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DOE OSTI2026

Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure–strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

4D-STEM↗

DOE OSTI2025

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated $\underline{w}et$ $\underline{c}ollection$ of single microscale tissue voxels and $\underline{S}urfactant$$-assisted$ $\underline{O}ne$-$\underline{P}ot$ voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of a single voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of ~900 and ~4,600 proteins for single voxels at 20 µm × 20 µm × 10 µm (close to single cells) and 200 µm × 200 µm × 10 µm (~100 cells) from fresh frozen human spleen tissue, respectively. 100s-1000s of protein signatures were spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. To evaluate its broad applicability, we applied wcSOP-MS to two commonly accessible, OCT-embedded and FFPE, human archived tissues. It enabled to identify spatially resolved proteome changes and enriched pathways between diseased (breast cancer tumor or AD amyloid plaque) and adjacent normal regions. Antibody-based CODEX and IHC imaging validated label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

2D end-to-end modelling of kilonovae from binary neutron star merger remnants

We investigate the kilonova emission resulting from outflows produced in a 3D general-relativistic magnetohydrodynamic (GRMHD) simulation of a hypermassive neutron star (HMNS) remnant. We map the outflows into the flash hydrodynamics code to model their expansion in axisymmetry, and study the effects of employing different r-process heating rates. Except for the highest heating rate prescription, we find no significant differences with respect to overall ejecta dynamics and morphology compared to the simulation without heating. Once homologous expansion is attained, typically after ~2s for these ejecta, we map the outflows to the sedona radiative transfer code and compute the spectral evolution of the kilonova and broad-band light curves in various Legacy Survey of Space and Time (LSST) bands. The kilonova properties depend on the remnant lifetime, with peak luminosities and peak time-scales increasing for longer lived remnants that produce more massive ejecta. For all models, there is a strong dependence of both the bolometric and broad-band light curves on the viewing angle. While the short-lived (12 ms) remnant produces higher luminosities when viewed from angles closer to the pole, longer lived remnants (240 ms and 2.5 s) are more luminous when viewed from angles closer to the equator. Our results highlight the importance of self-consistent, long-term modelling of merger ejecta, and taking viewing-angle dependence into account when interpreting observed kilonova light curves. We find that magnetized outflows from an HMNS – if it survives long enough – could explain blue kilonovae, such as the blue emission seen in AT2017gfo.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Tunable Growth of Layered Double Hydroxide Nanosheets through Hydrothermal Conversion of Atomic Layer Deposition Seed Layers

To enable the design and manufacturing of hierarchical nanomaterial architectures, there is a need for synthesis and processing methods that can enable tunable geometric control at the nanoscale while maintaining conformality on complex 3-D templates. Here, in this study, we explore the programmable control of vertically oriented Zn-Al layered double hydroxide (LDH) nanosheet arrays using atomic layer deposition (ALD) to deposit a seed layer of Al 2 O 3 , which is subsequently consumed and converted into the LDH phase under hydrothermal growth conditions. We demonstrate tunable control over the spacing and length of the nanosheets by varying the thickness of the initial ALD seed layer with subnanometer precision. This can be viewed as a nanoscale titration reaction, where Al acts as the limiting reagent during the hydrothermal synthesis of the nanosheets. Elemental mapping demonstrates the dynamic evolution of the resulting morphology, which is driven by surface diffusion and nucleation processes. The conformal nature of ALD allows for hierarchical growth of nanosheets on the surface of a variety of nonplanar substrate geometries, including microposts, paper fibers, and porous ceramic supports. This illustrates the power of ALD to enable bottom-up growth of 3-D nanoarchitectures with tunable geometries by controlling nucleation and growth in subsequent solution reactions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Structure-guided utilization of lignocellulose for catalysis, energy, and biomaterials

As a complex composite of cellulose, hemicellulose, and lignin, plant lignocellulose has long served as a major resource for biomass conversion, materials engineering, and bio-based product development. High-resolution structural insights enabled by solid-state nuclear magnetic resonance (ssNMR) now allow the mapping of polymer interfaces, identification of functional group accessibility, and tracking of molecular organization during processing, all of which are critical factors for optimizing catalytic strategies. These insights could drive transformative progress in lignocellulose-based applications, including selective depolymerization, improved pretreatment design, and efficient upcycling of lignin into resins, plastics, and biomedical materials. In industry-relevant contexts, such as biofuel generation and renewable material manufacturing, understanding the hydration dynamics, cross-linking patterns, and structural heterogeneity is also essential. The ability to visualize these features in native biomass presents a unique opportunity to develop new strategies for sustainability and performance. As the structural toolbox continues to expand, it is becoming a central enabler for innovations in renewable energy, green chemistry, and advanced bioproducts.

bioproduct↗

DOE OSTI2024

Non-contact imaging of terahertz surface currents with aperture-type near-field microscopy

Terahertz (THz) near-field imaging and spectroscopy provide valuable insights into the fundamental physical processes occurring in THz resonators and metasurfaces on the subwavelength scale. However, so far, the mapping of THz surface currents has remained outside the scope of THz near-field techniques. In this study, we demonstrate that aperture-type scanning near-field microscopy enables non-contact imaging of THz surface currents in subwavelength resonators. Through extensive near-field mapping of an asymmetric D-split-ring THz resonator and full electromagnetic simulations of the resonator and the probe, we demonstrate the correlation between the measured near-field images and the THz surface currents. The observed current dynamics in the interval of several picoseconds reveal the interplay between several excited modes, including dark modes, whereas broadband THz near-field spectroscopy analysis enables the characterization of electromagnetic resonances defined by the resonator geometry.

Norman, Sarah↗

DOE OSTI2025

Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems

This study proposes a graded structure via additive manufacturing for divertor and first wall blanket applications in fusion reactors. Materials were selected based on thermodynamic calculations to operate from 1100 °C at the plasma-facing level to 550 °C at the structural steel level. Conventional joining methods often lead to failures due to discrete reaction layers with significant mechanical property differences. Using laser beam-directed energy deposition (LB-DED), this study demonstrates the fabrication of a VCrTi-Gr91 steel functionally graded component through a novel process parameter optimization framework. A systematic approach included powder characterization, single-track depositions, and construction of printability maps. Near full-density specimens of each interlayer were additively manufactured, and a transition from V-based alloys to reduced activation ferritic martensitic steels was achieved. Computational material selection of interlayer alloys and thermodynamic/diffusion kinetics simulations prevented most interface incompatibilities. A brittle intermetallic formed at one interface, causing cracking, which was not predicted by current thermodynamic models. Transition alloy design approach was updated with a more recent database and a mitigation strategy has been proposed to eliminate the formation of deleterious intermetallic phases. Ultimately, LB-DED has proven effective for producing multi-material graded systems for fusion applications, with the demonstrated process parameter optimization framework applicable to various materials.

Additive manufacturing↗

DOE OSTI2026

Adaptive X-ray imaging with reinforcement learning

X-ray imaging is a powerful technique to scan samples in a variety of contexts including biological, environmental and materials science, but commonly requires a synchrotron light source to produce X-rays at sufficient intensity. As these facilities are expensive to operate, the available beam time is limited and always in high demand. Particularly if the illuminated samples are sparse, standard raster scanning methods can be time-consuming, with a majority of that time being spent on areas of the image that carry little information. To increase the efficiency and maximize the information gain for a given time budget, we split the scanning process into a series of steps where previous measurements are used to inform the decision making and adapt the exposure distribution at later stages of the sequence. We formulate this task as a reinforcement learning problem where the goal is to produce a sequence of exposure maps that maximize a predefined scalar metric. We demonstrate the potential of this approach in simulations where the adaptive illumination can accelerate the measurement process by up to an order of magnitude compared with standard raster scanning. Finally, we present the first results from deploying the trained agents on an X-ray fluorescence beamline at the Stanford Synchrotron Radiation Lightsource.

Reinforcement Learning↗

DOE OSTI2024

Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems

In previous work, we have introduced a mathematical model for solving a discretized version of the process family design problem. This involves two sets of decision variables. One set selects which unit module designs are included in the process platform out of a candidate set of options; the other set determines which of these unit module designs are assigned to each variant. In this work, we exploit a parallelized Progressive Hedging (PH) algorithm to solve even larger scale design problems. PH is a well-known algorithm traditionally used to solve stochastic programming problems. While our problem is not a two-stage stochastic programming problem, the structure is similar, and it can be directly mapped to the PH approach, which we employ here to solve this deterministic optimization problem. We decompose our problem by process variant. We treat the platform unit module design variables as first-stage and the assignment of unit module designs to variants as second-stage, solving the problem using mpi-sppy. We demonstrate this approach on case studies of CC, water desalination, and refrigeration.

Stinchfield, Georgia↗

DOE OSTI2024

Force-Free Identification of Minimum-Energy Pathways and Transition States for Stochastic Electronic Structure Theories

Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.

Iyer, Gopal R.↗

DOE OSTI2024

Bulk reconstruction and non-isometry in the backwards-forwards holographic black hole map

The backwards-forwards map, introduced as a generalization of the non-isometric holographic maps of the black hole interior of Akers, Engelhardt, Harlow, Penington, and Vardhan to include non-trivial dynamics in the effective description, has two possible formulations differing in when the post-selection is performed. While these two forms are equivalent on the set of dynamically generated states — states formed from unitary time evolution acting on well-defined initial configurations of infalling matter — they differ on the generic set of states necessary to describe the apparent world of the infalling observer. We show that while both versions successfully reproduce the Page curve, the version involving post-selection as the final step, dubbed the backwards-forwards-post-selection (BFP) map, has the desirable properties of being non-isometric but isometric on average and providing state-dependent reconstruction of bulk operators, while the other version does not. Thus the BFP map is a suitable non-isometric code describing the black hole interior including interior interactions.

3-D Image Reconstruction↗

DOE OSTI2025

Molecular Design of Al(II) Intermediates for Small Molecule Activation

Promoting societally important small molecule activation processes with earth-abundant metals is foundational for a sustainable chemistry future. In this context, mapping new reaction pathways that would enable abundant main-group elements to mimic the behaviors of d- and f-block elements is facilitated by exploring unusual oxidation states. The most abundant metal on earth, aluminum, has been well studied in the Lewis acidic +III and Lewis basic +I oxidation states but rarely in the potentially biphilic +II oxidation state until recently, when a renaissance of Al(II) chemistry emerged from a range of research groups. In this Perspective, we review the chemistry of mononuclear Al radicals, including both Al-centered radicals (i.e., Al(II) compounds) and redox non-innocent systems (i.e., formally Al(II) species that are physically Al(III) with ligand-centered radicals), with an emphasis on small molecule reactivity. We also provide a meta-analysis of the Al(II) literature to summarize how different design strategies (e.g., redox non-innocence, strained coordination geometries) have been shown to impart biphilic character to Al radicals and tune their behavior, thus allowing Al radicals to mimic the chemistry of certain d- and f-block metal ions such as Ti(III) and Sm(II). We expect these molecular design concepts to inform future Al(II) studies as the chemistry of this unusual oxidation state of Al continues to grow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Exploring Flood Predictability in Taiwan through Coupled Atmospheric–Hydrological and High-Performance Hydrodynamic Models

Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. Furthermore, we first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting.

Coupled models↗

DOE OSTI2025

As part of its commitment to advancing safety and reliability assessment methodologies, Argonne National Laboratory pioneered the use of an evaluation method called the Success Path Method (SPM) to improve risk management for offshore oil and gas operations. The development of the SPM at Argonne has been driven by the need to improve existing risk assessment methodologies by focusing on the steps necessary for success rather than failure modes alone. This is particularly important for industrial environments like offshore facilities that perform multiple functions under a continuously evolving set of operational conditions – such as water depth and temperature, currents, and weather conditions. In these dynamic environments, the traditional Probabilistic Risk Assessment (PRA) approach is far too complex as it focuses on what can go wrong – which comprises an infinite failure space that must be fully explored and understood. By shifting the focus to a finite space of success paths, the SPM enables operators and decision makers to prioritize a manageable number of steps that must go right to ensure success. Building on its five decades of experience in safety assessments for the nuclear industry, Argonne made major adaptations to existing risk assessment methods utilizing features similar to fault trees that are traditionally used in PRA to map all pathways in which the system can malfunction. In contrast, SPM identifies the components and processes that must function correctly to achieve specific outcomes – such as preventing the uncontrolled release of hydrocarbons during drilling operations. The SPM framework integrates equipment, procedures, software, processes, and human actions to ensure that physical barriers meet critical safety functions in dynamic operational conditions. This approach helps identify failure modes and improve operational risk management by narrowing the focus to key success elements, which in turn reduces uncertainty and helps users understand, manage, and respond to failures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

DOE OSTI2026

Mapping the soil microbiome functions shaping wetland methane emissions

Accounting for only 8% of Earth’s land cover, freshwater wetlands remain the foremost contributors to global methane emissions. Yet the microorganisms and processes underlying methane emissions from wetland soils remain poorly understood. Over a five-year period, we surveyed the microbial membership and in situ methane measurements from over 700 samples in one of the most prolific methane-emitting wetlands in the United States. We constructed a catalog of 2,502 metagenome-assembled genomes (MAGs), with more than half of the 70 bacterial and archaeal phyla sampled containing novel lineages. Integration of these data with 133 soil metatranscriptomes provided a genome-resolved view of the biogeochemical specialization and versatility expressed over wetland soil spatial and temporal gradients. Centimeter-scale depth differences best explained patterns of microbial community structure and transcribed functionalities, even more than land cover or temporal information. Moreover, while extended flooding restructured soil redox, this perturbation failed to reconfigure the transcriptional profiles of methane-cycling microorganisms, contrasting with theoretically expected responses to hydrological perturbations. Co-expression analyses, coupled with depth-resolved methane measurements, revealed the metabolisms and trophic structures most predictive of methane hotspots. Mapping the spatiotemporal transcriptional patterns on this compendium of biogeochemically classified soil-derived genomes begins to untangle the microbial carbon, energy, and nutrient processing contributing to wetland methane production.

MAG↗

DOE OSTI2026

Developing a Process for Collaboration-Based Siting of a Federal Consolidated Interim Storage Facility in the United States: Learning from Communities Living with Legacy Waste

In June of 2023, the U.S. Department of Energy (DOE), Office of Nuclear Energy (NE) announced the selection of its Collaboration-Based Siting (CBS) Consortia, a group of 12 awardees, including the Consortium for Risk Evaluation with Stakeholder Participation (CRESP) (led by Vanderbilt University) to assist DOE-NE with the development of its process for siting a federal consolidated interim storage facility (FCISF) for spent nuclear fuel (SNF) storage. At this time, DOE NE is not soliciting interested host communities, rather the CBS Consortia are tasked with in-depth engagement, mutual learning, and capacity building to provide DOE NE with feedback on the CBS process. CRESP’s objective is to engage communities in two regions (the Pacific Northwest and the Southeast) with sites currently storing defense- and research-related SNF to foster learning concerning the best and worst practices in community participation in risk-informed decision making. This includes learning from existing structures for public input in radioactive waste management decision making [e.g. citizen advisory boards (CABs)] and other local parties about how to build and sustain trust among the parties. CRESP has been working to engage stakeholders and Tribes surrounding two DOE sites historically differing in receptiveness to engaging with DOE and trusting in DOE to accomplish its missions. CRESP’s approach to date has consisted of (1) engaging voluntarily members and former members of the DOE Office of Environmental Management’s CABs to develop a mutual understanding of their perspectives, values, and experiences related to risk and participatory decision making; (2) engaging members of those communities that would likely be part of any radioactive waste management discussions and who may provide valuable feedback for the development of the CBS process; and in the longer term, (3) engaging communities to foster knowledge sharing on understanding and definitions of risk and how risk is factored into community decision making. Our emphasis is to identify opportunities for improving risk communication frameworks, strategies, and decision making. The selection of a future FCISF site is likely to result in the creation of a structure similar to a CAB composed of members of the local community. We anticipate that these insights can help DOE NE learn from existing participatory risk communication structures in place at sites storing defenseor research-related SNF to understand best practices for fostering enduring and participatory relationships with future CABs. Within this paper, we (1) describe CRESP’s overall approach to assisting DOE NE with maturing the CBS process; (2) present a summary of preliminary phase 1 project results, including the development of a body of knowledge (describing available resources to support community engagement, risk communica tion, and participatory decision making) and community ecosystem information (leveraging demographic, social, economic, and environmental attribute mapping and advanced sentiment analysis of social media data); and (3) based on these results, provide observations for future research opportunities to support the development of CBS processes for radioactive waste management facilities, generally.

collaboration-based siting↗

DOE OSTI2026

Visual Systems Mapping to Define and Compare Woody Biomass LCAs for Sustainable Systems

The challenge addressed in this research centres on the need to choose between several biomass sources and energy production processes, while supporting rural economies and resilience of forest systems. A key barrier to effective decision-making for strategies using biomass is the lack of standardized and transparent life cycle assessment (LCA) baselines. These baselines are critical for assessing the impacts of biomass strategies but often vary due to regional factors and chosen simplifying assumptions of the LCAs. However, omitting key variables can mean the LCA omits key feedback and balancing loops relevant to fully assessing impacts of the change or test scenario. To address these complexities, this project employs a systems engineering approach: visual systems mapping. This technique is used to define the boundaries and dynamic behaviours of LCA baselines, enhancing transparency. By examining five literature sources and their documented baseline scenarios, the systems mapping case-studies demonstrates an approach to documenting and archiving these baselines. Recommendations are that visual systems mapping should be used to document key assumptions, such as baselines, of LCAs. Further, where possible open data repositories should hold key information about LCA baselines and reproducible workflows (e.g., using open-source tools) should be used to improve transparency and comparability in LCAs. Given the consensus within the broader scientific community on the importance of replicable data practices, this research reinforces the need for standardized frameworks and systems engineering tools in LCAs. This research demonstrates a pathway to more transparent, standardized, and comparable LCAs, that may bolster decisions for biomass systems.

Davis, Maggie [ORNL] (ORCID:0000000181319328)↗