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At least 217 records · Page 12

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DOE OSTI2025

2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge Study

# 2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge Study The 2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge (CIVIC) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of shared mobility strategies—such as collaborative ride-sharing programs—that could address the mobility needs of rural communities in Puerto Rico. The Civic Innovation Challenge is a multiagency, federal government research and action competition that funds ready-to-implement, research-based pilot projects that have the potential for scalable, sustainable, and transferable impact on community-identified priorities. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 31 participants. ## Survey Records, Data, and Documentation Survey records include 31 participants. The total number of trips was 1,373 and the total non-air-miles traveled was approximately 8,260.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge Study

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge Study

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗

DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Flow and thermal modelling of the argon volume in the DarkSide-20k TPC

The DarkSide-20k dark matter experiment, currently under construction at LNGS, features a dual-phase time projection chamber (TPC) with a ∼ 50 t argon target from an underground well. At this scale, it is crucial to optimise the argon flow pattern for efficient target purification and for fast distribution of internal gaseous calibration sources with lifetimes of the order of hours. To this end, we have performed computational fluid dynamics simulations and heat transfer calculations. The residence time distribution shows that the detector is well-mixed on time-scales of the turnover time (∼ 40 d). Notably, simulations show that despite a two-order-of-magnitude difference between the turnover time and the half-life of 83m Kr of 1.83 h, source atoms have the highest probability to reach the centre of the TPC 13 min after their injection, allowing for a homogeneous distribution before undergoing radioactive decay. We further analyse the thermal aspects of dual-phase operation and define the requirements for the formation of a stable gas pocket on top of the liquid. We find a best-estimate value for the heat transfer rate at the liquid-gas interface of 62 W with an upper limit of 144 W and a minimum gas pocket inlet temperature of 89 K to avoid condensation on the acrylic anode. This study also informs the placement of liquid inlets and outlets in the TPC. The presented techniques are widely applicable to other large-scale, noble-liquid detectors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Unidirectional Sidechain Engineering to Construct Dual‐Asymmetric Acceptors for 19.23 % Efficiency Organic Solar Cells with Low Energy Loss and Efficient Charge Transfer

Abstract Achieving both high open‐circuit voltage ( V oc ) and short‐circuit current density ( J sc ) to boost power‐conversion efficiency (PCE) is a major challenge for organic solar cells (OSCs), wherein high energy loss ( E loss ) and inefficient charge transfer usually take place. Here, three new Y‐series acceptors of mono‐asymmetric asy‐YC11 and dual‐asymmetric bi‐asy‐YC9 and bi‐asy‐YC12 are developed. They share the same asymmetric D 1 AD 2 (D 1 =thieno[3,2‐ b ]thiophene and D 2 =selenopheno[3,2‐ b ]thiophene) fused‐core but have different unidirectional sidechain on D 1 side, allowing fine‐tuned molecular properties, such as intermolecular interaction, packing pattern, and crystallinity. Among the binary blends, the PM6 : bi‐asy‐YC12 one has better morphology with appropriate phase separation and higher order packing than the PM6 : asy‐YC9 and PM6 : bi‐asy‐YC11 ones. Therefore, the PM6 : bi‐asy‐YC12‐based OSCs offer a higher PCE of 17.16 % with both high V oc and J sc , due to the reduced E loss and efficient charge transfer properties. Inspired by the high V oc and strong NIR‐absorption, bi‐asy‐YC12 is introduced into efficient binary PM6 : L8‐BO to construct ternary OSCs. Thanks to the broadened absorption, optimized morphology, and furtherly minimized E loss , the PM6 : L8‐BO : bi‐asy‐YC12‐based OSCs achieve a champion PCE of 19.23 %, which is one of the highest efficiencies among these annealing‐free devices. Our developed unidirectional sidechain engineering for constructing bi‐asymmetric Y‐series acceptors provides an approach to boost PCE of OSCs.

Chemistry↗

DOE OSTI2025

Dissociation of gas-phase anisole induced by low-energy electron interactions: understanding patterns of aromatic bond cleavage

Abstract In view of elucidating the fragmentation patterns of aromatic systems induced by low-energy electron interactions, dissociative electron attachment (DEA) to gas-phase anisole was performed. Anionic fragments resulting from this DEA process were detected by a quadrupole mass spectrometer, and ion yields of those fragments as a function of incident electron energy were rendered. Our study showed the formation of CH 3 − , HCC − , and OCH 3 − fragments, suggesting that various dissociation channels proceed out of DEA to anisole. We employed density functional theory to compute thermodynamic threshold energies for each potential dissociation channel. Those theoretical calculations supported the prediction that the CH 3 − and OCH 3 − fragments form via mechanisms of single-bond cleavage; the HCC − fragments may form through two-, three-, or four-body dissociation channels that entail hydrogen transfers and the cleavage of multiple aromatic bonds. The experimental resonance energies that form the CH 3 − , HCC − , and OCH 3 − fragments were 6.0 eV, 5.8 and 9.7 eV, and 9.8 eV, respectively. Given the classification of anisole as a monosubstituted aromatic species, our results explain generalizable patterns of electron-mediated dissociation in aromatic systems.

Finley, Jacob (ORCID:0009000606963352)↗

DOE OSTI2022

Controlled spalling of (100)-oriented GaAs with a nanoimprint lithography interlayer for thin-film layer transfer without facet formation

Not Available

Braun, Anna K.↗

DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗

DOE OSTI2025

Electron transport across the cell envelope via multiheme c -type cytochromes in Geobacter sulfurreducens

Extracellular electron transfer (EET) enables certain microorganisms to respire using soluble and insoluble extracellular electron acceptors by transporting electrons across the cell envelope. Among these, G. sulfurreducens serves as a model organism for understanding direct EET pathways, where multiheme c-type cytochromes mediate electron transport from intracellular redox carriers to extracellular acceptors such as Fe(III) oxides and electrodes. This review focuses on heme-dependent electron transfer in Geobacter sulfurreducens, detailing the roles of inner membrane cytochromes, periplasmic carriers, outer membrane conduits, and recently characterized extracellular nanowires formed by polymerized multiheme c-type cytochromes, including OmcS, OmcE, and OmcZ. We examine the state of understanding of their physiological function, their structural features, expression patterns, and essentiality under various respiratory conditions. These insights advance our understanding of microbial anaerobic respiration and have implications for biogeochemical cycling, bioenergy generation, and bioremediation. The molecular architecture, assembly mechanisms, and secretion pathways of multiheme c-type cytochrome nanowires remain active areas of investigation, offering promising directions for future research and biotechnological innovation in engineered microbial systems.

Chemistry↗

DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Broadband cross polarization for ultra-wideline magic-angle spinning NMR

Over the past decade, there has been a sustained interest in using frequency-swept (FS) pulses for the efficient acquisition of wideline and ultra-wideline (UW) NMR powder patterns. Such experiments are typically conducted under static conditions, employing both direct- and indirect-excitation methods (i.e., WCPMG and BRAIN-CP/WCPMG, respectively). Recently, Koppe et al. demonstrated that the WCPMG pulse sequence can be used to efficiently acquire wideline and UW NMR spectra with spinning sideband (SSB) manifolds under magic-angle spinning (MAS) conditions, capitalizing on the increased signal-to-noise ratios (SNR) afforded by MAS. To date, there have been only a few instances of broadband cross-polarization (CP) experiments using FS pulses under MAS conditions and no applications to systems exhibiting wideline and/or ultra-wideline powder patterns, despite the clear advantages these experiments could offer. Herein, we demonstrate that FS pulses selectively applied to a single sideband of the S spin can be used for efficient 1 H-S polarization transfer to S = 1/2 nuclides with large anisotropic chemical shift interactions at slow to moderate MAS rates. The Hartmann–Hahn matching conditions in BRAIN-CP/WCPMG-MAS experiments bear similarity to those of standard CP sequences, yet operate over UW frequency ranges and only require low-amplitude RF pulses on the S channel. Crucial to the success of the BRAIN-CP/WCPMG-MAS experiment is careful calibration of the RF amplitude, transmitter offset, and effective frequency sweep of the FS pulse applied to the S spins at a given MAS rate. Thus, by means of numerical simulations and experimental testing, we provide recommendations for the parameterization and setup of BRAIN-CP/WCPMG-MAS experiments for their most efficient use. Results showcasing the capability of the BRAIN-CP/WCPMG-MAS pulse sequence are presented, including applications to 119 Sn, 195 Pt, and 103 Rh NMR.

Kimball, James J. [Florida State University, Talla↗

DOE OSTI2023

Binary pseudo-random array (BPRA) for inspection and calibration for cylindrical wavefront interferometry

High-accuracy metrology is vitally important in manufacturing ultra-high-quality free-form mirrors designed to manipulate X-ray light with nanometer-scale wavelengths. However, surface topography measurements are instrument dependent, and without the knowledge of how the instrument performs under the practical usage conditions, the measured data contain some degree of uncertainty. Binary Pseudo Random Array (BPRA) “white noise” artifact are effective and useful for characterizing the Instrument Transfer Function (ITF) of surface topography metrology tools and wavefront measurement instrument. BPRA artifact contains features with all spatial frequencies in the instrument bandpass with equal weight. As a result, power spectral density of the patterns has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range. The application examples include electron microscopes, x-ray microscopes, interferometric microscopes, and large field-of-view Fizeau Interferometers. Furthermore, we will introduce the application of BPRA method to characterizing the ITF of Cylindrical Wavefront Interferometry (CWI), by developing the BPRA artifact which matches the radius of curvature of the cylindrical wavefront. The data acquisition and analysis procedures for different applications of the ITF calibration technique developed are also discussed.

Munechika, K↗

DOE OSTI2024

Performance of plane wavefront Fizeau interferometers in power spectral density measurements with tilted plane optics

Binary pseudo-random array (BPRA) test samples are useful devices for calibrating the instrument transfer function (ITF) of Fizeau interferometers, interferometric microscopes, and other optical and non-optical surface and wavefront metrology instruments. The intrinsic white noise character of the power spectral density (PSD) function of the BPRA pattern simplifies the extraction of the ITF from the measured PSD. The ITF determined in a dedicated calibration experiment can be used to reconstruct the surface height profile from the measured data, effectively enhancing the instrument’s spatial resolution. For a high confidence reconstruction procedure, a reliable analytical model of the IFT is desirable. Usually, the model accounts for the contributions to the ITF related the imperfections of the instrument’s optical and detector systems. Here, we experimentally demonstrate that in the case of surface height metrology with Fizeau interferometers, the PSD measurements and, therefore, the efficacy of the ITF calibration of the tool, are strongly affected by the instrument data acquisition and processing procedures, as well as by the shape of the optic under test and its alignment with respect to the interferometer.

Yashchuk, V V↗

DOE OSTI2023

Excited-State Charge Transfer in Push–Pull Platinum(II) π-Extended Porphyrins Fused with Pentacenequinone

Platinum(II) π-extended porphyrins fused with pentacenequinone and dihydropentacene have been successfully synthesized. These porphyrins were investigated using various techniques including absorption, steady-state, and time-resolved phosphorescence spectroscopy and differential pulse voltammetry. UV–vis absorption spectra of pentacenequinone-fused porphyrins (SW-Pt1 and SW-Pt2) showed unusually broad and nontypical absorption patterns. Phosphorescence spectra of SW-Pt1, SW-Pt2, and SW-Pt3 displayed similar emissions in the 704–706 nm region indicating electronic transitions of similar origin; however, the triplet lifetimes were found to be quenched in the case of both SW-Pt1 and SW-Pt2, suggesting the occurrence of excited-state events. Facile reductions were obtained for both the pentacene-quinone-fused monomer (SW-Pt2) and dimer (SW-Pt1) and were identified to be located at the pentacenequinone components. Here, the observed orbital segregations for SW-Pt2 and SW-Pt1 from DFT calculations supported the possibility of charge transfer in these push–pull systems. Interestingly, the established energy level diagram revealed that the charge transfer from the triplet excited Pt porphyrin is thermodynamically an uphill process. Systematic studies involving both femtosecond and nanosecond transient absorption techniques revealed that the singlet excited Pt porphyrins undergo an intermediate charge transfer state prior to populating the triplet state, providing a plausible explanation for phosphorescence quenching. The lifetime of the intermediate charge transfer states was found to be 25.9 and 5.68 ps, respectively, for SW-Pt1 and SW-Pt2.

Charge transfer↗

DOE OSTI2024

Structure, dynamics, and redox reactivity of an all-purpose flavodoxin

The flavodoxin of Rhodopseudomonas palustris CGA009 (Rp9Fld) supplies highly reducing equivalents to crucial en- zymes such as hydrogenase, especially when the organism is iron-restricted. By acquiring those electrons from photodriven electron flow via the bifurcating electron transfer flavoprotein, Rp9Fld provides solar power to vital metabolic processes. To understand Rp9Fld’s ability to work with diverse partners, we solved its crystal structure. We observed the canonical fla- vodoxin (Fld) fold and features common to other long-chain Flds but not all the surface loops thought to recognize part- ner proteins. Moreover, some of the loops display alternative structures and dynamics. To advance studies of protein– protein associations and conformational consequences, we assigned the 19F NMR signals of all five tyrosines (Tyrs). Our electrochemical measurements show that incorporation of 3- 19 F-Tyr in place of Tyr has only a modest effect on Rp9Fld’s redox properties even though Tyrs flank the flavin on both sides. Meanwhile, the 19 F probes demonstrate the expected paramagnetic effect, with signals from nearby Tyrs becoming broadened beyond detection when the flavin semiquinone is formed. However, the temperature dependencies of chemical shifts and linewidths reveal dynamics affecting loops close to the flavin and regions that bind to partners in a variety of systems. These coincide with patterns of amino acid type conservation but not retention of specific residues, arguing against detailed specificity with respect to partners. We pro- pose that the loops surrounding the flavin adopt altered con- formations upon binding to partners and may even participate actively in electron transfer.

19F NMR↗

DOE OSTI2025

Electron Microscopy Transfer System to Protect Atmosphere‐Sensitive Materials for Scanning Electron Microscopy Characterization

Atmosphere- and/or moisture-sensitive materials can be challenging to characterize using electron microscopy techniques due to sample preparation workflows that generally require exposure to ambient conditions. Here, we describe a novel preparation method that uses aluminum foil in combination with a commercial cryo-EM transfer system to circumvent undesired exposure to the atmosphere. First, hygroscopic MgCl 2 was used as a model material, and prepared samples (both protected and unprotected) were placed in a controlled-humidity environment (> 80% relative humidity) for various exposure lengths (circa seconds to hours). Following this, the effectiveness of the sample preparation method was determined by comparing qualitative photos and quantitative X-ray diffraction patterns between the two sample subsets. The combined results of these experiments suggest that the outlined preparation method effectively protects MgCl 2 from atmospheric contamination compared to MgCl 2 samples that had no protective measures taken. Finally, the preparation method was utilized to protect a highly hygroscopic crystalline BaO thin film for characterization via scanning electron microscopy, thereby demonstrating a functional application of the outlined preparation technique and an additional use for the commercial cryo-EM transfer system beyond its intended application.

atmosphere-sensitive materials↗