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Conference paper titled, Comparing microclimate weather conditions from the weather research and forecasting (WRF) model with typical meteorological year (TMY) data for building energy simulations accepted for Building Simulation 2025 Conference in Brisbane, Australia in Aug 2025.
Wayne State University (WSU) researchers, in collaboration with the University of California-Los Angeles (UCLA) and Los Alamos National Laboratory (LANL), have successfully developed a new sorbent media used in a process that concentrated the rare earth content in a coal fly ash sample from southeast MI by >350×, resulting in >13 wt% REO powder. The project developed a new DTPA-associated media that was used to concentrate total REEs. This project demonstrated a coupled hydrothermal extraction and solid-liquid recovery process that produced REO powder from raw fly ash while nearly eliminating the use of organic solvents. The project successfully: 1) identified a commercially viable ash supply with >$2.5 M in REO value being deposited monthly, 2) hydrothermally extracted >77% of REEs in coal fly ash (20% more than acid alone), 3) developed a new sorbent media that was used to concentrate REEs, 4) and engineered a process that resulted in a >13 wt% REO powder. The media also demonstrates a selectivity for mid and heavy REEs over Ce and La and the potential for use in separating individual rare earth elements. An invention disclosure for the sorbent media has been filed with WSU and a patent application will be submitted in the near future. The project examined the potential to couple hydrothermal leaching of coal fly ash with the engineering of a custom ligand-associated media to provide an organic solvent-free method of extracting and recovering REEs. Fly ash feedstock was collected from Detroit area coal-fired power plants, characterized for REE content, and subjected to a bench-top batch hydrothermal leaching/dissolution process inside custom-build pressure reactors at Los Alamos National Laboratory (LANL). This process applied a dissolution technique previously developed to process spent nuclear fuel and other nuclear materials. The modified ash was acidified and the resulting leachate was enriched with REEs along with Ca, Al, and Si. Ligands known to be effective in lanthanide/actinide separation were evaluated based on a series of criteria such as selectivity and association to organosilica through hydrophobicity. Batch experiments were conducted to establish the best conditions (e.g., pH, eluent, etc.) and best ligand systems for the effective extraction of REEs from the alkaline feed solution obtained from the hydrothermal leaching process. Once the organosilica-ligand extraction media was developed for REE concentration, an acidic solution was used to back extract and concentrate the REEs into a heavily REE-laden strip solution. The REEs in the strip solution were precipitated with oxalate to produce rare earth oxides.
U.S. Department of Energy's National Renewable Energy Laboratory (NREL) hosts one of the world's most energy-efficient HPC data centers; this system uses component-level warm-water liquid cooling to efficiently remove heat from the data center and capture it for reuse in the building or rejection to the atmosphere. Given the complexity of this system, building data-driven tools for holistically monitoring and operating the entire data center is a priority for ensuring maximal efficiency and resiliency. In this advanced smart facility, over one million metrics are recorded per minute using state-of-the-art streaming data architecture and software to capture and process the state of the system in real time. Here we detail two efforts to effectively analyze, visualize, and interpret this large volume streaming data. We have developed a novel, flexible system for identifying and visualizing individual metric anomalies and component performance across the data center through automatic metadata extraction and physically-motivated visualization for quick interpretation. Additionally, to directly connect system maintenance to data stream processing we explore a physics informed multi-metric drift and anomaly detection application to detect scale-build up in heat exchangers.
This report describes our progress in developing a scalable wireless metering system that integrates plug-load energy meters that measure real, reactive, and apparent power along with load monitoring based on extracting high-fidelity electrical waveform features collected at every load to capture power profiles and automatically identify and categorize MELs, and make this technology available to the research, commercial, and government sectors.
Refined bio-crude production from hydrothermal liquefaction of algae holds the potential to replace fossil-based conventional liquid fuels. The microalgae act as natural carbon sequestrators by consuming CO2. However, this absorbed CO2 is released to the atmosphere during the combustion of the bio-crude. Thus, the life-cycle greenhouse gas (GHG) emissions of refined bio-crude are linked to the production and supply of the materials involved and the process energy demands. One prominent raw material is CO2, which is the main source of carbon for algae and the subsequent products. The emissions associated with the supply of CO2 can have a considerable impact on the sustainability of the algae-based refined bio-crude production process. Furthermore, the diurnal algae growth cycle complicates the CO2 supply scenarios. Traditionally, studies have relied on CO2 supplied from existing power plants. However, there is potential for building natural gas or biomass-based power plants with the primary aim of supplying CO2 to the biorefinery. Alternately, a direct air capture (DAC) process can extract CO2 directly from the air. The life-cycle GHG emissions associated with the production of refined bio-crude through hydrothermal liquefaction of algae are presented in this study. Different CO2 supply scenarios, including existing fossil fuel power plants and purpose-built CO2 sources, are compared. The integration of the CO2 sources with the algal biorefinery is also presented. The CO2 supply from biomass-based power plants has the highest potential for GHG reduction, with a GHG footprint of −57 g CO2 eq./MJ refined bio-crude. The CO2 supply from the DAC process has a GHG footprint of 49 CO2 eq./MJ refined bio-crude, which is very similar to the scenario that considers the supply of CO2 from an existing conventional natural gas-based plant and takes credit for the carbon utilization.
Plant bioengineering has generated tens of thousands of genotype-to-phenotype relationships, but this knowledge remains fragmented across narrative literature and difficult to use computationally. Inconsistent descriptions of DNA constructs, host species, and traits, including variable species names, omitted regulatory elements, and inconsistent gene symbols, impede data reuse, comparative analysis, and design-build-test-learn cycles. Here, we present the Plant Bioengineering Atlas, a literature-mined, ontology-grounded knowledge base assembled using an artificial intelligence (AI)-aided extraction pipeline. A large language model parsed open-access primary research articles to generate structured, provenance-anchored records of engineered genes, modification types, promoter-gene-terminator constructs, host species, target traits, and reported phenotypes, with every record traceable to its source. The current release contains 14,358 curated records encompassing 6,998 distinct genes across 436 plant species from 6,452 papers published between 2000 and 2026. Corpus analysis reveals that experiments are concentrated in a small group of model and crop species, disease and pathogen resistance is the most frequently engineered trait class, and constitutive regulatory parts (particularly the CaMV 35S promoter and NOS terminator) remain pervasive. Two in five records omit one or both flanking regulatory elements (i.e., promoter and terminator), while only 23.4% describe cassettes in which both elements resolve to named part classes, exposing a systematic reproducibility gap. We organize these data into a knowledge graph linking genes, constructs, species, and traits; provide access through an interactive web portal; and propose an AI-compatible documentation standard for AI-ready reporting. The Plant Bioengineering Atlas provides a foundation for data-driven hypothesis generation and AI-aided plant biodesign.
Archives of remote sensing (RS) data are increasing swiftly as new sensing modalities with enhanced spatiotemporal resolution become operational. While promising new breakthroughs, the sheer volume of RS archives stretches the limits of human analysts and existing AI tools, as most models are: i) limited to single data modalities; ii) task-specific; iii) heavily reliant on labeled data. The emerging Foundation Models (FMs) have the potential to address these limitations. Trained on vast unlabeled datasets through self-supervised learning, FMs enable generic feature extraction that facilitate specialization to a wide variety of downstream tasks. This paper describes a vision towards an FM for multimodal Earth Observation data (FM4EO), discussing key building blocks and open challenges. We put particular emphasis on multimodal reasoning, a topic underexplored in EO. Our ultimate goal is a practical path toward FM4EO with capacity to unlock breakthroughs in few-shot learning scenarios, multimodal geographic knowledge integration, synthesis, and hypothesis generation.
Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.
Formulation of targets and establishing which factors in different contexts will achieve these targets are critical to successful decarbonization of the building sector. To contribute to this, we have performed an evidence map of roadmaps for zero and low energy and carbon buildings (ZLECB) worldwide, including a list and classification of documents in an on-line geographical map, a description of gaps, and a narrative review of the knowledge gluts. We have retrieved 1219 scientific documents from Scopus, extracted metadata from 274 documents, and identified 117 roadmaps, policies or plans from 27 countries worldwide. We find that there is a coverage bias towards more developed regions. The identified scientific studies are mostly recommendations to policy makers, different types of case studies, and demonstration projects. The geographical inequalities found in the coverage of the scientific literature are even more extreme in the coverage of the roadmaps. These underexplored world regions represent an area for further investigation and increased research/policy attention. Our review of the more substantial amount of literature and roadmaps for developed regions shows differences in target metrics and enforcement mechanisms but that all regions dedicate some efforts at national and local levels. Roadmaps generally focus more on new and public buildings than existing buildings, despite the fact that the latter are naturally larger in number and total floor area, and perform less energy efficiently. A combination of efficiency, technical upgrades, and renewable generation is generally proposed in the roadmaps, with behavioral measures only reflected in the use of information and communication technologies, and minimal focus being placed on lifecycle perspectives. We conclude that insufficient progress is being made in the implementation of ZLECB. More work is needed to couple the existing climate goals, with realistic, enforceable policies to make the carbon savings a reality for different contexts and stakeholders worldwide.
FAIR to WISE (F2W) is an iterative, large-language model (LLM) driven pipeline that turns unstructured research PDFs into structured, queryable knowledge graphs (KGs). Core features include schema-driven extraction to a LinkML model; full provenance capture; ontology-grounded enrichment (e.g., chemical validation and ChEBI lookup); graph construction to JSON-LD with stable IDs; and KG-RAG question answering with evidence-aware retrieval. The system is engineered for reproducibility and accessibility (open-source Ollama models, temperature=0, NVTX/Nsight profiling) with robust QA (relation verification, deduplication, and deterministic outputs). Primary uses are literature-to-KG automation, knowledge-grounded Q&A, and experimental steering support. We demonstrate the approach in organic photovoltaics, where the pipeline ingests papers, builds a domain KG, and evaluates answers against expert competency questions to guide experimental planning and interpretation. Compared with off-the-shelf LLMs and ad-hoc NLP tools, F2W addresses ontology gaps and reduces hallucination risk by grounding responses in extracted evidence and enforcing schema constraints; it also offers deterministic, provenance-linked outputs and open, cost-aware deployment. Evidence-aware ranking further improves answer quality over pure vector search.
We present an analytic reconstruction of one-loop amplitudes for the process $0\to \overline{q} qt\overline{t}H$. Our calculation is a novel use of analytic reconstruction, retaining explicit covariance in the massive spin states through the massive spinor-helicity formalism. The analytic reconstruction relies on embedding the massive five-point kinematics in a fully massless eight-point phase space while still building a minimal ansatz directly in the five-point phase space. In order to obtain compact analytic expressions it is necessary to identify suitable partial fraction decompositions and extract common numerator factors, which we achieve through careful inspection of limits in which pairs of denominators vanish. We find that the resulting amplitudes are more numerically efficient than ones computed using automatic methods but that the gains are not as significant as in the massless case, at least at present. The method opens the door to applications at two-loop order, where numerical efficiency and improvements in the reconstruction methodology are more crucial, especially with regards to the number of free parameters in the ansatz.
Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.
The Proton Power Upgrade Project at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory will double the proton power capability from 1.4 to 2.8 MW. This will be accomplished through an energy increase from 1.0 to 1.3 GeV and a beam current increase from 26 to 38 mA. The energy increase will be accomplished through the addition of 7 cryomodules to the linear accelerator (Linac). The beam current increase will be supported by upgrading several radio-frequency systems in the normal-conducting section of the Linac. Upgrades to the accumulator ring injection and extraction regions will accommodate the increase in beam energy. A new 2-MW-capable target and supporting systems will be developed and installed. Conventional facility upgrades include build-out of the existing klystron gallery and construction of a tunnel stub to facilitate future beam transport to the second target station. The project received approval to proceed with construction in October 2020. Procurements are in progress, and some installation activities have already occurred. Most of the installation will take place during three outages in 2022-2023. The project early finish is planned for 2025.
A. The Microreactor Applications Research Validation and Evaluation (MARVEL) reactor will offer experimental capabilities that are not currently available at DOE’s national laboratories. Idaho National Laboratory (INL), operated for the U.S. Department of Energy (DOE) by Battelle Energy Alliance, LLC (BEA) (Contractor hereafter) is procuring services for the design, analysis, fabrication, testing and delivery of a Secondary Coolant Equipment system (SCE). This specification contains the requirements for design, analysis, fabrication, testing and delivery of the SCE as described herein. The MARVEL reactor is a microreactor which uses eutectic sodium-potassium alloy (NaK) as a primary coolant. The primary coolant is circulated through four primary loops by natural convection of the coolant. In each loop is a closed well which will accommodate an intermediate heat exchanger (IHX) for extracting heat from the loop. These wells will be referred to in this specification as the “IHX wells.” It is intended for the IHX containment to also be filled with NaK. The MARVEL design team has determined that a Heat Extraction System (HES) using pumped NaK will be used to extract heat from the IHXs and deliver it to a downstream system for power generation or alternate process heat users. This Heat Extraction System will enable MARVEL operations including the ability to test, demonstrate, and address issues related to installation, startup, and operations. In addition, it will allow down-stream utilization of process heat for various uses. The objective of this specification is to develop the final design for the HES Secondary Coolant Equipment system (SCE) that will be used as the core of the HES. This system provides control of the NaK circulation between the MARVEL reactor and the subsequent process heat utilization systems. It does not include design of the Intermediate Heat Exchangers and piping inside the T-REXc pit in which the reactor is located. B. The MARVEL microreactor will be installed in the Transient Reactor Test Facility (TREAT) building in the Transient Reactor Test (TREAT) Micro-Reactor Experiment Cell (T-REXc) C. An INL Subcontractor has developed a conceptual design for this system per SPC-71145, referred to in that specification as the Process Heat Extraction System. SPC-71260 is based on the pumped NaK loop concept developed under SPC-71145. D. The SCE system design and (as option scope) fabrication shall be provided by the awardee of the subcontract (Subcontractor hereafter) pertaining to this Specification. Prior to shipment, the SCE will be fabricated, assembled, and tested at the Subcontractor’s facility. After successful completion of acceptance testing, the SCE and associated equipment will be shipped to the Materials and Fuels Complex (MFC) at the INL (Contractor’s Facility hereafter) to be installed by others in TREAT/T-REXc.
Bioconversion of lignocellulosics to ethanol is significantly hindered by biomass recalcitrance and, therefore, often requires a biomass pretreatment step. Furan-based compounds such as furfural (FF) and 5-hydroxymethylfurfural (HMF) are versatile building blocks for fuels and chemicals. However, their production during pretreatment often suffers from low yield and low separation efficiency. Biphasic solvent systems are capable of reducing biomass recalcitrance and extracting furans into the organic phase, thus preventing their degradation, increasing their yield, and allowing much easier separation. The development of a sustainable biphasic solvent system is essential to the furan-driven biorefinery and has drawn significant attention. Finally, this review systematically summarizes recent advances in the development of biphasic solvent systems in lignocellulosic biorefinery for improving the production of liquid fuels and furan-based compounds.
Renewable polymers have become an important focus in next-generation materials, and algae biomass offers an environmentally low-impact feedstock that can serve multiple uses. This study aims to develop a scalable methodology for production of microalgae-based polyols for polyurethane synthesis from waste oils derived from algae biomass. Following separation of omega-3 fatty acids from algae oil, residual oils can offer valuable building blocks for petrochemical replacements. However, unlike vegetable oils, algae oils contain organic contaminants, including photosynthetic pigments and hydrophobic cofactors that can complicate preparative methodologies. Here we convert and purify waste streams from omega-3 depleted Nannochloropsis salina algae oil, with major components consisting of palmitic and palmitoleic acid, into azelaic acid (AA) as a building block for flexible polyurethanes, with a simultaneous production of heptanoic acid (HA) as a flavor and fragrance precursor. Conversion of free fatty acid mixtures into a soft soap allows extraction of organic contaminants, and urea complexation provides isolated palmitoleic acid, which is subsequently ozonolyzed to produce AA and HA. Bio-based polyester diols are prepared from AA via esterification to provide a polyol monomer for flexible polyurethane foam preparation. The HA co-product is modified to produce the flavoring agent methyl heptanoate and also decarboxylated to produce hexane as a renewable solvent. This scalable process can be performed on oils from multiple algal species, offering valuable monomers from a highly sustainable source.
Borehole thermal energy storage (BTES) is an effective solution for managing imbalanced heating and cooling loads in cold regions. This study evaluated the long-term feasibility of a BTES system in the Fairbanks area, Alaska, through building energy modeling, resource characterization, and numerical modeling. The system was designed to store waste heat from a nearby coal power plant during summer and provide thermal energy during winter to geothermal heat pumps supplying heating loads in two buildings. Heating load profiles were modeled for the buildings using EnergyPlus, and the results indicated the annual heating load was 5.6 times greater than the cooling load. 40 borehole heat exchangers were pre-designed approximately 100 m away from the two buildings in terms of land availability and regulatorily optimized depth. The 20-year performance of the designed BTES system under two operational scenarios—one with a 5-year preheating period and one without—was numerically modeled using subsurface temperature and properties characterized through the literature review and thermal response tests. Both scenarios demonstrated that the BTES has the capacity to fully cover the heating loads in the two buildings throughout the 20-year lifetime. Production temperatures at central wells were 33 % higher on average than at outer wells in both scenarios. The 5-year preheating period increased subsurface and extraction temperatures, and correspondingly annual average and total thermal energy production was higher for 8 years than in the scenario without the preheating period. These results highlight the long-term reliability and sustainability of the BTES system in meeting heating demands over its lifetime, with the preheating period offering potential performance improvements. Implementing the BTES system in cold regions with high heating demand, such as Fairbanks, Alaska, could provide a long-term, sustainable energy solution for managing imbalanced heating and cooling loads.
Abstract Expressing plant metabolic pathways in microbial platforms is an efficient, cost‐effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour‐intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost‐effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data‐driven machine learning framework designed to extract ‘yeastizing’ rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design‐build‐test cycle.