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The 2026 AMMT program review meeting will cover work completed to-date in the 2026 fiscal year. This overview presentation introduces the neutron irradiation and corrosion work done under the environmental effects area
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.
This presentation is focused on Team Dynamics and Development in Chemical Emergency Response. While regulatory requirements focus on defining the performance requirements associated with this category of response team, there is very little information and guidance provided to team leaders on the criticality of team dynamics of development in performance. These elements are essential to achieving successfully integrated team operations. NREL has worked to expand chemical response team participation to include operational personnel from research, support operations, and various technical areas. Since this team expansion has encompassed integration from numerous levels and areas within the organization, a systematic approach was adopted to better understand how organization culture, dynamics, and diversity can be used to be more effective. Benchmarking other organizations and reviewing relevant literature assisted in laying the foundation to develop a strategy that incorporates team development principles, dynamics, and leadership practices to increase the effectiveness of emergency response operations. This presentation focuses on the valuable lessons learned and the team-based approaches that can be used to develop a successful response team.
High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.
The goal of this project was to work with rural electric cooperatives to facilitate the diffusion of solar energy adoption in households located in the rural and semi-urban areas of Virginia by identifying social and behavioral factors that might be unique to rural regions; and develop a model to calculate the solar adoption propensity score for household based on their demographics, social and behavioral characteristics which would provide an objective metric to cooperatives that can be further used to do targeted marketing of rooftop solar panels. This was achieved through the following tasks: (1) Conducted a survey of the members of Virginia electric cooperatives to identify demographic, social, financial and behavioral attributes of individuals who are likely to adopt rooftop solar panels. (2) Developed a highly detailed, data-driven, agent-based model of the population of Virginia, focusing on the rural regions. (3) Developed diffusion models that use social, behavioral, and demographic factors, and peer effects to study their impact on solar adoption in rural areas. (4) Built a prototype tool based on the diffusion model to help study market segmentation in rural areas and made it available to National Rural Electric Cooperative Association (NRECA). (5) Results and recommendations derived from the model were provided to NRECA to be shared with participating cooperatives. (6) Results were published in peer reviewed journals, conference proceedings and book chapters, and ideas disseminated through presentations and newsletters. There were several important methodological contributions made under this project which are detailed in the published papers, including: (1) Built a decision-adjusted model for predicting adoptors with imbalanced training data; (2) designed seeding strategies to maximize adoption given a fixed budget; (3) built a methodology to compare different agent based models; (4) created models to identify important factors that influence decision to adopt solar panels; and (5) built a methodology for building household profiles of solar generation to study the duck curve phenomenon. The team included members from the University of Virginia (lead), National Rural Electric Cooperative Association (NRECA), Arizona State University, Virginia Tech and Sandia National Laboratory. Note that no individual entity or stakeholder has incentive to promote solar in rural regions. Most of the research and work focuses around urban regions where the potential for growth in solar adoption is higher due to higher population density. This puts rural areas at a disadvantage. By improving the diffusion of solar adoption in rural parts of the country, we can not only provide clean energy to rural areas but also promote job growth and improves energy independence.
Trisodium bismuthide (Na 3 Bi) is the first experimentally verified topological Dirac semimetal, and is a 3D analogue of graphene hosting relativistic Dirac fermions. Its unconventional momentum-energy relationship is interesting from a fundamental perspective, yielding exciting physical properties such as chiral charge carriers, the chiral anomaly, and weak anti-localization. It also shows promise for realizing topological electronic devices such as topological transistors. Herein, an overview of the substantial progress achieved in the last few years on Na 3 Bi is presented, with a focus on technologically relevant large-area thin films synthesized via molecular beam epitaxy. Key theoretical aspects underpinning the unique electronic properties of Na 3 Bi are introduced. Next, the growth process on different substrates is reviewed. Spectroscopic and microscopic features are illustrated, and an analysis of semiclassical and quantum transport phenomena in different doping regimes is provided. Furthermore, the emergent properties arising from confinement in two dimensions, including thickness-dependent and electric-field-driven topological phase transitions, are addressed, with an outlook toward current challenges and expected future progress.
The development of integrated biorefineries and the greater utilization of biomass resources to reduce dependence on fossil fuel-derived products require research emphasis not just on conversion strategies but also on improving separations associated with biorefining. A significant roadblock towards developing biorefineries is the lack of effective separation techniques evidenced by the relative deficiency of literature in this area. Additionally, high conversion yields may only be realized if effective separations generate feedstock of sufficient purity – this makes research into biomass conversion strategies all the more critical. In this review, the challenges associated with biomass separations are discussed, followed by an overview of the most appropriate separation strategies for processing biomass. One of the unit operations most appealing for biorefining, membrane separations (MS), is then considered along with a review of the recent literature utilizing this technique in biomass processing.
Enzyme engineering and metabolic engineering drive innovation in energy biotechnology. In recent years, artificial intelligence (AI) has supported successful applications in designing effective enzymes and productive microbial cell factories. This review summarizes recent advances in enzyme redesign using protein language models, de novo enzyme design with generative models, and AI tools for engineering metabolism and related cellular phenotypes. Across these areas, AI models are shifting from single modality inputs to integrated representations of protein function, metabolic pathways, and cell states. We emphasize that unifying the diverse data representations across scales will be necessary for advancements in energy biotechnology.
The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure, and function that have important feedbacks to the global climate system. The heterogeneous nature of arctic landscapes creates challenges for monitoring and improving understanding of these ecosystems, as current efforts typically rely on ground, airborne, or satellite-based observations that are limited in space, time, or pixel resolution. The use of remote sensing instruments on small Unoccupied Aerial Systems (UASs) has emerged as an important tool to bridge the gap between detailed, but spatially limited ground-level measurements, and lower resolution, but spatially extensive high-altitude airborne and satellite observations. UASs allow researchers to view, describe and quantify vegetation dynamics at fine spatial scales (1-10 cm) over areas much larger than typical field plots. UASs can be deployed with a high degree of temporal flexibility, enabling observation across diurnal, seasonal, and annual timescales. In this work, we review how established and emerging UAS remote sensing technologies can enhance arctic plant ecological research by quantifying fine-scale vegetation patterns and processes, and by enhancing the ability to link ground-based measurements with broader-scale information obtained from airborne and satellite platforms. Synthesis: Improved ecological understanding and model representation of arctic vegetation is needed to forecast the fate of the Arctic in a rapidly changing climate. Observations from UASs provide an approach to address this need, however, the use of this technology in the Arctic currently remains limited. Here we share recommendations to better enable and encourage the use of UASs to improve the description, scaling, and model representation of arctic vegetation.
Inspired by natural photosynthesis, the mission of the AMPED EFRC was to develop the fundamental molecular basis for solar-driven water oxidation and carbon dioxide reduction catalysis with dye-sensitized photoelectrodes. From 2014-2022, the Center advanced the state-of-the-art in molecule-decorated photoanodes for water oxidation and broke new ground in the development of photocathodes for CO 2 reduction. A collaborative approach brought together experts in inorganic synthesis, organometallic catalysis, interfacial materials synthesis, photoelectrochemistry, and time-resolved dynamics to address by Grand Challenges in the 2007 DOE BESAC report. Since 2009, the UNC EFRC has published 314 peer-reviewed journal articles, and 2 book chapters. More specifically there were 146 peer-reviewed publications,1-146 during the 2014-20 funding cycle, with 80% supported solely by the EFRC, and about half resulting from collaborations among multiple PIs. Three major scientific research areas emerged from AMPED that are described below with details of the most impactful advances for solar energy research.
Grid-scale energy storage has been identified by the U.S. Department of Energy’s (DOE) Energy Storage Grand Challenge as a necessary technology to support the continued build-out of intermittent renewable energy resources required to attain a carbon-free energy future. To meet this goal, the 2018 Department of Energy Research and Innovation Act mandated the creation of a comprehensive program to accelerate the development and commercialization of next-generation energy storage technologies. One of numerous energy storage technology options is the storage of excess energy as heated geothermal brine in suitable geologic formations. This concept, known as reservoir thermal energy storage (RTES), geologic thermal energy storage (GeoTES), aquifer thermal energy storage (ATES), etc., relies on the storage of thermal energy in geologic formations for recovery and use in large-scale direct use geothermal (e.g., district heating, industrial processes, etc.) and electrical power generation applications. This thermal energy is derived from excess or waste heat from any high-temperature heat source, such as concentrated solar or from conventional thermal/nuclear generation. As such, RTES can potentially play a significant role in meeting the energy storage shortfall in the coming decades. RTES can provide energy arbitrage through both the storage and production of thermal energy stored in geologic formations for direct use applications and can serve as a source of hot fluids that can be used to generate electricity to support peak demand ramping, thus easing stress on transmission and distribution. This energy storage option has geographic benefits in that energy can be stored locally or regionally depending on the various needs/loads. RTES can also be located across an enormous geographic area, without the need for a traditional hydrothermal resource but where thermal gradients and hydrogeology allow economic exploitation of subsurface heat. The work conducted for this project includes (1) a review of lessons learned from past high-temperature RTES international projects; (2) geochemical experimental investigation and numerical simulations of potential domestic sedimentary reservoirs and (3) development of a thermo-hydrological-mechanical (THM) numerical simulation tool for optimizing formation properties and design parameters to maximize thermal energy storage performance.
This project’s objective was to improve the quality of acoustic information about marine energy converters that could be collected from groups of drifting hydrophones, while reducing the costs of deployment and data analysis. This was achieved through technology development addressing four focus areas: (1) minimizing flow-noise and self-noise, (2) integrating metadata streams into a single data acquisition system, (3) developing post-processing routines to facilitate rapid data review, and (4) enabling objective identification of marine energy converter sound against a backdrop of ambient noise using time-delay-of-arrival localization.
Marine energy developers are beginning to rely more heavily on advanced composite materials for their load-bearing structures. Understanding how these materials respond and degrade in harsh marine environments is a complex problem. Much research has been performed at smaller laboratory scales to understand the fundamental physical and chemical degradation mechanisms, but little structural testing research has been performed at larger scales to understand how these physical and chemical interactions scale and how they should be accounted for in the design process. The Advanced Materials project is a multilaboratory research collaboration that addresses these issues with the intention of reducing risks and barriers to marine energy developers wanting to adopt advanced composite materials. The National Laboratory of the Rockies has been playing a leading role in developing larger-scale testing infrastructure and test methods to validate complex numerical degradation models and standardize testing procedures to do so. This paper reviews past structural testing activities, how they have grown into current research activities, and how they will continue to evolve to directly address industry needs. This paper covers key research areas - large-scale water absorption and subsequent structural validation testing, qualification of bolted and adhesive connections, and combined submerged fatigue testing at increasing scales. It provides insights into the complex requirements for testing infrastructure and test method development to effectively qualify materials and certify marine energy structures for harsh marine environments. This research program paves the way for better guidance and standardization within the industry when adopting advanced composite materials.
Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.
Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.
In this session at the SETO Peer Review, several solar cybersecurity projects will be discussed. INL will provide an overview of the multi-lab Securing Solar for the Grid (S2G) project. We will highlight the focus areas of standards development and best practices, cybersecurity tool kits and supply chain analysis, and education and workforce development.
Cellulose nanomaterials (CNMs) naturally exist in plant biomass. The success of extraction of CNMs opened up a new era of using plant biomass for innovative industrial applications. Because CNMs are abundant, renewable, biodegradable, transparent, light weight and low in cost, they are ideal materials for large volume applications such as packaging, automotive, building and infrastructure. In many potential application areas, CNM-enable products appear in a composite form, mostly polymer composites. Additionally, the industrial-scale manufacturing of CNM/thermoplastic composites remains as a set of unsolved problems for academia and industry. A prime challenge in applications is the nanoscale dispersion of CNMs in thermoplastic matrices during melt processing. Both bench-scale and pilot-scale studies have been conducted to solve the dispersion issue of CNMs. In this article, research related to the dispersion of CNMs in thermoplastic matrices during melt processing were critically reviewed. All research papers were classified into three groups: chemically-aided dispersion, physically-aided dispersion and mechanically-aided dispersion. Numerous factors affect the CNM dispersion and the mechanical performance of its nanocomposites. There are material-related factors, including CNM types and forms, polymer matrices, surface modification, coupling agents, etc. Furthermore, extrusion processing parameters also play a significant role, covering screw rotation speed, extrusion barrel temperature settings and screw design. In addition, the material-related factors interact with the processing-related factors. Understanding all factors and their interactions are important for moving CNM nanocomposites research a step further towards industrial-relevant production, which is the final ambitious goal of this manuscript.
In this article, federal offshore waters in the Gulf of Mexico are of interest for large-scale geologic carbon storage (GCS). However, more than 80,000 offshore oil and gas wells exist in the region, which could impact the integrity of sealing intervals. In this study, we propose a screening methodology for ranking offshore legacy wells based on the challenge they may present to GCS. The methodology relies on the review of well regulatory records to 1) identify leakage pathways and assess the potential hazards that wells pose to planned GCS operations, 2) evaluate well features that impact the accessibility of wells to determine the feasibility of potential corrective actions, and 3) rank wells based on the overall challenge they may pose for GCS. We demonstrate our framework by evaluating the construction and abandonment of 156 wells across eight areas of interest (AOIs) in shallow federal waters along the Texas Gulf Coast. The majority (99.3 %) of wells considered were constructed and plugged in a manner that did not isolate prospective GCS targets in the Upper and Lower Miocene formations and may potentially require a challenging or uncertain corrective action prior to GCS. Dataset trends suggest that the observed well construction and plugging designs may be common in shallow offshore federal waters along the Texas Gulf Coast. Consequently, operators pursuing offshore GCS projects in the region may consider selecting areas that avoid challenging wells or performing robust evaluations of legacy well leakage risks to plan corrective action prior to CO 2 injection.