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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Overcoming Variability: A Reproducible Approach to SERS Detection of Nanodiamonds

Detonation nanodiamonds (DNDs) are formed at specific pressures and temperatures during explosions. Different explosives produce varied yields of DNDs within their detonation soot, with Composition B producing the highest yield. Raman spectroscopy (RS) is often used for the characterization of sp 2 - and sp 3 -hybridized carbon allotropes in carbonaceous materials because of distinct disorder and graphitic bands. Bulk diamond also gives a distinct Raman peak at 1332 cm –1 . Furthermore, as bulk diamond decreases in size to nanometer-sized species, the peak red-shifts and broadens, becoming increasingly difficult to detect with RS using visible excitations. Therefore, surface-enhanced Raman spectroscopy (SERS) was used to enhance the diamond peak of DNDs, enabling better detection and faster examination of DNDs within detonation soot. Previous literature of the SERS of DNDs delivered inconsistent results in spectral signatures and SERS substrates. Herein, refining of the methodology for the acquisition of SERS spectra of DNDs was achieved. Before any SERS experiments, the DNDs were first characterized with normal Raman (NR) and scanning electron microscopy. Two routes for SERS enhancement were evaluated: colloidal noble metal nanoparticles and evaporated silver films. Silver films produced the best signal enhancement of DNDs with the best signal-to-noise and peak enhancements observed at 20–30 nm thick silver films at 5% (∼300 μW) laser power. Consistent, reproducible SERS spectra were acquired of small aggregates of DNDs down to ∼500 nm. NR and SERS mapping analysis of DNDs before and after evaporation of silver films revealed the improvements in the detection capabilities of SERS compared with NR.

Carbon↗
DOE OSTI2021

Enhancing Top-Down Proteomics of Brain Tissue with FAIMS

Proteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation, however it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, offline fractionation techniques are commonly used to reduce sample complexity, limiting throughput. A higher throughput alternative is online fractionation, such as gas phase high-field asymmetric waveform ion mobility spectrometry (FAIMS). Using a high complexity sample derived from Alzheimer’s disease brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS at -50 compensation voltage (CV) more than doubled the mean number of non-redundant proteoforms observed (1,833 ± 17, n = 3), compared to without (754 ± 35 proteoforms). We also found FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aß) proteoforms, including the aggregation-prone Aß1-42 variant which is strongly linked to Alzheimer’s disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Contextually Supervised Optimization-Based HVAC Load Disaggregation Methodology

This paper presents a novel contextually supervised optimization-based approach for disaggregating heating, ventilation, and air-conditioning (HVAC) loads using smart meter or Supervisory Control and Data Acquisition data. To disaggregate the load into HVAC loads, large and infrequently used loads (LIUL), and base loads, we formulate an optimization problem to minimize a set of five loss terms, consisting of the reconstruction errors of the overall load profile, the ramp rate losses, and three distinct loss functions linked with the HVAC load, base load, and LIUL, respectively. To enhance accuracy, we incorporate two forms of contextual information into the problem formulation. First, we utilize mutual information to estimate HVAC energy consumption. Second, we employ a base load dictionary to constrain HVAC load estimation errors. The obtained HVAC load profiles are fine-tuned by abnormal ramp detection followed by binary hypothesis testing. Here, the proposed method is developed and tested using sub-metered residential and commercial building data. Simulation results show that the proposed method outperforms existing methods across various data resolutions and load aggregation levels, showing excellent transferability and generalizability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

CHARMM-GUI Bicelle Builder : An Extension of Membrane Builder for Modeling and Simulation of Bicelle Systems

Membrane mimetics, such as detergent micelles, nanodiscs, and amphipol complexes, which can provide membrane-like environments while retaining small and soluble features, have been utilized to study membrane proteins. A bicelle, composed of varying lipids and detergents, is a useful membrane mimetic because the lipid-to-detergent ratio, the q-value, can be adjusted to alter the properties of the aggregate, including the thickness and size of the bicelle. However, building a bicelle model for modeling and simulation studies requires nontrivial efforts, even for experts. We introduce CHARMM-GUI Bicelle Builder, a web-based platform that can generate various all-atom bicelle systems via a graphical user interface with all available lipids and detergents in Membrane Builder. To illustrate and validate Bicelle Builder with practical systems, we have modeled and simulated pure bicelles consisting of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids with 1,2-dihexanoyl-sn-glycero-3-phosphocholine (C6DHPC) detergents and protein–bicelle complexes, composed of DMPC with C6DHPC, foscholine-10 (FOS10), and lysophosphatidylcholine-12 (LPC12) detergents. Our simulation results indicate that Bicelle Builder can generate reliable and robust bicelle models with and without proteins that retain DMPC bilayer characteristics. Bicelle Builder is expected to help researchers better understand not only bicelles themselves but also atomistic-level structures of protein–bicelle complexes that are often difficult to access through experimental approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2020

Deconstruction of biomass enabled by local demixing of cosolvents at cellulose and lignin surfaces

A particularly promising approach to deconstructing and fractionating lignocellulosic biomass to produce green renewable fuels and high-value chemicals pretreats the biomass with organic solvents in aqueous solution. Here, neutron scattering and molecular-dynamics simulations reveal the temperature-dependent morphological changes in poplar wood biomass during tetrahydrofuran (THF):water pretreatment and provide a mechanism by which the solvent components drive efficient biomass breakdown. Whereas lignin dissociates over a wide temperature range (>25 °C) cellulose disruption occurs only above 150 °C. Neutron scattering with contrast variation provides direct evidence for the formation of THF-rich nanoclusters (R g ∼ 0.5 nm) on the nonpolar cellulose surfaces and on hydrophobic lignin, and equivalent water-rich nanoclusters on polar cellulose surfaces. The disassembly of the amphiphilic biomass is thus enabled through the local demixing of highly functional cosolvents, THF and water, which preferentially solvate specific biomass surfaces so as to match the local solute polarity. A multiscale description of the efficiency of THF:water pretreatment is provided: matching polarity at the atomic scale prevents lignin aggregation and disrupts cellulose, leading to improvements in deconstruction at the macroscopic scale.

09 BIOMASS FUELS↗
DOE OSTI2021

Kinetically Controlled Formation of Semi-crystalline Conjugated Polymer Nanostructures

Conjugated polymer (CP) materials that are considered as an attractive choice for various electronic and optoelectronic applications possess highly heterogeneous complex structures that can span several orders of magnitude in length scale. This is due to the intricate coupling effects of weak secondary interactions and entropic forces that determine molecular organization in bulk polymer materials. Understanding the influence of molecular interactions on the emergence and evolution of nano- and microscale structures is of paramount importance to the guided design and development of condensed CP materials. Such understanding is even more critical for the rational design of hierarchical systems away from equilibrium, where weak molecular interactions can guide the system along competing kinetic pathways toward local energy minima and metastable architectures. In this work, we studied a promising concept for accessing various kinetically stabilized semi-crystalline CP nanostructures that are formed in the process of controlled chain-growth Kumada catalyst-transfer polymerization initiated by a small-molecule catalytic species which can exist in equilibrium between free and aggregated states. Specifically, we chose a small-molecule perylenedicarboximide (PDCI) polymerization catalytic initiator which has a strong tendency toward reversible supramolecular assembly–disassembly that can be controlled by temperature. Addition of a thiophene monomer to a solution of the PDCI initiator starts the chain-growth polymerization process, which produces distinct nanoscale semi-crystalline polythiophene structures formed under predominant kinetic control. Furthermore, we demonstrated that, depending on the reaction temperature (which affects the fine balance between the position of PDCI assembly–disassembly equilibrium, rate of polymerization, and solvent–solute interactions for the growing polythiophene chains), a range of kinetically trapped hierarchically organized semi-crystalline CP systems with substantially varying optoelectronic properties could be obtained. In addition to spectroscopic and electron microscopic studies, in order to better reveal the structural aspects of the generated polymer nanoscale systems, we carried out a series of X-ray diffraction and neutron scattering experiments which indicated complex hierarchical organization in these kinetically stabilized CP assembled materials. We expect that this in situ polymerization-based approach can lead to a general way to expand access to various semi-crystalline CP nanostructured materials with broadly tunable electronic and optical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2020

Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)

Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.

25 ENERGY STORAGE↗
DOE OSTI2024

Lipid membrane remodeling and metabolic response during isobutanol and ethanol exposure in Zymomonas mobilis

Abstract Background Recent engineering efforts have targeted the ethanologenic bacterium Zymomonas mobilis for isobutanol production. However, significant hurdles remain due this organism’s vulnerability to isobutanol toxicity, adversely affecting its growth and productivity. The limited understanding of the physiological impacts of isobutanol on Z. mobilis constrains our ability to overcome these production barriers. Results We utilized a systems-level approach comprising LC–MS/MS-based lipidomics, metabolomics, and shotgun proteomics, to investigate how exposure to ethanol and isobutanol impact the lipid membrane composition and overall physiology of Z. mobilis . Our analysis revealed significant and distinct alterations in membrane phospholipid and fatty acid composition resulting from ethanol and isobutanol exposure. Notably, ethanol exposure increased membrane cyclopropane fatty acid content and expression of cyclopropane fatty acid (CFA) synthase. Surprisingly, isobutanol decreased cyclopropane fatty acid content despite robust upregulation of CFA synthase. Overexpression of the native Z. mobilis’ CFA synthase increased cyclopropane fatty acid content in all phospholipid classes and was associated with a significant improvement in growth rates in the presence of added ethanol and isobutanol. Heterologous expression of CFA synthase from Clostridium acetobutylicum resulted in a near complete replacement of unsaturated fatty acids with cyclopropane fatty acids, affecting all lipid classes. However, this did not translate to improved growth rates under isobutanol exposure. Correlating with its greater susceptibility to isobutanol, Z. mobilis exhibited more pronounced alterations in its proteome, metabolome, and overall cell morphology—including cell swelling and formation of intracellular protein aggregates —when exposed to isobutanol compared to ethanol. Isobutanol triggered a broad stress response marked by the upregulation of heat shock proteins, efflux transporters, DNA repair systems, and the downregulation of cell motility proteins. Isobutanol also elicited widespread dysregulation of Z. mobilis ’ primary metabolism evidenced by increased levels of nucleotide degradation intermediates and the depletion of biosynthetic and glycolytic intermediates. Conclusions This study provides a comprehensive, systems-level evaluation of the impact of ethanol and isobutanol exposure on the lipid membrane composition and overall physiology of Z. mobilis . These findings will guide engineering of Z. mobilis towards the creation of isobutanol-tolerant strains that can serve as robust platforms for the industrial production of isobutanol from lignocellulosic sugars.

09 BIOMASS FUELS↗
DOE OSTI2024

Targeted Rare Earth Element Extraction from Mine Drainage Treatment Solids Informed by Advanced Characterization

In support of a clean energy transition in the U.S., National Energy Technology Laboratory (NETL) has collaborated with staff at Hedin Environmental and students at the University of Pittsburgh to characterize critical mineral content and recovery potential from acid mine drainage treatment solids (AMD solids). AMD solids in Appalachia are an unconventional feedstock of rare earth elements (REEs), with potential of suppling 1,102 tons REE/year. To inform recovery efforts, select AMD solids were examined using synchrotron microprobe analysis in conjunction with USGS-developed geochemical modeling to indicate likely phases hosting critical minerals (REE, Co, Ni, etc.) and associated metals . More than 100 AMD solids were collected from 94 passive AMD treatment systems in Pennsylvania, where limestone aggregates are used for acidity neutralization. As pH increases, dissolved metals and critical minerals in AMD are attenuated as surface coatings on limestone. The collected AMD solids contained up to 2000 mg/kg REE, up to 13,000 mg/kg transition metals (Co, Ni, Zn) and up to 440 mg/kg Li. Regardless of the diverse chemical compositions from AMD solids (Al-rich, Mn-rich, or Al,Fe,Mn-rich), REEs were mostly associated with Al and Mn (hydr)oxides, while select heavy REEs (e.g., Gd, Dy) were co-localized with Fe (hydr)oxides. Co and Ni have different distribution zones, while both co-localized with Mn (hydr)oxides. Based on this characterization, NETL developed a patent-pending innovative step-leaching protocol, “Targeted Rare Earth Extraction (TREE)” to effectively recover up to 90% REE and 60% Co in separate steps. In addition, select post-TREE solid residuals (purified Al oxides, or Mn oxides) can be further developed into functional materials (e.g., lithium and CO2 sorbents) needed for green energy transition and carbon management. This characterization-informed approach as well as TREE processing from AMD solids can be used for other legacy wastes (e.g., coal ash, oil and gas drill cutting, mine tailings), and offers an opportunity to transform waste streams into environmental and economic assets that meet U.S. Department of Energy and U.S. Environmental Protection Agency goals.

characterization and extraction of rare earth elem↗
DOE OSTI2020

Ink-substrate interactions during 3D printing revealed by time-resolved coherent X-ray scattering

Additive printing techniques are regarded as revolutionary and versatile methods of advanced device manufacturing, stemming from the possibility to pattern materials on a custom-based approach and the potential to create novel microstructures and achieve new functionalities. Despite these advantages, the inherent anisotropy of the printing process is a source of property gradients within the printed materials, often associated with variable and/or poor performance. Up to date, the evolutionary pathways associated with printing have largely remained unaddressed, mainly owing to the difficulty to study the transformations induced in the material during processing. Time-resolved coherent X-ray scattering techniques, such as X-ray photon correlation spectroscopy, enable the in situ study of transient nanoscale and mesoscale states in a large variety of materials, including amorphous ones, by directly accessing the most relevant timescales and length scales of their nanoscale and mesoscale dynamics, self-assembly, and mesostructure evolution. In this work, we conduct in operando studies of continuous-flow direct writing with colloidal inks, focusing on how the ink formulation and ink-substrate interactions affect the processes that determine the macroscopic properties of the printed materials. We find fundamental differences in the ink structural relaxations emerging from the primary colloid properties (monodisperse versus aggregated colloids) and the substrates surface energy and mechanical properties. Our work helps to reveal and quantify the basic science governing the evolution of 3D-printed materials during processing, ultimately improving engineering criteria for the design of printable materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Carbon dioxide pipeline network transportation cost model: evaluating economic and geographic factors for efficient carbon capture, storage, and utilization

This study presents a comprehensive pipeline network modeling framework to estimate the CO 2 delivery cost for CO 2 utilization and geologic CO 2 storage across the United States. We developed a Python-based CO 2 pipeline transportation cost model leveraging Argonne National Laboratory’s pipeline engineering expertise and detailed natural gas transmission pipeline cost data across U.S. regions. Using existing road corridors as practical routing guides, the model designs pipeline networks that aggregate CO 2 from one or multiple sources and deliver it to selected destinations. It then minimizes the total transportation cost by optimizing pipeline diameters and incorporating booster pumps. A key contribution is the incorporation of up-to-date, region-specific cost factors with itemized components for materials, labor, miscellaneous construction expenses, and right-of-way acquisition. Results emphasize that regional variation and economies of scale associated with CO 2 pipeline costs are significant and should be explicitly accounted for in screening and planning studies. By combining realistic routing constraints with regionalized cost inputs, the model provides transparent design methodology and location-specific insights into source–destination delivery costs, including the effects of routing complexity along existing road networks. We demonstrate the model with two illustrative case studies – one for CO 2 storage and one for CO 2 utilization – in which the model designs pipeline networks spanning hundreds of miles across the states, collecting CO 2 from multiple sources and delivering it to designated endpoints while minimizing levelized cost of delivery via diameter and compression optimization. The model offers a practical, scalable approach for alternative design option screening and early-stage CO 2 transportation planning.

CCS↗
DOE OSTI2021

Cybersecurity Guide for Distributed Wind

Distributed wind sits at the intersection of grid-connected, off-grid and behind-the-meter cyber-physical electrical energy systems. The unique physical properties and communications requirements for distributed wind systems mean that there are unique cybersecurity considerations, but there is little to no existing guidance on best practices for cybersecurity. This document is intended to be a starting point for distributed wind stakeholders including manufacturers, installers and integrators, and operators (facility, aggregator, or utility). We discuss common distributed wind architectures and describe their role in the larger power system, pointing out some of the key connections to be aware of. Cybersecurity cannot exist in a vacuum, but rather must consider all the system and all its connections holistically. The role of distributed wind and the functions it can serve are described to gain understanding of the full range of capabilities. The purpose and application of relevant standards that may apply to certain distributed wind systems is presented. These standards may not apply to all installations, but even for systems that are not required to meet these standards they can be a good reference for best practices. A holistic threat perspective is used to describe the adversaries, threats, and potential impacts of cyberattacks, with special emphasis on what sets distributed wind systems apart from other distributed energy resources (DER). Finally, we present the recommendations for cybersecurity, both in terms of needs of the system and roles that specific stakeholders should fulfill. Distributed wind systems can come in a variety of architectures and applications, so there is no one-size-fits-all approach to cybersecurity. However, this document contains the relevant information for stakeholders to identify the cybersecurity needs of their system, refer to relevant standards, and apply best practices in a manner most consistent with their security and operational goals.

17 WIND ENERGY↗
DOE OSTI2022

A round-robin approach provides a detailed assessment of biomolecular small-angle scattering data reproducibility and yields consensus curves for benchmarking

Through an expansive international effort that involved data collection on 12 small-angle X-ray scattering (SAXS) and four small-angle neutron scattering (SANS) instruments, 171 SAXS and 76 SANS measurements for five proteins (ribonuclease A, lysozyme, xylanase, urate oxidase and xylose isomerase) were acquired. From these data, the solvent-subtracted protein scattering profiles were shown to be reproducible, with the caveat that an additive constant adjustment was required to account for small errors in solvent subtraction. Further, the major features of the obtained consensus SAXS data over the q measurement range 0–1 Å −1 are consistent with theoretical prediction. The inherently lower statistical precision for SANS limited the reliably measured q -range to <0.5 Å −1 , but within the limits of experimental uncertainties the major features of the consensus SANS data were also consistent with prediction for all five proteins measured in H 2 O and in D 2 O. Thus, a foundation set of consensus SAS profiles has been obtained for benchmarking scattering-profile prediction from atomic coordinates. Additionally, two sets of SAXS data measured at different facilities to q > 2.2 Å −1 showed good mutual agreement, affirming that this region has interpretable features for structural modelling. SAS measurements with inline size-exclusion chromatography (SEC) proved to be generally superior for eliminating sample heterogeneity, but with unavoidable sample dilution during column elution, while batch SAS data collected at higher concentrations and for longer times provided superior statistical precision. Careful merging of data measured using inline SEC and batch modes, or low- and high-concentration data from batch measurements, was successful in eliminating small amounts of aggregate or interparticle interference from the scattering while providing improved statistical precision overall for the benchmarking data set.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Twisted A-D-A Type Acceptors with Thermally-Activated Delayed Crystallization Behavior for Efficient Nonfullerene Organic Solar Cells

We report that molecular aggregation and crystallization during film coating play a crucial role in the realization of high-performing organic photovoltaics. Strong intermolecular interactions and high solid-state crystallinity are beneficial for charge transport. However, fast crystallization during thin-film drying often limits the formation of the finely phase-separated morphology required for efficient charge generation. Herein, the authors show that twisted acceptor-donor-acceptor (A-D-A) type compounds, containing an indacenodithiophene (IDT) electron-rich core and two naphthalenediimide (NDI) electron-poor units, leads to formation of mostly amorphous phases in the as-cast film, which can be readily converted into more crystalline domains by means of thermal annealing. This design strategy solves the aforementioned conundrum, leading to an optimal morphology in terms of reduced donor/acceptor domain-separation sizes (ca. 13 nm) and increased packing order. Solar cells based on these acceptors with a PBDB-T polymer donor show a power conversion efficiency over 10% and stable morphology, which results from the combined properties of desirable excited-state dynamics, high charge mobility, and optimal aggregation/crystallization characteristics. These results demonstrate that the twisted A-D-A motif featuring thermally-induced crystallization behavior is indeed a promising alternative design approach toward more morphologically robust materials for efficient organic photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2022

Application of Manufacturing Quality Management Principles to PV System Installations

To help SETO/DOE achieve its goals, the IBTS team proposed a project addressing system reliability by improving installation standards and quality management. The proposed approach was designed to help achieve measurable reductions in installation defect density and improvements in the performance of PV systems by optimizing design and installation of residential and commercial PV systems. This approach addressed the soft costs associated with installations and quality management. The project demonstrated improved system reliability and reduced PV system installation costs. The software developed improved operations, decreased risk, and increased the overall value of PV systems across their lifecycle. The project used several data collection methods, including extensive industry surveys, face-to-face high-level interviews at industry conferences, stakeholder teleconferences, and in-depth interviews conducted by IBTS staff. Results from the research found the industry needs a uniform assessment method for national providers to be more efficient; the software should support both code officials and installers; most industry stakeholders would find value in a centralized software system that allows them to collect, report, and review information on in-process and completed solar installations; and mobile solutions that bridge existing knowledge gaps with inspectors and integrate with existing methodologies (such as permitting software) are of great value. The software solution developed is web-based, allowing for national access, and is built on a Google Firebase platform that can handle significant users and data. It can be used onsite or remotely, allowing for code compliance to continue despite ongoing pandemic related delays or shutdowns for local economies. The information provided by the software tool allows users to uniformly assess a system for compliance and use that aggregated data to identify training topics or create internal process designed to improving issues and reducing occurrence. This solution has multiple benefits in managing quality at time of use and promoting an increase in future safety and quality through education. Perhaps most importantly, this software increases public safety by ensuring compliance of installed systems and allows for local AHJs to remotely engage specialized and qualified solar specific expertise for oversite of the installation in their jurisdictions. Data analysis provides the quality feedback loop identifying the root cause of failure and drives installation practices to improve through training and education, resulting in systems with higher performance, greater reliability, and reduced operations and maintenance costs. With the successful completion of this project, the industry can expect reduced soft costs and increased performance and safety and will ultimately benefit from longer performing systems that cost less to operate.

14 SOLAR ENERGY↗
DOE OSTI2023

Evidence for Lignin–Carbohydrate Complexes from Studies of Transgenic Switchgrass and a Model Lignin–Pectin Composite

Lignin–carbohydrate complexes (LCCs) form through interactions of lignin with plant cell wall polysaccharides and are thought to be a significant source of biomass recalcitrance. In this work, we investigated LCCs formed between lignin and pectin homogalacturonan (HG). The structural changes in HG deficient transgenic switchgrass (GAUT4-knockdown, GAUT4-KD) after hot water pretreatment were compared to wild-type plants using small-angle neutron scattering (SANS), which showed that there were ~2.2-fold more lignin aggregates in GAUT4-KD biomass compared to the wild type. This demonstrated that decreased pectin resulted in more lignin redistribution and suggested that interactions between lignin and HG restrict lignin mobility in plant cell walls. To better understand the types of interactions between lignin and pectin, a model composite was prepared by polymerizing either protiated or partially deuterated coniferyl alcohol to form a dehydrogenation polymer (DHP) in the presence of HG. Small-angle X-ray scattering (SAXS) showed that the DHP and HG form a highly interconnected network structure that is not observed in a physical mixture of the individual polymers. Contrast matching SANS revealed the structure of DHP and HG in the composite and showed that the HG forms a swollen interconnected polymer network (power-law exponent, P = 1.5) interspersed with DHP particles (radius of gyration, Rg, 264 Å) that are composed of solvent-accessible DHP polymers (P = 2.3). Fourier transform infrared spectroscopy showed a unique ester absorption band in the DHP/HG composites. Solid-state nuclear magnetic resonance (NMR) analysis also supports interactions between DHP and HG. Overall, this study provides new insights into the relationship between primary and secondary cell wall polymers during cell wall synthesis and how LCCs formed between pectin and lignin could represent a previously unrecognized source of biomass recalcitrance. This knowledge may help develop new approaches to modulate cell wall properties to improve biofuel and bioproduct production.

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