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At least 145 records · Page 8

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DOE OSTI2026

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Distributed optimization for multi-commodity urban traffic control

A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed- gradient projection algorithm. Further, the intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.

Augmented Lagrangian↗

DOE OSTI2026

Expert evaluation of LLM world models: A high-T c superconductivity case study

Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems, two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Situated Visualization of Photovoltaic Module Performance for Workforce Development

The rapid growth of the solar energy industry requires advanced educational tools to train the next generation of engineers and technicians. We present a novel system for situated visualization of photovoltaic (PV) module performance, leveraging a combination of PV simulation, sun-sky position, and head-mounted augmented reality (AR). Our system is guided by four principles of development: simplicity, adaptability, collaboration, and maintainability, realized in six components. Users interactively manipulate a physical module's orientation and shading referents with immediate feedback on the module's performance.

augmented reality↗

DOE OSTI2024

Situated Visualization of Photovoltaic Module Performance for Workforce Development: Preprint

augmented reality↗

DOE OSTI2024

Reducing AI RAG Hallucination by Optimizing Routing Techniques

Large Language Models (LLMs), such as ChatGPT, tend to “hallucinate”, meaning they confidently generate false information. Retrieval Augmented Generation (RAG) attempts to diminish hallucination by providing context to the LLM from data stores (indexes) containing relevant information. The LLM uses this context to formulate its response. RAG systems can still suffer from hallucination because of bad embeddings or ineffective routing. For example, a router will often return context from an irrelevant index, resulting in a hallucinated answer. In this study, we aim to minimize the frequency of routing hallucinations by optimizing Index Summary Routing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Situated Visualization of Photovoltaic Module Performance for Workforce Development

augmented reality↗

DOE OSTI2025

AI for Interpreting Nuclear Power Plant Documents for Power Uprates

To reduce the cost and time needed for regulatory compliance, nuclear power plants (NPPs) can utilize artificial intelligence (AI) to assist in interpreting complex and voluminous documents that typically span thousands of pages. Usually, the process of interpreting a plant’s technical specifications (TSs) and associated documents is labor intensive. This study aims to understand what processes state-of-the-art large language models (LLMs) can automate and to identify the pitfalls associated with using LLMs to reduce human labor costs and time. This research uses a recent AI technology called retrieval augmented generation (RAG), which retrieves pages of information from TSs and associated documents to assist with NPP power uprates (cleared to produce more power). LLMs are integral to RAG because they create human-like responses based on the retrieved information, aiding in the interpretation and application processes. A baseline case demonstrates how LLMs can operate successfully for a power uprate application. Then five use cases show five types of potential failures: (1) RAG retrieving the incorrect information, (2) RAG misinterpreting the retrieved information, (3) RAG relying on knowledge not contained in the retrieved information, (4) RAG hallucinating, and (5) RAG refusing to answer. The results of the five use cases suggest that automating the human interpretation of TSs and associated documents with AI should be approached with caution. A subject-matter expert reviewed the AI outputs from the five use cases and concluded that an LLM can produce technical information that is needed to produce power uprate applications in certain instances.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

DOE OSTI2025

Ultra‐Flexible Pixelated Perovskite Photodetectors Enabled by Honeycomb Polymer Grids for High‐Resolution Imaging

Abstract A nature‐inspired fabrication method based on a photolithography‐free flexible polymer grid is reported for high‐resolution pixelation of perovskite photodiode arrays with exceptional mechanical ductility and a morphology resembling that of natural compound eyes. The resulting pixelated perovskite photosensitive layer has a ≈1 µm pixel size with 2000 Pixels per inch (PPI) resolution when fully assembled as a photodetector array, delivering a detectivity of >10 13 Jones while providing cross‐talk free imaging. Using a polymer grid effectively releases stress on the perovskite platform, greatly increasing the mechanical agility of the otherwise brittle perovskite film. This novel fabrication methodology and device design offer new possibilities for applications in robotics, biomedical imaging, and virtual and augmented reality.

Zheng, Ding [Department of Chemistry and the Mater↗

DOE OSTI2025

Rational Regulation of Layer‐by‐Layer Processed Active Layer via Trimer‐Induced Pre‐Swelling Strategy for Efficient and Robust Thick‐Film Organic Solar Cells

Thick-film (>300 nm) organic solar cells (OSCs) have garnered intensifying attention due to their compatibility with commercial roll-to-roll printing technology for the large-scale continuous fabrication process. However, due to the uncontrollable donor/acceptor (D/A) arrangement in thick-film condition, the restricted exciton splitting and severe carrier traps significantly impede the photovoltaic performance and operability. For this work, combined with layer-by-layer deposition technology, a twisted 3D star-shaped trimer (BTT-Out) is synthesized to develop a trimer-induced pre-swelling (TIP) strategy, where the BTT-Out is incorporated into the buried D18 donor layer to enable the fabrication of thick-film OSCs. The integrated approach characterizations reveal that the exceptional configuration and spontaneous self-organization behavior of BTT-Out trimer could pre-swell the D18 network to facilitate the acceptor's infiltration and accelerate the formation of D/A interfaces. This enhancement triggers the elevated polarons formation with amplified hole-transfer kinetics, which is essential for the augmented exciton splitting efficiency. Furthermore, the regulated swelling process can initiate the favorable self-assembly of L8-BO acceptors, which would ameliorate carrier transport channels and mitigate carrier traps. As a result, the TIP-modified thin-film OSC devices achieve the champion performance of 20.3% (thin-film) and 18.8% (thick-film) with upgraded stability, among one of the highest performances reported of thick-film OSCs.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Molecularly Imprinted Polymer Sensor Empowered by Bound States in the Continuum for Selective Trace-Detection of TGF-beta

The integration of advanced materials and photonic nanostructures can lead to enhanced biodetection capabilities, crucial in clinical scenarios and point-of-care diagnostics, where simplified strategies are essential. Herein, a molecularly imprinted polymer (MIP) photonic nanostructure is demonstrated, which selectively binding to transforming growth factor-beta (TGF-β), in which the sensing transduction is enhanced by bound states in the continuum (BICs). The MIP operating as a synthetic antibody matrix and coupled with BIC resonance, enhances the optical response to TGF-β at imprinted sites, leading to an augmented detection capability, thoroughly evaluated through spectral shift and optical lever analogue readout. The validation underscores the MIP-BIC sensor capability to detect TGF-β in spiked saliva, achieving a limit of detection of 10 fM and a resolution of 0.5 pM at physiological concentrations, with a precision of two orders of magnitude above discrimination threshold in patients. The MIP tailored selectivity is highlighted by an imprinting factor of 52, showcasing the sensor resistance to interference from other analytes. The MIP-BIC sensor architecture streamlines the detection process eliminating the need for complex sandwich immunoassays and demonstrates the potential for high-precision quantification. This positions the system as a robust tool for biomarker detection, especially in real-world diagnostic scenarios.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

In Situ Exploration of Dipole Field Effects on Weak Hysteresis in 3D/2D Perovskites

This research delves into the effects of 2D layers on the functionality of 3D perovskite using lock-in amplifier-based in situ surface photovoltage (SPV) and its phase spectroscopy, with an emphasis on elucidating the connection between the tuning of dipole moments and the photocurrent hysteresis. Conventionally, the SPV of a perovskite/hole transport layer is observed to diminish as positive bias escalates. However, this trend is reversed in the case of 3D perovskite samples, where an augmentation in SPV is noted under positive bias. Notably, 3D/2D perovskite structures initially show a decrease, then an increase in SPV as bias intensifies, a phenomenon more pronounced with larger dipole moments in 2D. However, there is no linear relationship between the dipole moment and the hysteresis factor. Furthermore, using in situ light-chopping-frequency-modulated SPV and Kelvin Probe Force Microscopy, it is revealed that the dipole fields of 2D layers can hinder ion migration. This leads to efficient hole transfer and minimal photocurrent hysteresis in 3D/2D perovskites, providing strong evidence for the underlying cause of hysteresis. Additionally, these findings suggest intricate interplays among the external electric field, interface dipole moments, and surface photovoltaics, offering significant insights into perovskite optoelectronics.

3D/2D perovskite↗

DOE OSTI2024

Molecular Precision Engineering for Efficient Binary Organic Photovoltaics through Energy Level and Fibrillar Structure Modulation

Adjusting the energy levels and fibrillar morphology is paramount to enhancing the power conversion efficiency (PCE) of organic solar cells (OSCs). In the present study, an increase in the open-circuit voltage (VOC) is facilitated through the elongation of the alkyl chain within AQx (namely AQx-8), aiming to decrease the free volume ratio (FVR). This reduction in FVR attenuates electron-phonon coupling, thereby augmenting emission efficiency and diminishing the non-radiative energy loss (ΔEnr). To further refine the energy levels and morphological characteristics, the external undecyl chain of AQx-8 is substituted with a shorter carbon chain and cyclohexane noted for its considerable steric hindrance (AQx-H). Additionally, this alteration significantly mitigates intermolecular aggregation, expands the bandgap, and elevates the lowest unoccupied molecular orbital (LUMO) energy level, culminating in an elevated VOC of 0.923 V in devices based on AQx-H. Morphological analysis reveals that blends based on AQx-H exhibit an enhanced multi-length-scale fibrillar structure, which is conducive to exciton dissociation and charge transport, thereby contributing to a high fill factor (FF) nearing 80%. Consequently, this study reports one of the highest binary PCEs documented, standing at 19.5% (with certification at 19.0%).

14 SOLAR ENERGY↗

DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Disentangling Acid‐Base Chemistry through Blue Shifting Hydrogen Bond Contributions

The blue shifting of vibrational frequencies in hydrogen bonded molecules, as observed in aqueous environments, has been attributed to local partial charge transfer from solvation. Here, in this study, we extrapolate the blue shift model to the stronger ionic interactions between hydrogen bond acceptors associated with protonation through augmented pH levels and competitive interactions with counter ion pairing. The chemical model we utilize in this work is the aqueous pyridine‐pyridinium equilibrium to characterize the blue shifts observed in the pyridinium chloride ionic system. The observed agreement between observed experimental and calculated spectral shifts shows that the blue shifting model can be extrapolated to stronger interactions and accurately describe the nature of the hydrogen bond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Optimizing Renewable Ammonia Production for a Sustainable Fertilizer Supply Chain Transition

Local renewable ammonia production using electrolytic hydrogen is an emerging approach to alleviate emissions attributed to synthetic nitrogen fertilizer production while also insulating against fluctuations in fertilizer prices and mitigating transportation costs and emissions. However, replacing ammonia currently produced using fossil fuels will not be immediate. To this end, we develop a supply chain transition model, which first optimizes the design and hourly operation of new renewable ammonia facilities to minimize production costs and then optimizes the annual installation timing, production scale, and location of these new renewable facilities along with ammonia transportation to meet county resolution demands. The objective is to augment and eventually replace conventional ammonia market imports in an economically competitive manner. We performed a case study for Minnesota's ammonia supply chain and found that a full transition to in-state renewable production by 2032 is optimal. This is incentivized by the U.S. federal government's clean hydrogen production credits. This transition results in 99 % reduction in carbon intensity along with stable supply costs below $475 per metric tonne. New renewable production facilities are an order of magnitude smaller than existing conventional plants. They use both wind and solar resources and operate dynamically to minimize expensive battery and hydrogen storage capacities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Resist! : Sustaining forest carbon sequestration and wood production after insect disturbance

Disturbances from insect pests threaten ecologically and economically important goods and services supplied by forests, including wood production and carbon sequestration. We highlight the factors that influence these services’ resistance, a term quantifying the initial response to disturbance. Insects inflict damage through a range of mechanisms, prompting distinct plant physiological responses that scale to influence ecosystem processes and, with time, goods and services. The degree and timing of tree mortality and defoliation affect the amount of residual vegetation available to support compensatory wood production and influence carbon sequestration by changing rates of detritus-fueled decomposition. Compounding, or sequential, insect attacks may prime a forest for additional disturbance, further eroding wood production and carbon sequestration. Forest management practices that promote biological and structural diversity, and augment or retain limiting biological and nutrient resources, may buffer against the effects of insect pests on wood production and carbon sequestration.

Gough, Christopher M. [Virginia Commonwealth Univ.↗