase Tool was developed for evaluating the optimal solution to improve sustainability and decarbonization of the buildings on the ORNL campus. The tool is an Excel-based tool integrated with VBA (Visual Basic for Applications) coding. The tool includes collected ORNL campus building information and an MTHP library, which comprises collected commercial and ORNL-defined MTHPs. The tool was used to evaluate the sustainability and decarbonization of the ORNL campus. The tool can be widely used or referenced for heat pump solutions and building decarbonization renovation strategies to modernize ORNL facilities and energy use–intensive equipment to enable efficient, sustainable, and resilient operations in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Editorial for Special Issue: Grid-Interactive Efficient Buildings - Part 1

We are pleased to present starting with this volume of ASME Journal of Engineering for Sustainable Buildings and Cities (JESBC), a series of peer-reviewed papers related to some of ongoing research projects and applications of Grid-interactive Efficient Buildings (GEBs). As part of a new program initiated by the Building Technology Office (BTO) with the US Department of Energy (DOE), GEBs are characterized as highly energy-efficient buildings that are equipped with smart technologies and distributed energy resources that can be dynamically controlled to meet grid needs and minimize electricity system costs, while meeting occupants’ comfort and productivity requirements. In simple terms,DOE defines GEBs as efficient, connected, smart, and flexible buildings. The GEB technologies touch on the main topical areas of JESBC aimed at fostering sustainable buildings and cities by providing results of evaluation studies and demonstration projects. Indeed, one of the main goals for JESBC is to disseminate solutions to enhance the built environment energy efficiencies, low carbon services, inter-connectivity services, resilient multi-services, and healthy and comfortable living spaces.

buildings↗

DOE OSTI2023

High-Throughput Automated Exploration of Phase Growth Behaviors in Quasi-2D Formamidinium Metal Halide Perovskites

Quasi-2D metal halide perovskites (MHPs) are an emerging material platform for sustainable functional optoelectronics, but the uncontrollable, broad phase distribution remains a critical challenge for applications. Nevertheless, the basic principles for controlling phases in quasi-2D MHPs remain poorly understood, due to the rapid crystallization kinetics during the conventional thin-film fabrication process. In this work, a high-throughput automated synthesis-characterization-analysis workflow is implemented to accelerate material exploration in formamidinium (FA)-based quasi-2D MHP compositional space, revealing the early-stage phase growth behaviors fundamentally determining the phase distributions. Upon comprehensive exploration with varying synthesis conditions including 2D:3D composition ratios, antisolvent injection rates, and temperatures in an automated synthesis-characterization platform, it is observed that the prominent n = 2 2D phase restricts the growth kinetics of 3D-like phases—α-FAPbI 3 MHPs with spacer-coordinated surface—across the MHP compositions. Thermal annealing is a critical step for proper phase growth, although it can lead to the emergence of unwanted local PbI 2 crystallites. Additionally, fundamental insights into the precursor chemistry associated with spacer-solvent interaction determining the quasi-2D MHP morphologies and microstructures are demonstrated. The high-throughput study provides comprehensive insights into the fundamental principles in quasi-2D MHP phase control, enabling new control of the functionalities in complex materials systems for sustainable device applications.

2D perovskites↗

DOE OSTI2025

Fluorinated Rocksalt‐Polyanion Cathode for Lithium‐Ion Batteries

Integrated rocksalt‐polyanion cathodes (DRXPS) are promising candidates for next‐generation lithium‐ion battery cathode materials that combine high energy density, stable cycling performance, and reduced reliance on Co and Ni. In this work, we investigated Li 3 Mn 1.6 P 0.4 O 5.4 F 0.6 , a new DRXPS cathode with fluoride incorporation. A pure spinel phase was formed and a discharge capacity retention of 84% was achieved after 200 cycles between 1.5 and 4.8 V versus Li/Li + . In comparison, the similarly synthesized Li 3 Mn 1.6 Nb 0.4 O 5.4 F 0.6 , in which all P 5+ was substituted by Nb 5+ while maintaining the same stoichiometry for all other elements, crystallized in a disordered rocksalt structure, and exhibited inferior capacity retention and rate capability than the P 5+ counterpart. Our findings expand the compositional space of DRXPS to include F − , justify the viability of integrating polyanion groups in rocksalt‐type cathodes, and highlight the superiority of P 5+ as a cation charge compensator compared to the commonly used Nb 5+ . This work thereby advances the design of robust, high‐performance cathode materials for sustainable batteries.

36 MATERIALS SCIENCE↗

DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.

Science & Technology - Other Topics↗

DOE OSTI2021

Climate reconstructions for the Last Glacial Maximum from a simple cirque glacier in Fiordland, New Zealand

Mountain glacier records offer important constraints on the timing and magnitude of climate variability during the last glacial cycle. Existing moraine chronologies from the central Southern Alps indicate maximum ice extent was achieved during marine isotope stages 3–4, followed by repeated advances of similar, but gradually declining extent during marine isotope stage 2, until onset of the glacial termination. Questions remain over the precise role of climate in driving these changes, as most existing moraine chronologies come from large, complex former valley glacier systems, where non-climatic influences such as changing bed topography and proglacial lake formation may have influenced glacier length changes. In this work, we address this problem via a new cosmogenic 10Be chronology and equilibrium line altitude reconstruction from a cirque glacier situated in Fiordland, New Zealand. Our chronology shows moraine deposition at 32 ± 11 ka, 18.7 ± 0.2 ka, 18.1 ± 0.1 ka, and c. 17.2 ± 0.3 ka. The simple geometry of the former glacier supports the role of climate in driving a net decline of regional ice volume during marine isotope stages 3–2. Close spacing and good preservation of the 19–17 ka moraines permits 2D glacier reconstruction which suggests the equilibrium line altitude remained depressed by c.1130 m (equivalent to 5.8 ± 0.6 °C colder than present) during this interval. Onset of warming after 17.2 ± 0.2 ka is consistent with climate proxy evidence for a sustained southward shift in the southern westerly winds, which may have promoted deglaciation via shifting ocean currents and promoting increases in atmospheric carbon dioxide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Impact of shading following installation of agrivoltaic systems on eco-physiological and biochemical attributes of “Howes” cranberry in Massachusetts

Agrivoltaic (AV) systems, which integrate solar energy generation with crop production on the same piece of land, offer a sustainable solution for optimizing land-use efficiency. However, the impact of shading induced by solar panels on crop growth, physiology, and metabolic activities of cranberry is not known. Understanding these interactions is essential for assessing the feasibility of AV systems in cranberry production. In this study we evaluated eco-physiological and biochemical crop responses on a commercial ‘Howes’ cranberry bog grown under an agrivoltaics system using different row spacings in the year immediately following installation. Following installation of fixed tilt solar panels at a height of 3.0 m above the plant canopy, four shading treatments were evaluated: an uncovered control area and row spacings of 9.0 m, 7.0 m, and 5.0 m, corresponding to 30%, 35%, and 37% shading respectively. The shading effect was also evaluated at three different fruit developmental stages (green, blush, and full-red stage). Using microclimate sensors installed in the treatment areas, key eco-physiological and biochemical parameters, including leaf gas exchange, chlorophyll, anthocyanin, and antioxidant content, electrolyte leakage, proline and trehalose accumulation, were assessed. While moderate shading (30% and 35%) alleviated environmental stress, as reflected by improved leaf water status and reduced electrolyte leakage, these same treatments suppressed key physiological and metabolic functions, including photosynthetic activity, carbon reserve accumulation, and the biosynthesis of stress-related osmolytes and antioxidant compounds. Under higher shading (37%), these reductions were even more pronounced, indicating a potential threshold beyond which the benefits of shading are outweighed by declines in assimilate production and secondary metabolism. These findings highlight a trade-off between the stress-mitigating benefits of shading and the alterations in key physiological and metabolic processes under agrivoltaic systems. Minimizing these trade-offs by optimizing row spacing in agrivoltaics systems is critical to maintain cranberry physiological status. Since commercial cranberry is grown as a continuous plant canopy (no interrow spaces for equipment travel), damage caused by the installation process likely impacted crop response evaluated in the first year; multi-year studies are needed to fully understand the dynamics of a dual-use system. Notably, this work provides the first direct measurements of proline, trehalose content, and electrolyte leakage in cranberry grown under AV conditions, offering new insight into their physiological adjustments to shading.

14 SOLAR ENERGY↗

DOE OSTI2020

Direct measurement of the contribution of street lighting to satellite observations of nighttime light emissions from urban areas

Nighttime light emissions are increasing in most countries worldwide, but which types of lighting are responsible for the increase remains unknown. Also unknown is what fraction of outdoor light emissions and associated energy use are due to public light sources (i.e. streetlights) or various types of private light sources (e.g. advertising). Here we show that it is possible to measure the contribution of street lighting to nighttime satellite imagery using ‘smart city’ lighting infrastructure. The city of Tucson, USA, intentionally altered its streetlight output over 10 days, and we examined the change in emissions observed by satellite. We find that streetlights operated by the city are responsible for only 13% of the total radiance (in the 500–900 nm band) observed from Tucson from space after midnight (95% confidence interval 10–16%). If Tucson did not dim their streetlights after midnight, the contribution would be 18% (95% confidence interval 15–23%). When streetlights operated by other actors are included, the best estimates rise to 16% and 21%, respectively. Existing energy and lighting policy related to the sustainability of outdoor light use has mainly focused on street lighting. These results suggest an urgent need for consideration of other types of light sources in outdoor lighting policy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A unifying equation for fermentation sustainability across the titer-rate-yield landscape

Industrial fermentation is central to the sustainable production of fuels and chemicals, yet commercial viability of emerging technologies hinges on improving fermentation titer, rate, and yield (TRY). How these metrics shape system cost remains difficult to generalize due to complex interactions among feedstocks, fermentation, separations, catalytic upgrading, waste management, and facility design. Here, we systematically map theoretical fermentation performance spaces (formed by all potential TRY combinations) for 32 representative biomanufacturing facilities—spanning distinct choices for feedstocks, fermentation regimes and products, separations, and catalytic upgrading—by simulating and evaluating them (via techno-economic analysis, TEA) under uncertainty (600,000 Monte Carlo simulations) and across TRY combinations (7500 TRY combinations for each of 32 configurations). Across this wide design and thermodynamic simulation space, we find the relationship between fermentation TRY and system cost is captured by a simple, generalizable mathematical equation (R 2 of 0.992 − 1.000 across our simulations; 0.954 − 1.000 when validated against prior studies that used different tools). We use this equation to elucidate key drivers that shape cost sensitivity to fermentation performance, generating widely applicable insights. By demonstrating a unifying relationship governs the impact of fermentation on biomanufacturing economics, this work establishes a foundation for agile, holistically predictive, resource-efficient strategies to prioritize fermentation research and development needs and accelerate commercialization of emerging biomanufacturing technologies.

applied mathematics↗

DOE OSTI2023

Functional Meta-Analysis of the Proteomic Responses of Arabidopsis Seedlings to the Spaceflight Environment Reveals Multi-Dimensional Sources of Variability across Spaceflight Experiments

The human quest for sustainable habitation of extraterrestrial environments necessitates a robust understanding of life’s adaptability to the unique conditions of spaceflight. This study provides a comprehensive proteomic dissection of the Arabidopsis plant’s responses to the spaceflight environment through a meta-analysis of proteomics data from four separate spaceflight experiments conducted on the International Space Station (ISS) in different hardware configurations. Raw proteomics LC/MS spectra were analyzed for differential expression in MaxQuant and Perseus software. The analysis of dissimilarities among the datasets reveals the multidimensional nature of plant proteomic responses to spaceflight, impacted by variables such as spaceflight hardware, seedling age, lighting conditions, and proteomic quantification techniques. By contrasting datasets that varied in light exposure, we elucidated proteins involved in photomorphogenesis and skotomorphogenesis in plant spaceflight responses. Additionally, with data from an onboard 1 g control experiment, we isolated proteins that specifically respond to the microgravity environment and those that respond to other spaceflight conditions. This study identified proteins and associated metabolic pathways that are consistently impacted across the datasets. Notably, these shared proteins were associated with critical metabolic functions, including carbon metabolism, glycolysis, gluconeogenesis, and amino acid biosynthesis, underscoring their potential significance in Arabidopsis’ spaceflight adaptation mechanisms and informing strategies for successful space farming.

59 BASIC BIOLOGICAL SCIENCES↗