Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Smart material”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications

Novel devices ranging from bio magnetic field sensors to energy harvesting nano machines utilize the piezotronic effect. For optimal function, understanding the interaction of electrical and strain phenomena within the semiconductor crystal is necessary. Here, studies of a model piezotronic system are presented, consisting of a ZnO microrod coated by a thin layer of gold, which forms a Schottky contact with the piezoelectric ZnO material. Coherent X-ray diffraction imaging (CXDI) and transmission electron microscopy (TEM) are used to visualize the structure and strain distribution, showing that the ZnO microrod exhibits strains of multiple origins in the bulk and at the interface. Strain values of -6 × 10 -4 have been measured by CXDI at the ZnO/Au interface. The origin is shown to be a combination of an interface strain, possibly caused by the Schottky contact formation, and distinct, localized electrical fields inside the crystal which are assigned to electron depletion and screening in a bent ZnO/Au piezotronic rod. These findings will contribute to sensor development and to a better understanding of piezotronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Backbone-Photodegradable Polymers by Incorporating Acylsilane Monomers via Ring-Opening Metathesis Polymerization

Materials capable of degradation upon exposure to light hold promise in a diverse range of applications including biomedical devices and smart coatings. Despite the rapid access to macromolecules with diverse compositions and architectures enabled by ring-opening metathesis polymerization (ROMP), a general strategy to introduce facile photodegradability into these polymers is lacking. Here, we report copolymers synthesized via ROMP that can be degraded by cleaving the backbone in both solution and solid states under irradiation with a 52 W, 390 nm Kessil LED to generate heterotelechelic low-molecular-weight fragments. To the best of our knowledge, this work represents the first instance of the incorporation of acylsilanes into a polymer backbone. Mechanistic investigation of the degradation process supports the intermediacy of an α-siloxy carbene, formed via a 1,2-photo Brook rearrangement, which undergoes insertion into water followed by cleavage of the resulting hemiacetal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Polarizable nanoparticles are of interest in materials science because of their rich and complex phase behavior that can be used to engineer nanostructured materials with long-range crystalline order. To understand and rationally navigate the design space of polarizable nanoparticles for self-assembling highly ordered superlattices, we developed a coarse-grained computational model to describe the nanoparticle-nanoparticle interactions in implicit solvent and employ the computationally efficient image method to model many-body polarization interactions. We conducted high-throughput virtual screening over a five-dimensional particle design space spanned by temperature, particle size, particle charge, particle dielectric, and solvent dielectric using enhanced sampling molecular dynamics calculations within an active learning framework to efficiently map out the regions of thermodynamic stability of the self-assembled aggregates. We validate our predictions in comparisons against small angle x-ray scattering measurements of gold nanoparticles surface functionalized with metal chalcogenide ligands. Lastly, we use our validated phase maps to computationally design switchable nanostructured materials capable of triggered assembly and disassembly as a function of temperature and solvent dielectric with potential applications as sensors, smart windows, optoelectronic devices, and in medical diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ab initio study of tungsten-based alloys under fusion power-plant conditions

Tungsten (W) is considered a leading candidate for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. However, tungsten also presents a very low fracture toughness, primarily associated with intergranular failure and bulk plasticity, limiting its applications. In recent years, several families of tungsten-based alloys have been explored to overcome the aforementioned limitations of pure tungsten. These include tungsten-based high-entropy alloys (W-HEAs) and tungsten-based Self-passivating Metal Alloys with Reduced Thermo-oxidation or “SMART alloys” (W-SAs). Given their proximity to the plasma, it is crucial to understand how the exposure of these candidate plasma-facing materials (PFMs) to the neutron fluxes expected in fusion reactors impacts their material behavior over time. In this work, we present a computational approach that combines inventory codes and first-principles DFT electronic structure calculations to understand the behavior of transmuting tungsten-based PFMs. In particular, we calculate the changes in the chemical composition, production uncertainties, the elastic and ductility properties, and the density of states for five tungsten-based PFMs when exposed to EU-DEMO fusion first wall conditions for ten years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Single‐Domain Multiferroic Array‐Addressable Terfenol‐D (SMArT) Micromagnets for Programmable Single‐Cell Capture and Release

Abstract Programming magnetic fields with microscale control can enable automation at the scale of single cells ≈10 µm. Most magnetic materials provide a consistent magnetic field over time but the direction or field strength at the microscale is not easily modulated. However, magnetostrictive materials, when coupled with ferroelectric material (i.e., strain‐mediated multiferroics), can undergo magnetization reorientation due to voltage‐induced strain, promising refined control of magnetization at the micrometer‐scale. This work demonstrates the largest single‐domain microstructures (20 µm) of Terfenol‐D (Tb 0.3 Dy 0.7 Fe 1.92 ), a material that has the highest magnetostrictive strain of any known soft magnetoelastic material. These Terfenol‐D microstructures enable controlled localization of magnetic beads with sub‐micrometer precision. Magnetically labeled cells are captured by the field gradients generated from the single‐domain microstructures without an external magnetic field. The magnetic state on these microstructures is switched through voltage‐induced strain, as a result of the strain‐mediated converse magnetoelectric effect, to release individual cells using a multiferroic approach. These electronically addressable micromagnets pave the way for parallelized multiferroics‐based single‐cell sorting under digital control for biotechnology applications.

Khojah, Reem↗
DOE OSTI2023

Smart Preprocessing & Robust Integration Emulator

To achieve the desired particle size of biomass feedstocks during preprocessing for trouble-free handling and conversion to produce biofuels and bioproducts, the raw materials must undergo a crucial milling process. The particle size of biomass plays a critical role in subsequent biofuel manufacturing, where a larger area-to-volume ratio facilitates efficient synthesis while balancing the impact of moisture on biomass storage. To optimize biofuel production efficiency and overcome these challenges, it is imperative to accurately predict the particle size distribution (PSD) of the biomass in the design of efficient preprocessing systems. The population balance model (PBM), upon empirical calibration and validation, can provide rapid prediction of post-milling PSD of granular biomass. However, PSD has limitations related to mass conservation and the absence of moisture considerations. To overcome these drawbacks, a deep learning model called the enhanced deep neural operator (DNO+) is implemented in the code. This model not only retains the capabilities of the PBM in handling complex mapping functions but also incorporates additional factors influencing the system. By considering various experimental conditions such as sieve size and moisture content, the trained DNO+ model can effectively predict the PSD after milling for any given feed PSD. To further reduce the reliance on experimental data, the PBM is integrated into the DNO+ model, resulting in a physics-informed DNO+ (PIDNO+). The PIDNO+ model addresses the non-conservation of quality exhibited by the PBM while inheriting the advantages of the DNO+ model in considering multiple influencing factors. Moreover, the PIDNO+ model significantly reduces the amount of data required for model training. Both deep learning models, i.e., DNO+ and PIDNO+, are excellent in predictive performance, offering swift and accurate machine learning-based predictions. The use of this code that contains these models will assist in guiding the proper milling equipment selection and operational conditions to achieve the desired biomass particle sizes, ensuring the efficiency of subsequent biofuel and bioproduct production processes.

Xia, Yidong [Idaho National Laboratory (INL), Idah↗
NASA NTRS1995

Probes For Measuring Changing Internal Temperatures

Improved thermocouple probes devised for measuring rapidly changing temperatures within layers of solid materials. In original application, layers in question are carbon-cloth phenolic liners in solid-rocket motor nozzles, and probes inserted in layers to various depths of order of 0.5 in. measure changing temperatures in layers during hot-fire tests. Probe concept adapted to measurements of temperatures inside various other layers, materials, and components, including material test specimens.

Hunt, S. B.↗
NASA NTRS1998

Computational Materials Research

High temperature thermoplastic polyimide polymers are incorporated in engineering structures in the form of matrix materials in advanced fiber composites and adhesives in bonded joints. Developing analytical tools to predict long term performance and screen for final materials selection for polymers is the impetus for intensive studies at NASA and major industry based airframe developers. These fiber-reinforced polymeric composites (FRPCs) combine high strength with lightweight. In addition, they offer corrosion and fatigue resistance, a reduction in parts count, and new possibilities for control through aeroelastic tailoring and "smart" structures containing fully-integrated sensors and actuators. However, large-scale acceptance and use of polymer composites has historically been extremely slow. Reasons for this include a lack of familiarity of designers with the materials; the need for new tooling and new inspection and repair infrastructures; and high raw materials and fabrication costs.

Veazie, David R.↗
DOE OSTI2026

A Smart Vision-Aided RICH (Robotic Interface Control and Handling) System for VULCAN

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.

automation↗
DOE OSTI2025

Autonomous Changes in Polymer Materials Driven by Chemical Fuels

Time-dependent properties in polymer materials can be achieved through coupling to out-of-equilibrium chemical fuel reactions that mimic biological processes. Through transient changes in bonding in polymers, transient gelation, changes in mechanical stiffness, swelling, self-healing, or self-assembly can be achieved. Recent advances in these categories are discussed. These out-of-equilibrium behaviors enable applications ranging from smart adhesives to actuators for soft robotics. However, challenges remain, including waste accumulation, bio-compatibility, and achieving functionally useful performance. Addressing these issues is essential for advancing the practical use of chemically driven polymer materials and unlocking their full potential for future technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Black textile with bottom metallized surface having enhanced radiative cooling under solar irradiation

We discuss the cooling performance of garments can play an important role of enabling comfortable human activities under extreme environments. Imparting extra cooling performance to a garment in a passive way is extremely challenging under the sunlight which provides a huge energy influx to the garment, especially made of black colored textile. In this study, a solar-adaptive-textile (SAT) has been designed and experimentally demonstrated. Near-infrared (NIR) transmittance to the human skin from the solar irradiance has been intercepted by incorporating a nanoscale sputtered thin aluminum metal film underneath the textile layer facing the skin. The high-pressure sputtering employed allows a deep penetration of aluminum into the fabric structure for enhanced solar-energy-blocking effect and film stability. The aluminum layer effectively reduces the solar irradiance as well as the thermal radiation from the textile, which gets heated in lieu of the human skin. The outdoor, under-the-sun measurements with a simulated skin showed an outstanding 2 °C cooling effect compared to the normal textile without the metal film, while preserving most of the given textile properties such as colors, air permeability and wicking behavior.

36 MATERIALS SCIENCE↗
NASA NTRS2011

High-Temperature Smart Structures for Engine Noise Reduction and Performance Enhancement

One of key NASA goals is to develop and integrate noise reduction technology to enable unrestricted air transportation service to all communities. One of the technical priorities of this activity has been to account for and reduce noise via propulsion/airframe interactions, identifying advanced concepts to be integrated with the airframe to mitigate these noise-producing mechanisms. An adaptive geometry chevron using embedded smart structures technology offers the possibility of maximizing engine performance while retaining and possibly enhancing the favorable noise characteristics of current designs. New high-temperature shape memory alloy (HTSMA) materials technology enables the devices to operate in both low-temperature (fan) and high-temperature (core) exhaust flows. Chevron-equipped engines have demonstrated reduced noise in testing and operational use. It is desirable to have the noise benefits of chevrons in takeoff/landing conditions, but have them deployed into a minimum drag position for cruise flight. The central feature of the innovation was building on rapidly maturing HTSMA technology to implement a next-generation aircraft noise mitigation system centered on adaptive chevron flow control surfaces. In general, SMA-actuated devices have the potential to enhance the demonstrated noise reduction effectiveness of chevron systems while eliminating the associated performance penalty. The use of structurally integrated smart devices will minimize the mechanical and subsystem complexity of this implementation. The central innovations of the effort entail the modification of prior chevron designs to include a small cut that relaxes structural stiffness without compromising the desired flow characteristics over the surface; the reorientation of SMA actuation devices to apply forces to deflect the chevron tip, exploiting this relaxed stiffness; and the use of high-temperature SMA (HTSMA) materials to enable operation in the demanding core chevron environment. The overall conclusion of these design studies was that the cut chevron concept is a critical enabling step in bringing the variable geometry core chevron within reach. The presence of the cut may be aerodynamically undesirable in some respects, but it is present only when the chevron is not immersed in the core jet exhaust. When deployed, the gap closes as the chevron tip enters the high-speed, high-temperature core stream. Aeroacoustic testing and flow visualization support the contention that this cut is inconsequential to chevron performance.

Quackenbush, Todd R.↗
DOE OSTI2021

Hairy nanoparticles by atom transfer radical polymerization in miniemulsion

Polymer nanoparticles with various architectures and functionalities are promising materials in numerous fields. Miniemulsion polymerization is one of the suitable pathways to prepare polymer nanoparticles since each droplet could act as a “nanoreactor”. A “smart” atom transfer radical polymerization (ATRP) catalytic system comprising Cu-TPMA/DS – (TPMA = tris(2-pyridylmethyl)amine, DS – = dodecyl sulfate anion) ion pair catalyst was efficiently applied in miniemulsion ATRP at low catalyst concentrations. Herein, hairy nanoparticles consisting of hydrophobic poly(n-butyl methacrylate) (P(BMA/EGDMA), EGDMA = ethylene glycol dimethacrylate) network “core” and hydrophilic oligo(ethylene oxide) methyl ether methacrylate (OEOMA) chains as “hair” were prepared by miniemulsion ATRP and a successive chain extension by aqueous ATRP from the particle surface. The addition of an inimer, 2-(α-bromoisobutyryloxy)ethyl methacrylate (HEMA-iBBr) to the P(BMA/EGDMA) network introduced more ATRP initiation sites for ATRP of OEOMA, enabling adjustment of the particle size from >400 nm to <150 nm. The miniemulsion system remained stable after the one pot synthesis of P(BMA/EGDMA)-g-POEOMA and P(BMA/EGDMA/HEMA-iBBr)-g-POEOMA, and the resulting polymer nanoparticles were re-dispersed in water for further modification. A zwitterionic monomer, [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA), was grafted from P(BMA/EGDMA/HEMA-iBBr)-g-POEOMA. The resulting hairy nanoparticles provide an avenue for the design and preparation of novel nanostructured materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Novel Temperature Sensors and Wireless Telemetry for Active Condition Monitoring of Advanced Gas Turbines

The objective of the program is to develop and engine test hardware and software technologies that will enable active condition monitoring to be implemented on hot gas path turbine blades in large industrial gas turbines. The specific objectives are (1) to fabricate and install Smart Turbine Blades with thermally sprayed sensors and high temperature wireless telemetry systems in a gas turbine engine, (2) to integrate the component engine test data with remaining useful life (RUL) models and develop an approach for networking the component RUL data with Siemens' Power Diagnostics® engine monitoring system. These significant advances carried out in Phase 1 in temperature wide bandgap telemetry, along with new induced power driver and receiver geometry combined with an innovative approach to transmit digital data wirelessly will enable the opportunity to proceed with more technical innovation. The Phase 2 program focused on validation testing of sensor-wireless telemetry package in spin rig and advanced operation-based assessment (OBA) model utilizing artificial intelligence. Significant efforts were dedicated on the download of the technology onto components to be tested an actual gas turbine engine for full realization of active condition monitoring for Smart Turbine Blades.

03 NATURAL GAS↗
DOE OSTI2024

Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions

Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H 2 ) storage likewise holds strong potential, though with distinct challenges and mechanisms. H 2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H 2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H 2 storage systems by optimizing H 2 adsorption at the nanoscale and improving the kinetics of H 2 uptake and release. We discuss various H 2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H 2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H 2 storage, particularly in prototype vehicles, from a techno-economic perspective. In conclusion, this forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand.

25 ENERGY STORAGE↗
DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗