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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 OSTI2022

Tunable White Light for Elders (TWLITE): A Protocol Demonstrating Feasibility and Acceptability for Deployment, Remote Data Collection, and Analysis of a Home-Based Lighting Intervention in Older Adults

Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer’s disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants’ light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer’s disease and related conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Geolocation tracking for human identification and activity recognition using radar deep transfer learning

Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.

Alkasimi, Ahmad↗
DOE OSTI2020

Smart Vacuum Cleaner Concept of Operations [Slides]

Modern living brings with it the need to use time as efficiently as possible, one of the key objectives being to create as much free-time for ourselves as we possibly can. In an ideal world, the domestic appliances of today would be replaced by new gadgets with the ability to perform tasks in a completely independent way. This document presents the application of Systems Engineering tools for the concept of operations for a smart vacuum cleaner, a device that replaces the use of hand-held vacuums, and provides a completely automated remote-operated option for keeping floors of all types clean.

42 ENGINEERING↗
DOE OSTI2023

Biobased Semi-Interpenetrating Polymer Networks of Poly(ε-caprolactone) and Epoxidized Soybean Oil with Nanoscale Morphology, Shape-Memory Effect, and Biocompatibility

Creating biobased polymer blends with outstanding properties, nanoscale morphology, shape-memory capability, and biocompatibility is very crucial and requires a fundamental understanding of the phase behavior, macromolecular structure, and biological compatibility of the polymer blends with living cells. It is very critical to understand the complex relationships among the polymer structure, morphology, and performance of multifunctional smart materials under conditions that they are likely to encounter during use, particularly in biomedical applications. Biobased semi-interpenetrating polymer networks of poly(ε-caprolactone) and epoxidized soybean oil with nanoscale morphology have been successfully synthesized via in situ cationic polymerization and compatibilization in a homogeneous solution. Varies analytical and characterization techniques, such as Fourier transform infrared spectroscopy, differential scanning calorimetry, dynamic mechanical analysis, transmission electron microscopy, X-ray scattering, cell toxicity, and shape-memory effects (SMEs), have been employed to understand the structure–properties relationship of these smart, biobased nanostructured polymer blends. The synthesized nano blends were nontoxic or biocompatible and supported attachment of human vein endothelial cells, showing their potential use in biomedical applications. The current versatile, low-cost strategy for synthesizing the nanoscale morphology of semi-interpenetrating polymer networks with SMEs and biocompatibility should be widely applicable for polymer systems. This study is also considered as a continuation to our efforts in the area of biobased polymers to develop innovative technologies to transform natural resources into smart multifunctional materials for a wide range of applications, including coatings, adhesives, and medical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing Smart Home Privacy: A Tutorial on Local Differential Privacy Techniques for Frequency and Mean Estimation

The ubiquity of Internet of Things (IoT) systems has seamlessly integrated into our daily lives, particularly in smart homes where devices continuously monitor and optimize our living environments. These systems significantly contribute to home automation, energy efficiency, and overall comfort. However, this widespread connectivity poses inherent risks linked to the streaming of sensitive household data, necessitating robust privacy preservation mechanisms. This tutorial systematically examines privacy preservation through local differential privacy (LDP), with a particular focus on frequency and mean estimation techniques for smart home applications. Here, we present a comprehensive taxonomy of smart home data formats and provide detailed implementation guidance for event-based and w-event LDP mechanisms. Through practical examples using smart thermostats and HVAC systems, we demonstrate how these techniques can be effectively deployed in real-world scenarios. The tutorial concludes by examining emerging research directions, including adaptive privacy budgets and federated learning approaches, establishing a foundation for privacy-preserving smart home deployments.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2020

Near-Zero Power MOF-Based Sensors for NO 2 Detection

Detection and capture of toxic nitrogen oxides (NO x ) is important for emissions control of exhaust gases and general public health. The ability to directly electrically detect trace (0.5–5 ppm) NO 2 by a metal–organic framework (MOF)-74-based sensor at relatively low temperatures (50 °C) is demonstrated via changes in electrical properties of M-MOF-74, M = Co, Mg, Ni. The magnitude of the change is ordered Ni > Co > Mg and explained by each variant's NO 2 adsorption capacity and specific chemical interaction. Ni-MOF-74 provides the highest sensitivity to NO 2 ; a 725× decrease in resistance at 5 ppm NO2 and detection limit <0.5 ppm, levels relevant for industry and public health. Furthermore, the Ni-MOF-74-based sensor is selective to NO 2 over N 2 , SO 2 , and air. Linking this fundamental research with future technologies, the high impedance of MOF-74 enables applications requiring a near-zero power sensor or dosimeter, with the active material drawing <15 pW for a macroscale device 35 mm 2 with 0.8 mg MOF-74. Finally, this represents a 10 4 –10 6 × decrease in power consumption compared to other MOF sensors and demonstrates the potential for MOFs as active components for long-lived, near-zero power chemical sensors in smart industrial systems and the internet of things.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Editorial for Special Issue: Grid-Interactive Efficient Buildings - Part 1

We are pleased to present starting with this volume of ASME Journal of Engineering for Sustainable Buildings and Cities (JESBC), a series of peer-reviewed papers related to some of ongoing research projects and applications of Grid-interactive Efficient Buildings (GEBs). As part of a new program initiated by the Building Technology Office (BTO) with the US Department of Energy (DOE), GEBs are characterized as highly energy-efficient buildings that are equipped with smart technologies and distributed energy resources that can be dynamically controlled to meet grid needs and minimize electricity system costs, while meeting occupants’ comfort and productivity requirements. In simple terms,DOE defines GEBs as efficient, connected, smart, and flexible buildings. The GEB technologies touch on the main topical areas of JESBC aimed at fostering sustainable buildings and cities by providing results of evaluation studies and demonstration projects. Indeed, one of the main goals for JESBC is to disseminate solutions to enhance the built environment energy efficiencies, low carbon services, inter-connectivity services, resilient multi-services, and healthy and comfortable living spaces.

buildings↗
DOE OSTI2020

Interactive Metadata Integration with Brick

Many different digital representations of a building are produced over the course of its lifecycle. While these representations individually contain metadata required to support different stages of the building's lifecycle, they are largely not interoperable due to differences in structure, syntax and semantics. This impedes the development and deployment of data-driven applications providing fault detection and diagnosis, virtual metering or optimal control. [4] introduces a new platform for the continuous curation of a unified, Brick [1]-based metadata model that can be maintained throughout the building lifecycle. In this demonstration, we present a live proof-of-concept implementation of the platform with support for several metadata representations in the context of a simulated building lifecycle.

Fierro, Gabe↗
DOE OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

An enzyme-based approach for highly efficient self-replication of DNA origami dimers

Self-replication and exponential growth are essential to all living things, the driving force for Darwinian evolution, and potentially useful in nanotechnology for large-scale production of nanoscopic materials. An artificial (nonliving) self-replication system has been shown to exhibit exponential growth and selection using DNA monomer origami tiles templated on a dimer seed. That system purposefully avoided the use of enzymes to get a hint of how self-replication might have evolved in a prebiotic world by using CNV K and UV light to crosslink complementary DNA single strands. For further investigations into competition and extinction and for potential applications involving biocompatibility, we wanted to investigate enzymatic ligation to replace the chemical photo crosslinking step. Here, we present a system which uses thermotolerant T4 DNA ligase and no UV. This system has several additional advantages including a much faster cycling time, yielding 2,000,000 amplifications in 12 h. We also introduce competition to study the possibility of Darwinian-like evolution. Two pairs of DNA origami tiles compete for the same connection strands and show different growth rates under different connection strand concentrations. This system has the potential to combine with other enzymes, such as RNA polymerase to support feedback, allowing us to fine-tune replication dynamics and achieve sophisticated, life-like behaviors. The highly efficient self-replication and exponential growth of DNA origami dimers demonstrated in this work not only enhances our understanding of Darwinian evolution in nature but also opens the door to applications ranging from synthetic biology to smart materials.

Science & Technology - Other Topics↗
DOE OSTI2025

Fusion Intelligence: A Paradigm for Merging Natural and Artificial Intelligence

Here, this article presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic capabilities of intelligent biological organisms with the advanced potential of artificial intelligence (AI)-driven systems. FI harnesses the unique intelligence, sensing, actuation, and mobility attributes of living organisms, such as honeybees, blending these with the sophisticated data-driven problem-solving functionalities of AI. By bridging the gap between natural intelligence (NI) and AI, FI can transform how humans interact with and harness the capabilities of both natural and artificial systems. This article presents the model of FI and its application to solve practical problems, discusses the challenges and future directions of FI research, emphasizing a generalized approach to solve complex problems, where AI can observe/control NI in a closed-loop system. We demonstrate the potential for FI to enhance the performance of an agricultural IoT system via a simulated case study, which achieves 50% improvement in the efficacy of insect pollination (entomophily).

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Autonomously Responsive Membranes for Chemical Warfare Protection

Stimuli-responsive materials offer new opportunities to resolve long-standing material challenges and are rapidly gaining pivotal roles in diverse applications. For example, smart protective garments that rapidly transport water vapor and autonomously block chemical threats are expected to enable an effective new paradigm of adaptive personal protection. However, the incorporation of these seemingly incompatible properties into a single responsive system remains elusive. Herein, a bistable membrane that can rapidly, selectively, and reversibly transition from a highly breathable state in a safe environment to a chemically protective state when exposed to organophosphate threats such as sarin is demonstrated. Dynamic response to chemical stimuli is achieved through the physical collapse of an ultrathin copolymer layer on the membrane surface, which efficiently gates transport through membrane pores composed of single-walled carbon nanotubes (SWNTs). The adoption of nanometer-wide SWNTs for ultrafast moisture conduction enables a simultaneous boost in size-sieving selectivity and water-vapor permeability by decreasing nanotube diameter, thereby overcoming the breathability/protection trade-off that limits conventional membrane materials. Adaptive multifunctional membranes based on this platform greatly extend the active use of a protective garment and present exciting opportunities in many other areas including separation processes, sensing, and smart delivery.

36 MATERIALS SCIENCE↗