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At least 667 records · Page 37

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DOE OSTI2018

Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels

Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014-2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by a number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential ‘fit for the purpose’ use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. In conclusion, the analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

The role of peer review and science for advancing public understanding

We live in a time of rapid change. The Covid-19 pandemic has united the world to understand and develop solutions to protect the health and wellbeing of society. At the same time, access to information on the internet and social media has exploded. People are exposed to information with many claims. Some of the information is relevant and accurate. A significant fraction may be inaccurate, biased, and potentially dangerous. With Covid-19, the information might point to cures that are toxic or risks that are not relevant. The scientific community has developed a process to develop and convey knowledge. The validation of results and their interpretation is critical to advancing knowledge. The core of the scientific process is based on developing a hypothesis-driven concept, designing and executing an experimental approach to prove or disprove the hypothesis, conducting a statistical analysis of the results to confirm their validity, and interpreting their meaning and impact. When their work is completed the scientist prepares to publish the work. Publication requires peer review. Hypotheses, experimental approaches, results, and interpretation of results are independently and anonymously reviewed by a scientific peer. The reviewers are selected based on their expertise in the field. The peer review process is essential for the integrity of the scientific process. Without a robust scientific process, society cannot discriminate between validated results and interpretations and biased, inaccurate, and potentially dangerous conjecture. With inaccurate science society and policy makers may make poor decisions that could hurt people and the world around them. While scientists should not censor information available to the public, scientists have a responsibility to inform the public. We must articulate what the scientific process is and why it is essential when considering the validity of information. Scientist are responsible for pointing out when public information has not been vetted through a scientific process and therefore cannot be considered conclusive. Scientists are responsible for highlighting information that has been generated and vetted with the scientific process. Over time, our goal is for the public to value the scientific process based on peer review. The public could become enlightened to the necessity for peer review to support decisions and action. Scientists need to contribute to advancing public knowledge. When invited to serve as a peer reviewer, scientists have a responsibility to contribute to the process. We all are extremely busy making peer review a distraction. While peer review does not directly benefit our own work and career, it benefits our chosen field of science. This is very important for the research area that our journal, Water-Energy Nexus, is focusing on. We cover the convergence of science and technology integrating across large spatial ranges (micro to global) in environmental pollution and the balance of remediation issues with energy use. Water-Energy Nexus wants to provide scientists with a platform for scientific debate and discussion on this rich and complicated topic. With the manuscripts submitted to WEN cover integrated research topics across a broad range of subjects from environment sciences through energy engineering, the role of peer review is essential. Peer review on a new submission to WEN should not be biased towards a specific science field. It should be done considering the complex nature of issues in the water-energy nexus. Scientists need to contribute to the public dialog. To accomplish these goals, we need to be honest, accurate, articulate, and use understandable language. The impact of WEN is very diverse affecting society in many ways. The scientific audience for WEN is diverse and readers may not fully understand the content of the papers outside their research expertise. Therefore, scientists, especially those who want to contribute to WEN, should work with communications specialists to convey message to benefit society. The audience includes the general public and researchers outside the author’s core specialty. While the peer-reviewed journal article is the cornerstone of our careers and critical for advancing scientific knowledge, a general interest story could benefit society even more. The author cannot lose sight that our work has two audiences, the science community, and more importantly, our family, friends, neighbors, and policy makers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Delivering real-time multi-modal materials analysis with enterprise beamlines

Contemporary advancements in low-cost automation and computation, reduced barrier to entry in developing artificial intelligence/machine learning (AI/ML), and increased ability to represent complex materials in digital form have led to a number of accelerated materials discovery platforms. However, many of these approaches operate with completely rigid vertical integration in an isolated feedback loop using limited modalities. In order to make a substantial impact on discovering new energy materials, AI-driven experiments must operate collaboratively with each other and researchers and over multiple measurement modalities. Herein, we describe the potential for an “internet of things” approach to self-driving enterprise beamlines that merges core information technologies, robotics, and multi-modal AI. The approach will enable full utility of light sources, collaborate effectively with other remote materials acceleration platforms, and help stride toward the world’s energy future.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Next Steps for Compressively Sensed Video in Transmission Electron Microscopy

Here, compressive sensing (CS) is a fast-emerging approach for improving data throughput by taking advantage of the inherent compressibility of real world data—the same kind of compressibility that allows millions of users to stream full-screen video without breaking the internet. Transmission electron microscope (TEM) users regularly collect multi-GB data sets and then use aggressive data reduction strategies to draw out the tiny residual of Shannon entropy that answers the question they wanted to ask. Much of the raw-byte-count information in an acquisition is random, redundant, or irrelevant, yet its collection still represents substantial cost in terms of instrumentation bandwidth and sample damage.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

ENVIRONMENTAL REVIEWS AND CASE STUDIES: Addressing the Public Outreach Responsibilities of the National Historic Preservation Act: Argonne National Laboratory’s Box Digital Display Platform

Federal agencies are made responsible for managing the historic properties under their jurisdiction by the National Historic Preservation Act of 1966, as amended. A component of this responsibility is to mitigate the effect of a federal undertaking on historic properties through mitigation often through documentation. Providing public access to this documentation has always been a challenge. To address the issue of public access to mitigation information, personnel from Argonne National Laboratory created the Box Digital Display Platform, a system for communicating information about historic properties to the public. The platform, developed for the US Army Dugway Proving Ground, uses short introductory videos to present a topic but can also incorporate photos, drawings, GIS information, and documents. The system operates from a small, self-contained computer that can be attached to any digital monitor via an HDMI cable. The system relies on web-based software that allows the information to be republished as a touch-screen device application or as a website. The system does not connect to the Internet, and this increases security and eliminates the software maintenance fees associated with websites. The platform is designed to incorporate the products of past documentation to make this information more accessible to the public; specifically those documentations developed using the Historic American Building Survey/ Historic American Engineering Record (HABS/HAER) standards. Argonne National Laboratory’s Box Digital Display Platform can assist federal agencies in complying with the requirements of the National Historic Preservation Act. Environmental Practice 18: 209–213 (2016)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

OpenMSI: A High-Performance Web-Based Platform for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) enables researchers to directly probe endogenous molecules directly within the architecture of the biological matrix. Unfortunately, efficient access, management, and analysis of the data generated by MSI approaches remain major challenges to this rapidly developing field. Despite the availability of numerous dedicated file formats and software packages, it is a widely held viewpoint that the biggest challenge is simply opening, sharing, and analyzing a file without loss of information. Here we present OpenMSI, a software framework and platform that addresses these challenges via an advanced, high-performance, extensible file format and Web API for remote data access (http://openmsi.nersc.gov). The OpenMSI file format supports storage of raw MSI data, metadata, and derived analyses in a single, self-describing format based on HDF5 and is supported by a large range of analysis software (e.g., Matlab and R) and programming languages (e.g., C++, Fortran, and Python). Careful optimization of the storage layout of MSI data sets using chunking, compression, and data replication accelerates common, selective data access operations while minimizing data storage requirements and are critical enablers of rapid data I/O. The OpenMSI file format has shown to provide >2000-fold improvement for image access operations, enabling spectrum and image retrieval in less than 0.3 s across the Internet even for 50 GB MSI data sets. To make remote high-performance compute resources accessible for analysis and to facilitate data sharing and collaboration, we describe an easy-to-use yet powerful Web API, enabling fast and convenient access to MSI data, metadata, and derived analysis results stored remotely to facilitate high-performance data analysis and enable implementation of Web based data sharing, visualization, and analysis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Colloidal Quantum Dot Photovoltaics: Current Progress and Path to Gigawatt Scale Enabled by Smart Manufacturing

Colloidal quantum dots (QDs) have lately been pursued with intense vigor for optoelectronic applications such as photovoltaics (PV), flexible electronics, displays, mid-infrared photodetectors, lasers, and single-photon emitters. These nanometer-sized semiconducting crystals can be suitably mass-produced and size-tuned via cost-effective solution-based synthetic routes to operate in the quantum size confinement regime, endowing them with a wide array of exotic optical and electronic properties. While the first potential market entry could be in displays and in niche applications such as “internet-of-things”, ultimately, the technology has the potential to influence large-scale terrestrial power generation, because it is amenable to high-throughput synthesis from Earth-abundant materials and large-area solution-based coating techniques and can be air-stable. In this Review, we chronicle the recent advances that have propelled QD PV toward commercialization and highlight potential areas for further progress. We present an account of the material compositions being explored as QDs and their various benefits, major chemical passivation and doping strategies that have been developed to allay QD surface traps, and advanced device designs deployed to maximize charge extraction. We also discuss pathways to >20% efficient QD PV and describe recent advances in high-precision and autonomous synthesis of such materials. With recent demonstrations of scalable synthesis of high-quality QDs, smart manufacturing of QDs and QD solids, and fabrication of stable solar cells under ambient conditions, we suggest that the technology is on the road to achieving maturity and technological relevance and that gigawatt per year distributed panel production sites may be within reach.

14 SOLAR ENERGY↗

DOE OSTI2018

Plasma Jet Printing and in Situ Reduction of Highly Acidic Graphene Oxide

Miniaturization of electronic devices and the advancement of Internet of Things pose exciting challenges to develop technologies for patterned deposition of functional nanomaterials. Printed and flexible electronic devices and energy storage devices can be embedded onto clothing or other flexible surfaces. Graphene oxide (GO) has gained much attention in printed electronics due its solution processability, robustness, and high electrical conductivity in the reduced state. We introduce an approach to print GO films from highly acidic suspensions with in situ reduction using an atmospheric pressure plasma jet. Low-temperature plasma of a He and H 2 mixture was used successfully to reduce a highly acidic GO suspension (pH < 2) in situ during deposition. This technique overcomes the multiple intermediate steps required to increase the conductivity of deposited GO. X-ray spectroscopic studies confirmed that the reaction intermediates and the concentration of oxygen functionalities bonded to GO have been reduced significantly by this approach without any additional steps. Moreover, the reduced GO films showed enhanced conductivity. Hence, this technique has a strong potential for printing conducting patterns of GO for a range of large-scale applications.

42 ENGINEERING↗

DOE OSTI2018

Design and Integration of Flexible Sensor Matrix for in Situ Monitoring of Polymer Composites

Sensory polymer composites are highly desirable for applications such as in situ and real-time production processes and structural health monitoring, and for technologies that include human–machine interfaces for the next generation of Internet of Things. However, the development of these materials is still in its infancy: these materials have been reported, but large-scale fabrication of polymer composites with versatile and customizable sensing capabilities has yet to be demonstrated. Here, we report on a scalable fabrication strategy that enables such materials by designing and integrating PCB technology-inspired large-area flexible sensor matrices into polymer composites. The integrated sensor matrices successfully monitored in situ the production processes and structural health of an industrial polymer composite: from the application of vacuum, resin flow and polymerization, production defects, and temperature distribution. Our results demonstrate that the proposed strategy is a simple and effective solution as a distributed monitoring platform for polymer composites and shows the potential towards next generation of sensory polymer composites.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Life Cycle Inventory Availability: Status and Prospects for Leveraging New Technologies

The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.

blockchain platforms↗

DOE OSTI2016

Multidimensional materials and device architectures for future hybrid energy storage

Electrical energy storage plays a vital role in daily life due to our dependence on numerous portable electronic devices. Moreover, with the continued miniaturization of electronics, integration of wireless devices into our homes and clothes and the widely anticipated ‘Internet of Things’, there are intensive efforts to develop miniature yet powerful electrical energy storage devices. Here, this review addresses the cutting edge of electrical energy storage technology, outlining approaches to overcome current limitations and providing future research directions towards the next generation of electrical energy storage devices whose characteristics represent a true hybridization of batteries and electrochemical capacitors.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Giant piezoelectric voltage coefficient in grain-oriented modified PbTiO 3 material

A rapid surge in the research on piezoelectric sensors is occurring with the arrival of the Internet of Things. Single-phase oxide piezoelectric materials with giant piezoelectric voltage coefficient (g, induced voltage under applied stress) and high Curie temperature (T c ) are crucial towards providing desired performance for sensing, especially under harsh environmental conditions. Here, we report a grain-oriented (with 95% <001> texture) modified PbTiO 3 ceramic that has a high T c (364°C) and an extremely large g 33 (115 × 10 -3 Vm N -1 ) in comparison with other known single-phase oxide materials. Our results reveal that self-polarization due to grain orientation along the spontaneous polarization direction plays an important role in achieving large piezoelectric response in a domain motion-confined material. Finally, the phase field simulations confirm that the large piezoelectric voltage coefficient g 33 originates from maximized piezoelectric strain coefficient d 33 and minimized dielectric permittivity ε 33 in [001]-textured PbTiO 3 ceramics where domain wall motions are absent.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Record thermopower found in an IrMn-based spintronic stack

The Seebeck effect converts thermal gradients into electricity. As an approach to power technologies in the current Internet-of-Things era, on-chip energy harvesting is highly attractive, and to be effective, demands thin film materials with large Seebeck coefficients. In spintronics, the antiferromagnetic metal IrMn has been used as the pinning layer in magnetic tunnel junctions that form building blocks for magnetic random access memories and magnetic sensors. Spin pumping experiments revealed that IrMn Néel temperature is thickness-dependent and approaches room temperature when the layer is thin. Here, we report that the Seebeck coefficient is maximum at the Néel temperature of IrMn of 0.6 to 4.0 nm in thickness in IrMn-based half magnetic tunnel junctions. We obtain a record Seebeck coefficient 390 (±10) μV K -1 at room temperature. Our results demonstrate that IrMn-based magnetic devices could harvest the heat dissipation for magnetic sensors, thus contributing to the Power-of-Things paradigm.

42 ENGINEERING↗

DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A Dive into Underwater Solar Cells

Our oceans are vast, mostly unexplored and difficult to monitor. Large-scale implementation of a fully autonomous 'Internet of Underwater Things' would transform how we collect and share data from this domain; however, deployment is prohibited by the lack of persistent power sources. In principle, underwater solar-energy generation can complement the use of batteries and provide a solution, although dedicated research is needed since traditional silicon solar cells do not perform well underwater due to water's strong absorption of near-infrared light. In this Perspective we present examples of solar-powered underwater applications and discuss which types of solar-harvesting materials could be appropriate, including GaInP variants, CdTe, organic semiconductors, and perovskite semiconductors. We also discuss challenges that need to be addressed, such as the development of effective antifouling coatings and new certification standards given that underwater conditions are starkly different from those in terrestrial environments.

antifouling coatings↗

DOE OSTI2025

Certified randomness using a trapped-ion quantum processor

Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers, realizing this potential remains a challenge. An example is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is impossible to achieve solely by classical computation. Here we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the Internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum ‘challenge’ circuits using a small randomness seed, sends them to an untrusted quantum server to execute and verifies the results of the server. We analyse the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 10 18 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of present-day quantum computers.

computer science↗

DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗