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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Aprendizaje vertical

In the teaching and learning events of our life, there are characters who play key roles. Teachers, mentors, relatives, novels, songs, newspaper columns, often help us understand the questions in our heads. However, we often reject them because they are messages or attitudes from older people and the generation gap does not allow us to understand them well. The times of covid-19 have us locked up at home. They alert us that precisely “elderly people” are a vulnerable group. Also, we have been asked to take care of grandparents. If we are lucky enough to have an internet connection, it seems that we can learn anything we want with just one click. This could lead us to explore only what we find attractive, easy and comfortable. With this testimony, I invite you to acknowledge on the role of the elderly in the learning of entire generations. I call for openness towards diverse voices and for the understanding that we all play a role in moving towards a more mature society.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

An Overview of the Database Framework for GEM Detector under CMS Experiment at CERN

In this paper, we give an overview of the database framework which we have developed for the Gas Electron Multiplier (GEM) Detector at CERN. The GEM constitutes a powerful addition to the family of fast radiation detectors; originally developed for particle physics experiments, and has spawned a large number of developments and applications. The GEM database framework comprises four components. The first component is the database itself. There are three instances of the database which have been deployed. One is for the development purpose which contains test data, other one is for the integration purposes while the last one is for the production purpose which contains real data. The database further comprises various schemas and each schema consists of different tables in it. We use separate schemas for various types of tables. The second component of the database framework is called DB Loader. The DB Loader is used to load data into database. The data is prepared in the predefined format which is in the XML form. Then the xml file is copied into a spool area of the server in which DB loader is running. Once the file is copied, the DB Loader loads the file into the database. The loader returns appropriate status codes after performing database insertion/update operation. The status of the database operations is checked against the status code which is returned by the loader. The third component of the database framework comprises graphical user interface (GUI). This is a web-based interface which can be accessed from the internet. This interface is basically used for the detector construction and to perform various quality control tests on the detector and its components. The last component of the database framework is called online monitoring system (OMS). The OMS is data visualization framework for the various detectors of the CMS experiment at CERN. It is also used to display data for the GEM. It enables users to view and retrieve database contents without having to learn database specifics.

Imran, Muhammad↗
DOE OSTI2023

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as Internet of Things (IoT), edge computing, and usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN’s (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optics. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded to fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. In conclusion, the development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Defending our public biological databases as a global critical infrastructure

Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world’s primary database of biological sequence (and related) information. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the Internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly “retrofitted” mitigations. Furthermore this Perspective surveys some potential quality assurance and security weaknesses in existing open genomic and proteomic repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from cybersecurity.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Augmented Reality for Enabling Smart Nuclear Infrastructure

This work presents a novel, comprehensive framework that leverages emerging augmented reality headset technology to enable smart nuclear industrial infrastructure that a human can easily interact with to improve their performance in terms of safety, security, and productivity. Nuclear industrial operations require some of the most complicated infrastructure that must be managed today. Nuclear infrastructure and their associated industrial operations typically features stringent requirements associated with seismic, personnel management (e.g., access control, equipment access), safety (e.g., radiation, criticality, mechanical, electrical, spark, and chemical hazards), security (cyber/physical), and sometimes international treaties for nuclear non-proliferation. Furthermore, a wide variety of manufacturing and maintenance operations take place within these facilities further complicating their management. Nuclear facilities require very thorough and stringent documentation of the operations occurring within these facilities as well as maintaining a tight chain-of-custody for the materials being stored within the facility. The emergence of augmented reality and a variety of Internet of Things (IoT) devices offers a possible solution to help mitigate these challenges. This work provides a demonstration of a prototype smart nuclear infrastructure system that leverages augmented reality to illustrate the advantages of this system. It will also present example augmented reality tools that can be leveraged to create the next generation of smart nuclear infrastructure. The discussion will layout future directions of research for this class of work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2018

Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards

Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Radio frequency sensing and its innovative applications in diverse sectors: A comprehensive study

Recent advancements in radio frequency (RF) sensing technology can be attributed to the development of the Internet of Things (IoT), healthcare, RF-identification, and communication applications. RF sensing is a multidisciplinary research field that requires expertise in computing, electronics, and electromagnetics to cover all system features, including protocol development, antenna design, sensor integration, algorithm formulation, interconnection, data, and analytics. The overarching aim of this work is to present detailed information about RF technologies and their innovations and application diversity from the novel work carried out at CSI Lab1, together in one platform with an extensive survey. This study presents state-of-the art applications and RF sensing that include W-Fi, radar, and SDR and RFID-based sensing. A comprehensive survey and study of the advantages and limitations of each non-contact technology is discussed. Additionally, open research gaps have been identified as well. Decades of knowledge and experience have been put to use to meet new challenges and demands. The development and study of RF systems, IoT, RFID sensing, and research and deployment activities, are briefly discussed. The emerging research projects with industry, institutional research centers, and academic studies are also addressed. Finally, an outline of identified potential future research areas is provided, emphasizing opportunities and challenges.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Grid Cyber-Security Strategy in an Attacker-Defender Model

The progression of cyber-attacks on the cyber-physical system is analyzed by the Probabilistic, Learning Attacker, and Dynamic Defender (PLADD) model. Although our research does apply to all cyber-physical systems, we focus on power grid infrastructure. The PLADD model evaluates the effectiveness of moving target defense (MTD) techniques. We consider the power grid attack scenarios in the AND configurations and OR configurations. In addition, we consider, for the first time ever, power grid attack scenarios involving both AND configurations and OR configurations simultaneously. Cyber-security managers can use the strategy introduced in this manuscript to optimize their defense strategies. Specifically, our research provides insight into when to reset access controls (such as passwords, internet protocol addresses, and session keys), to minimize the probability of a successful attack. Our mathematical proof for the OR configuration of multiple PLADD games shows that it is best if all access controls are reset simultaneously. For the AND configuration, our mathematical proof shows that it is best (in terms of minimizing the attacker's average probability of success) that the resets are equally spaced apart. We introduce a novel concept called hierarchical parallel PLADD system to cover additional attack scenarios that require combinations of AND and OR configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗
DOE OSTI2023

On the Feasibility of Market Manipulation and Energy Storage Arbitrage via Load-Altering Attacks

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

25 ENERGY STORAGE↗
DOE OSTI2026

Design of a SMART Valve Testbed for Nuclear Thermal Dispatch

By the year 2050, the United States aims to achieve net-zero carbon emissions. To achieve this target, the licensing of the Light Water Reactor (LWR) fleet has been extended for 20 more years. To stay economically competitive with other power sources such as renewable and fossil-fuel power plants, the U.S. Department of Energy has introduced a plan to modernize the existing LWR fleet and diversify the revenue stream. One of the plans is to dispatch thermal energy to endothermic industrial processes. SMART valves will play an important role in this initiative by efficiently balancing the load by regulating valves in a coordinated manner while monitoring the thermal-hydraulic systems to enhance safety and maintain the integrity of the power plant. This research aims to develop a facility to test the coordinated control algorithm and produce various test results for training the monitoring system. The constructed facility is capable of simulating various operational and accidental scenarios by coordinating all the valves (positions) and pump (flowrate). The facility is developed with an Internet of Things (IoT)-based custom system and a python-based valve position control and coordination mechanism. It has achieved stable sensor outputs, pump control, and coordinated valve regulation in all three valves with minimum obstruction in the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle

The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.

Gangwani, Pranav (ORCID:0000000159226002)↗
DOE OSTI2025

Exponential Backoff and Its Security Implications for Safety-Critical OT Protocols over TCP/IP Networks

The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication latency and cybersecurity. Oftentimes, legacy OT protocols were adapted to the TCP/IP stack without an extensive review of the ramifications to their robustness, performance, or safety objectives. To further accommodate the IT/OT convergence, protocol gateways were introduced to facilitate the migration from serial protocols to TCP/IP protocol stacks within modern IT/OT infrastructure. However, they often introduce additional vulnerabilities by exposing traditionally isolated protocols to external threats. This study investigates the security and reliability implications of migrating serial protocols to TCP/IP stacks and the impact of protocol gateways, utilizing two widely used OT protocols: Modbus TCP and DNP3. Our protocol analysis finds a significant safety-critical vulnerability resulting from this migration, and our subsequent tests clearly demonstrate its presence and impact. A multi-tiered testbed, consisting of both physical and emulated components, is used to evaluate protocol performance and the effects of device-specific implementation flaws. Through this analysis of specifications and behaviors during communication interruptions, we identify critical differences in fault handling and the impact on time-sensitive data delivery. The findings highlight how reliance on lower-level IT protocols can undermine OT system resilience, and they inform the development of mitigation strategies to enhance the robustness of industrial communication networks.

DNP3↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Integration of Green Energy and Advanced Energy-Efficient Technologies for Municipal Wastewater Treatment Plants

Wastewater treatment can consume a large amount of energy to meet discharge standards. However, wastewater also contains resources which could be recovered for secondary uses under proper treatment. Hence, the goal of this paper is to review the available green energy and biomass energy that can be utilized in wastewater treatment plants. Comprehensive elucidation of energy-efficient technologies for wastewater treatment plants are revealed. For these energy-efficient technologies, this review provides an introduction and current application status of these technologies as well as key performance indicators for the integration of green energy and energy-efficient technologies. There are several assessment perspectives summarized in the evaluation of the integration of green energy and energy-efficient technologies in wastewater treatment plants. To overcome the challenges in wastewater treatment plants, the Internet of Things (IoT) and green chemistry technologies for the water and energy nexus are proposed. The findings of this review are highly beneficial for the development of green energy and energy-efficient wastewater treatment plants. Future research should investigate the integration of green infrastructure and ecologically advanced treatment technologies to explore the potential benefits and advantages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High Seebeck Coefficient from Screen-Printed Colloidal PbSe Nanocrystals Thin Film

Thin-film thermoelectrics (TEs) with a thickness of a few microns present an attractive opportunity to power the internet of things (IoT). Here, we propose screen printing as an industry-relevant technology to fabricate TE thin films from colloidal PbSe quantum dots (QDs). Monodisperse 13 nm-sized PbSe QDs with spherical morphology were synthesized through a straightforward heating-up method. The cubic-phase PbSe QDs with homogeneous chemical composition allowed the formulation of a novel ink to fabricate 2 μm-thick thin films through robust screen printing followed by rapid annealing. A maximum Seebeck coefficient of 561 μV K -1 was obtained at 143 °C and the highest electrical conductivity of 123 S m -1 was reached at 197 °C. Power factor calculations resulted in a maximum value of 2.47 × 10 -5 W m -1 K -2 at 143 °C. To the best of our knowledge, the observed Seebeck coefficient value is the highest reported for TE thin films fabricated by screen printing. Thus, this study highlights that increased Seebeck coefficients can be obtained by using QD building blocks owing to quantum confinement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Self-Sustainable IoT-Based Remote Sensing Powered by Energy Harvesting Using Stacked Piezoelectric Transducer and Thermoelectric Generator

We propose a self-powered remote multi-sensing system for traffic sensing which is powered by the collective energy harvested from the mechanical vibration of the road caused by the passing vehicles and from the temperature gradient between the asphalt of the road and the soil underneath. A stacked piezoelectric transducer converts mechanical vibrations into electrical energy and a thermoelectric generator harvests the thermal energy from the thermal gradient. Electrical energy signals from the stacked piezoelectric transducer and the thermoelectric generators are converted into usable DC power to recharge the battery using AC-DC and DC-DC converters working simultaneously. The multi-sensing system comprises an embedded system with a microcontroller that acquires data from the sensors and sends the sensory data to an IoT transceiver which transmits the data as RF packets to an ethernet gateway. The gateway converts the RF packets into Internet Protocol (IP) packets and sends them to a remote server. Laboratory and road-testing results showed over 98% sensory data accuracy with the system functioning solely powered by the energy harvested from the alternative energy sources. The successful maximum transmission distance obtained between the IoT, and the gateway was approximately 1 mile, which is a considerable transmission distance achieved in an urban environment. Successful operation of the self-powered multi-sensing system under both laboratory and road conditions contributes considerably to the fields of energy harvesting and self-powered remote sensing systems. The energy flow chart and efficiency for the steps in the system were found to be mechanical power from vehicles to the energy harvester of 0.25%, stacked PZT transducer efficiency was found to be 37%, and for the TEGs the efficiency is 11%. AC-to-DC and DC-to-DC converters’ efficiencies were found to be 90% and 11%. The wireless communication RF transceiver efficiency was found to be 62.5%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗