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DOE OSTI2022

Pseudo-Polymorphism in Layered FeS Intercalates: A Competition between Charged and Neutral Guest Species

Systematic synthesis studies of the formation of tetrahedral FeS-ethylenediamine intercalates resulted in the synthesis of a new compound, [Fe 9.4(2) S 10 ][Fe(en) 3 ] 0.6(1) ·en 0.9 ( 3 ). The composition and complex crystal structure were determined based on a synergistic combination of elemental composition, decomposition behavior, high-resolution synchrotron X-ray diffraction and total scattering, 57 Fe Mössbauer spectroscopy, and electron diffraction. The structural model was derived based on a systematic comparison to the previously reported structures [Fe 8 S 10 ][Fe(en) 3 ] 1 ·en 0.5 and tetragonal FeS. The new compound has flat Fe 9.4 S 10 layers, analogous to those in superconducting binary FeS. In the crystal structure of [Fe 9.4 S 10 ][Fe(en) 3 ] 0.6 ·en 0.9 , the interlayer space is occupied by [Fe(en) 3 ] 2+ complexes and neutral ethylenediamine molecules in a ~2:3 ratio. Interlayer species are not randomly oriented but ordered as evidenced by superstructural diffraction peaks in both high-resolution X-ray diffraction and electron diffraction patterns. Magnetic studies reveal no superconducting transition down to 2 K, indicating that the presence of minute amounts (~6%) of iron vacancies at the Fe-S layer in [Fe 9.4 S 10 ][Fe(en) 3 ] 0.6 ·en 0.9 is still sufficient to shift the position of the Fermi level resulting in an adjustment of the properties. Here, our work shows the importance of detailed characterization of the crystal structure of intercalated compounds to understand the origin of the observed properties and develop proper structure–property relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Abstract Terrestrial gross primary production (GPP) represents the magnitude of CO 2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.

Environmental Sciences & Ecology↗

DOE OSTI2013

Multi-transmission-line-beam interactive system

We construct here a Lagrangian field formulation for a system consisting of an electron beam interacting with a slow-wave structure modeled by a possibly non-uniform multiple transmission line (MTL). In the case of a single line we recover the linear model of a traveling wave tube due to J. R. Pierce. Since a properly chosen MTL can approximate a real waveguide structure with any desired accuracy, the proposed model can be used in particular for design optimization. Furthermore, the Lagrangian formulation provides: (i) a clear identification of the mathematical source of amplification, (ii) exact expressions for the conserved energy and its flux distributions obtained from the Noether theorem. In the case of uniform MTLs we carry out an exhaustive analysis of eigenmodes and find sharp conditions on the parameters of the system to provide for amplifying regimes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data. The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made. The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library. Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.

Coal ash composition↗

DOE OSTI2021

Nuclear Energy Model Intercomparison Project

This document summarizes the current status and future plans of the Nuclear Model Intercomparison Project. The objectives of this project are to understand how issues central to nuclear energy are modeled in long-term capacity expansion models, to investigate how model structures and input assumptions impact projections for nuclear’s role, to refine model representations of nuclear energy, and to communicate findings to the research community and decision-makers. High-level goals are discussed for each of the four participating model groups: the U.S. Energy Information Administration (EIA), U.S. Environmental Protection Agency (EPA), Electric Power Research Institute (EPRI), and National Renewable Energy Laboratory (NREL). This document summarizes scenarios and assumptions for the model comparison, outcomes from the first workshop, and next steps.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Nuclear's Role in the U.S. Electricity System: A Multi-Model Inter-Comparison Analysis

A variety of US electric power sector capacity expansion models are used by decision makers and analysts to evaluate competition among generation, transmission, and storage technologies to meeting the demands of the system. CEMs use least-cost optimization to identify optimal portfolios of investments capable of satisfying all specified requirements. While CEMs are a useful tool to inform pathways to meet future needs, projections can differ significantly between tools for apparently similar scenario assumptions. Differences in model structure, scope, and input assumption contribute to this issue. This work compares model response with harmonization between four modeling teams on issues significant to the representation and development of nuclear energy.

capacity expansion model↗

DOE OSTI2023

Insights on Nuclear Energy from Long-Term System Models: Inter-Model Comparison Results

capacity expansion model↗

DOE OSTI2019

Confocal Raman Microscopy Investigation of Self-Assembly of Hybrid Phospholipid Bilayers within Individual Porous Silica Chromatographic Particles

Hybrid-supported phospholipid bilayers are a model structure utilized for measurement of molecular interactions that typically occur at cell membranes. These membrane models are prepared by adsorption of a lipid monolayer onto a stable n -alkyl chain layer that is covalently-bound to a support surface. Hybrid bilayers have been adapted to chromatographic retention measurements of lipophilicity through the assembly of a phospholipid monolayer onto n -alkane-modified silica surfaces in reversed-phase chromatographic particles. Recent Raman microscopy studies of these particles have shown that the acyl chains of the phospholipid interact with the C 18 -alkyl chains immobilized on the silica surface, where both lipid and C 18 alkyl chains become ordered due to chain interdigitation. Confocal Raman microscopy has also been used to investigate the association of small molecules with hybrid-lipid bilayers in C 18 chromatographic silica particles; the partitioning of model solutes compares favorably to that in lipid vesicle membranes with similar changes in acyl-chain structure (disordering) with solute partitioning. The present study seeks information about how these membrane-mimetic bilayers assemble onto the C 18 –derivatized silica surfaces of reversed-phase chromatographic silica particles. Confocal Raman microscopy is capable of interrogating the time-dependent internal composition and structure within individual silica particles. The Raman scattering data can be resolved into component Raman spectra and corresponding composition vectors that describe the time-dependent changes in intensity of the component spectra. This analysis provides insight into how the structures of both the lipid and C 18 alkyl chains of hybrid lipid bilayers evolve during deposition and organization on the internal surfaces of reversed-phase chromatographic silica particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Native Architecture of Wheat Straw Cell Walls: A Unified Model from X-ray Scattering and Solid-State NMR

Plant secondary cell walls constitute the dominant reservoir of renewable biomass, comprising tightly packed cellulose, hemicellulose, and lignin at the nanoscale. Recent advances in solid-state NMR spectroscopy and the availability of small-angle X-ray scattering for biomass characterization have led to an accumulation of experimental data on cell wall organization, yet no explicit structure model has simultaneously satisfied both Xray and NMR observations. Using wheat straw as a model system, we propose a structural framework consistent with current knowledge of cellulose biosynthesis, X-ray scattering data, and one- and two-dimensional 13 C solid-state NMR spectra. In this model, 18-chain elementary fibrils align in parallel and populate the cross-section at random. Arabinose-substituted xylan shows no conformational dependence for cellulose-binding in wheat, and only a minor fraction of 2-fold xylan appears in close proximity to cellulose, unlike in Arabidopsis, where xylan is more tightly attached to the cellulose surface. While NMR data cannot unambiguously resolve the internal arrangement of the 18 glucan chains, X-ray scattering profiles uniquely constrain the fibril size and exclude the possibility of tight bundling in the intact walls. The specific interaction between the matrix polymers and the cellulose elementary fibrils must be reconsidered in light of the small interfibril spaces, which bring the matrix components into spatial proximity with cellulose even in the absence of attractive interactions. These findings provide fundamental molecular-level insight into cellulose fibril architecture and matrix−polymer interactions, resolving longstanding discrepancies between spectroscopic and scattering data and advancing our understanding of biopolymer assembly into structurally and functionally versatile lignocellulosic biomaterials.

Carbohydrates↗

DOE OSTI2015

Multiple trapping on a comb structure as a model of electron transport in disordered nanostructured semiconductors

A model of dispersive transport in disordered nanostructured semiconductors has been proposed taking into account the percolation structure of a sample and joint action of several mechanisms. Topological and energy disorders have been simultaneously taken into account within the multiple trapping model on a comb structure modeling the percolation character of trajectories. The joint action of several mechanisms has been described within random walks with a mixture of waiting time distributions. Integral transport equations with fractional derivatives have been obtained for an arbitrary density of localized states. The kinetics of the transient current has been calculated within the proposed new model in order to analyze time-of-flight experiments for nanostructured semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0–100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. Here, by providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Short-Range Structure of Amorphous Calcium Hydrogen Phosphate

Calcium orthophosphates (CaPs) are the hard constituents of bones and teeth, and thus of ultimate importance to humankind, while amorphous CaPs (ACPs) may play crucial roles in CaP biomineralization. Among the various ACPs with Ca/P atomic ratios between 1.0–1.5, an established structural model exists for basic ACP (Ca/P = 1.5), while those of other ACPs remain unclear. In this work, the structure of amorphous calcium hydrogen phosphate (ACHP; Ca/P = 1.0) obtained via aqueous routes at near-neutral pH values, without stabilizers, was studied by experiments (mainly, TEM with ED, XRD, IR, and NMR spectroscopies, as well as XAS) and computer simulation. Our results globally show that ACHP has a distinct short-range structure, and we propose calcium hydrogen phosphate clusters (CHPCs) as its basic unit. This model is consistent with both computer simulations and the experimental results, where CHPCs are arranged together with water molecules to build up ACHP. We demonstrate that Posner’s clusters, which are conventionally accepted to be the building unit of basic ACPs, do not represent the short-range structure of ACHP, as Posner’s clusters and CHPCs are structurally distinct. This finding is important not only for the determination of the structures of diverse ACPs with varying Ca/P atomic ratios but also for fundamental understanding of a major mineral class that is central to biomineralization in vertebrates and, thus, humans, in particular.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

The Climate Change Impacts and Risk Analysis (CIRA) modeling exercise is a unique contribution to the scientific literature on climate change impacts, economic damages, and risk analysis that brings together multiple, national-scale models of impacts and damages in an integrated and consistent fashion to estimate climate change impacts, damages, and the benefits of greenhouse gas (GHG) mitigation actions in the United States. The CIRA project uses three consistent socioeconomic, emissions, and climate scenarios across all models to estimate the benefits of GHG mitigation policies: a Business As Usual (BAU) and two policy scenarios with radiative forcing (RF) stabilization targets of 4.5 W/m2 and 3.7 W/m2 in 2100. CIRA was also designed to specifically examine the sensitivity of results to uncertainties around climate sensitivity and differences in model structure. The goals of CIRA project are to 1) build a multi-model framework to produce estimates of multiple risks and impacts in the U.S., 2) determine to what degree risks and damages across sectors may be lowered from a BAU to policy scenarios, 3) evaluate key sources of uncertainty along the causal chain, and 4) provide information for multiple audiences and clearly communicate the risks and damages of climate change and the potential benefits of mitigation. This paper describes the motivations, goals, and design of the CIRA modeling exercise and introduces the subsequent papers in this special issue.

Climate Change Impacts↗

DOE OSTI2014

A facile route to synthesize CdZnSe core–shell-like alloyed quantum dots via cation exchange reaction in aqueous system

Highlights: • Water-soluble CdZnSe alloyed QDs synthesized by cation exchange reaction. • The as-prepared CdZnSe QDs have fairly good luminescence properties. • The surface defects of obtained QDs were removed due to the alloyed structure. - Abstract: Water-soluble CdZnSe alloyed nanocrystals have been successfully prepared via “green” cation exchange reaction in aqueous system. The X-ray diffraction (XRD) patterns indicate that the as-prepared nanocrystals had high crystallinity and small particle size of 4–5 nm. The absorption spectra of CdZnSe show red shift of 100 nm from 375 to 475 nm. Moreover, the band-gap photoluminescent (PL) emission has a red shift of 50 nm from 430 to 480 nm with the increase of the reaction time. On the basis of the PL properties and transmission electron microscopy (TEM) images, one kind of core–shell-like structure model was proposed, which resulted from the different cation exchange reaction rates. This structure could greatly improve the luminescence properties by the removal of surface defect of quantum dots. This work would support potential applications in optoelectronic devices, and biomedical tags fields.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Climate impacts in scenarios: time to close the loop?

Reaching a full understanding of the consequences of climate change for society and ecosystems, and the ensuing needs for adaptation, requires a consideration of the interactions between human and Earth systems. Currently, however, climate research largely separates the influence of society on climate from the influence of climate on society; that is, it doesn’t “close the loop.” A primary example of this approach is the generation and use of earth system model (ESM) simulations in the climate change research community. Large-scale socio-economic models, known as integrated assessment models (IAMs), are used to project emissions and land use change which serve as input to ESMs. ESM projections then serve as input to models of impacts on society and ecosystems. But, according to this modeling chain, those impacts do not affect the emissions and land use that drove the ESMs in the first place. Previous work has not drawn firm conclusions on whether this feedback would be large enough to warrant explicitly accounting for it. Two prominent possibilities, however, are that emissions and land use scenarios representing the high and low ends of the plausible range of future climate change are both too extreme. The high-end scenario may miss damaging impacts that would reduce economic activity, and therefore emissions, while the low-end scenario may ignore climate feedbacks that would make large-scale land-based carbon removal ineffective and therefore would hamper mitigation at the level assumed by the scenario. In this piece, we identify the opportunities and challenges that implementing such feedback loops would face. We argue that recent developments in climate impact research, human system modeling and ESM emulation make the time ripe to use IAMs in a structured model intercomparison exercise. Model intercomparison projects have benefitted the climate modeling community for decade snow, and more recently have also benefitted the impact modeling community. An IAM intercomparison focused on integrating impacts could make large strides in testing the implications of these feedbacks and assessing whether closing the loop would fundamentally change our outlook on future climate changes and their consequences.

Tebaldi, Claudia↗

DOE OSTI2024

Packaging “vegetable oils”: Insights into plant lipid droplet proteins

Abstract Plant neutral lipids, also known as “vegetable oils”, are synthesized within the endoplasmic reticulum (ER) membrane and packaged into subcellular compartments called lipid droplets (LDs) for stable storage in the cytoplasm. The biogenesis, modulation, and degradation of cytoplasmic LDs in plant cells are orchestrated by a variety of proteins localized to the ER, LDs, and peroxisomes. Recent studies of these LD-related proteins have greatly advanced our understanding of LDs not only as steady oil depots in seeds but also as dynamic cell organelles involved in numerous physiological processes in different tissues and developmental stages of plants. In the past 2 decades, technology advances in proteomics, transcriptomics, genome sequencing, cellular imaging and protein structural modeling have markedly expanded the inventory of LD-related proteins, provided unprecedented structural and functional insights into the protein machinery modulating LDs in plant cells, and shed new light on the functions of LDs in nonseed plant tissues as well as in unicellular algae. Here, we review critical advances in revealing new LD proteins in various plant tissues, point out structural and mechanistic insights into key proteins in LD biogenesis and dynamic modulation, and discuss future perspectives on bridging our knowledge gaps in plant LD biology.

Cai, Yingqi (ORCID:0000000203575809)↗