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At least 163 records · Page 9

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↗

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↗

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↗

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↗

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↗

The Highly Integrated Vehicle Ecosystem (HIVE): A Platform for Managing the Operations of On-Demand Vehicle Fleets

This paper introduces the Highly Integrated Vehicle Ecosystem (HIVE), a transportation modeling tool developed by within the Center for Integrated Mobility Sciences (CIMS) group at the National Renewable Energy Laboratory (NREL). HIVE is an agent-based supply/demand model for Mobility on Demand (MoD) which mixes agent-based modeling and centralized dispatch for automated and human-driven fleets and ride hail passengers. Research questions using HIVE span multiple categories, including intelligent fleet planning (assessing fleet, battery, and infrastructure investment decisions), intelligent fleet control (charge management, vehicle dispatching) and strategic business model decision-making (depot-based full-time drivers versus gig-based drivers, human-driven versus automated). The components of the HIVE model are explained and then HIVE is demonstrated in a case study using demand data from the New York City Taxi & Limousine Commission data set.

33 ADVANCED PROPULSION SYSTEMS↗

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗

Layered structures of assembled imine-linked macrocycles and two-dimensional covalent organic frameworks give rise to prolonged exciton lifetimes

Ordered organic materials and assemblies have great potential to be tailored to have desirable properties for optoelectronic applications, such as long exciton lifetime and high directional exciton mobility. Framework materials, such as two-dimensional covalent organic frameworks (2D COFs), as well as their truncated macrocyclic analogues, are versatile platforms to organize functional aromatic systems into designed assemblies and robust materials. In this study we investigate the exciton dynamics in a 2D COF, its corresponding hexagonal macrocycle, and extended nanotubes comprised of stacked macrocycles. The excitonic behavior of these three systems provide an understanding of excitonic processes that occur in the plane of the covalently bonded 2D macromolecules and between layers of the nanotubes and 2D COF. The nanotube and analogous 2D COF exhibit longer excited-state lifetimes (~100 ps) compared to the individual, solvated macrocycles (<0.5 ps). These differences are attributed to the internal conversion facilitated by the internal motions of the imine linkages which are significantly reduced in the assembled macrocycles in the nanotube and 2D COF sheets in the layered structures. The exciton diffusion processes in the assembled nanotubes and 2D COF systems were characterized by the autocorrelations of the transition dipole moment of the excitons, giving the depolarization time constants for both systems to be ~1 ps. This work also reveals the anisotropic exciton dynamics related to the in-plane and inter-plane structural factors in these systems. These studies provide guidance for the design of future COF materials, where the longer excited state lifetimes imparted by assembly are beneficial for optoelectronic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗

Eaton High Impact Project (Cooperative Research and Development Final Report)

The NREL team will partner with Eaton to evaluate control strategies for managing Electric bus (E-bus) fleets for grid services. NREL will develop a replicable platform for evaluating EV fleet management and quantifying benefits from grid services. The proposal will cover the technology areas of solar, energy storage, mobility, and buildings.

33 ADVANCED PROPULSION SYSTEMS↗

Thin-film TaAs: Developing a platform for Weyl semimetal devices

MX monopnictide compounds (M = Nb,Ta, X = As,P) are prototypical 3D Weyl semimetals (WSMs) that have been shown in bulk single crystal form to have potential for a wide variety of novel devices due to topologically protected band structures and high mobilities. However, very little is known about thin-film synthesis, which is essential to enable device applications. We synthesize TaAs(001) epilayers by molecular beam epitaxy on GaAs(001) and provide an experimental phase diagram illustrating conditions for single-phase, single-crystal-like growth. We investigate the relationship between nanoscale defects and electronic structure using angle-resolved photoemission spectroscopy, kelvin probe force microscopy, and transmission electron microscopy. Our results provide a roadmap and platform for developing 3D WSMs for device applications.

36 MATERIALS SCIENCE↗

Summary of Responses to the Request for Information (RFI) on Partnerships for Transformational Artificial Intelligence Models

The Department of Energy (DOE) issued a Request for Information (RFI) in December 2025 inviting public comments regarding partnerships for transformational Artificial Intelligence (AI) models for the Genesis Mission Consortium, a public-private partnership platform. This RFI solicited feedback from industry, nonprofit organizations, universities, independent research organizations and other stakeholders. Specifically, the RFI asked three questions on (1) mobilizing DOE National Laboratories to curate the scientific data in a responsible and privacy-preserving manner, (2) the extent to which existing general-purpose AI models can be leveraged and which scientific disciplines are priorities for such model development, and (3) mechanisms by which these AI models can be provided to scientific communities. This document summarizes the input from 194 unique nonproprietary responses from businesses, universities, nonprofit organizations, research institutes and laboratories as well as a variety of other contributors, including individual contributions.

97 MATHEMATICS AND COMPUTING↗

Local charge-screening and polarization switching in a ferroelectric ionic-superconductor

For ferroelectric ionic-conductors, polarization switching is complicated by the interplay between ion mobility and charge screening effects. When the ionic charge carriers also play a key role in the domain reversal, such as in Rb-doped KTiOPO 4 (RKTP), a higher level of complexity is introduced. RKTP provides an ideal platform for investigating the relationship between ionic conductivity and polarization reversal because its highly anisotropic crystal properties allow selective modification of material characteristics through diffusive cation doping. Here, we use indiffused Ba/K doping to create a significant increase in the ionic conductivity. Time-of-flight secondary ion mass spectrometry is employed to map Ba/K doping within the RKTP crystal and correlate it to changes in ionic mobility and polarization switching characteristics under an external field applied to the nonpolar face. Using band-excitation piezoresponse force microscopy, we demonstrate a selective switching-inhibition mechanism driven by the enhanced charge screening.

36 MATERIALS SCIENCE↗

Topological surface currents accessed through reversible hydrogenation of the three-dimensional bulk

Hydrogen, the smallest and most abundant element in nature, can be efficiently incorporated within a solid and drastically modify its electronic and structural state. In most semiconductors interstitial hydrogen binds to defects and is known to be amphoteric, namely it can act either as a donor (H + ) or an acceptor (H - ) of charge, nearly always counteracting the prevailing conductivity type. Here we demonstrate that hydrogenation resolves an outstanding challenge in chalcogenide classes of three-dimensional (3D) topological insulators and magnets — the control of intrinsic bulk conduction that denies access to quantum surface transport, imposing severe thickness limits on the bulk. With electrons donated by a reversible binding of H + ions to Te(Se) chalcogens, carrier densities are reduced by over 10 20 cm -3 , allowing tuning the Fermi level into the bulk bandgap to enter surface/edge current channels without altering carrier mobility or the bandstructure. The hydrogen-tuned topological nanostructures are stable at room temperature and tunable disregarding bulk size, opening a breadth of device platforms for harnessing emergent topological states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High conductivity coherently strained quantum well XHEMT heterostructures on AlN substrates with delta doping

Polarization-induced two-dimensional electron gases (2DEGs) in AlN/GaN/AlN quantum well high-electron-mobility transistors on ultrawide bandgap AlN substrates offer a promising route to advance microwave and power electronics with nitride semiconductors. The electron mobility in thin GaN quantum wells embedded in AlN is limited by high internal electric field and the presence of undesired polarization-induced two-dimensional hole gases (2DHGs). To enhance the electron mobility in such heterostructures on AlN, previous efforts have resorted to thick, relaxed GaN channels with dislocations. In this work, we introduce n-type compensation δ-doping in a coherently strained single-crystal (Xtal) AlN/GaN/AlN heterostructure to counter the 2DHG formation at the GaN/AlN interface, and simultaneously lower the internal electric field in the well. This approach yields a δ-doped XHEMT structure with a high 2DEG density of ∼3.2×1013 cm−2 and a room temperature (RT) mobility of ∼855 cm2/Vs, resulting in the lowest RT sheet resistance 226.7 Ω/□ reported to date in coherently strained AlN/GaN/AlN HEMT heterostructures on the AlN platform.

Physics↗

Epitaxial aluminum layer on antimonide heterostructures for exploring Josephson junction effects

In this study, we present results of our recent work of epitaxially-grown aluminum (epi-Al) on antimonide heterostructures, where the epi-Al thin film is grown at either room temperature or below zero °C. A sharp superconducting transition at T ∼ 1.3 K is observed in these epi-Al films. We further show that supercurrent states are realized in Josephson junctions fabricated in the epi-Al/antimonide heterostructures with mobility μ ∼ 1.0 × 10 6 cm 2 /Vs. These results clearly demonstrate we have achieved growing high-quality epi-Al/antimonide heterostructures, a promising platform for the exploration of Josephson junction effects for quantum information science and microelectronics applications.

Antimonide heterostructures↗

Electron mobility enhancement by electric field engineering of AlN/GaN/AlN quantum-well HEMTs on single-crystal AlN substrates

To enhance the electron mobility in quantum-well high-electron-mobility transistors (QW HEMTs), we investigate the transport properties in AlN/GaN/AlN heterostructures on Al-polar single-crystal AlN substrates. Theoretical modeling combined with experiment shows that interface roughness scattering due to high electric field in the quantum well limits mobility. Increasing the width of the quantum well to its relaxed form reduces the internal electric field and scattering, resulting in a binary QW HEMT with a high two-dimensional electron gas (2DEG) density of 3.68×1013 cm–2, a mobility of 823 cm2/Vs, and a record-low room temperature (RT) sheet resistance of 206 Ω/□. Further reduction of the quantum well electric field yields a 2DEG density of 2.53×1013 cm–2 and RT mobility > 1000 cm2/V s. These findings will enable future developments in high-voltage and high-power microwave applications on the ultrawide bandgap AlN substrate platform.

Physics↗

Layer-dependent optically induced spin polarization in InSe

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semi-conductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k · p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. In conclusion, optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗