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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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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Molecular-Level Control over Ionic Conduction and Ionic Current Direction by Designing Macrocycle-Based Ionomers

Poor ionic conductivity of the catalyst-binding, submicrometer- thick ionomer layers in energy conversion and storage devices is a huge challenge. However, ionomers are rarely designed keeping in mind the specific issues associated with nanoconfinement. Here, we designed nature-inspired ionomers (calix-2) having hollow, macrocyclic, calix[4]arene-based repeat units with precise, sub-nanometer diameter. In ≤100 nm-thick films, the in-plane proton conductivity of calix-2 was up to 8 times higher than the current benchmark ionomer Nafion at 85% relative humidity (RH), while it was 1-2 orders of magnitude higher than Nafion at 20-25% RH. Confocal laser scanning microscopy and other synthetic techniques allowed us to demonstrate the role of macrocyclic cavities in boosting the proton conductivity. The systematic self-assembly of calix-2 chains into ellipsoids in thin films was evidenced from atomic force microscopy and grazing incidence small-angle X-ray scattering measurements. Moreover, the likelihood of alignment and stacking of macrocyclic units, the presence of one-dimensional water wires across this macrocycle stacks, and thus the formation of long-range proton conduction pathways were suggested by atomistic simulations. We not only did see an unprecedented improvement in thin-film proton conductivity but also saw an improvement in proton conductivity of bulk membranes when calix-2 was added to the Nafion matrices. Nafion-calix-2 composite membranes also took advantage of the asymmetric charge distribution across calix[4]arene repeat units collectively and exhibited voltage-gating behavior. The inclusion of molecular macrocyclic cavities into the ionomer chemical structure can thus emerge as a promising design concept for highly efficient ion-conducting and ion-permselective materials for sustainable energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tailoring the Selective Oxidation of Hydroxyl-Containing Compounds via Precisely Tuning the Hydrogen-Bond Strength of Catalyst H-Bond Acceptors

The unique performance of the enzyme is mainly achieved via weak interactions between the “outer coordination sphere” and the substrate. Inspired by this process, we developed 3D encapsulated-structure catalysts with hydrogen-bond engineering on the shell, which mimics the “outer coordination sphere” of an enzyme. Various hydrogen bond acceptors (C=O, S=O, and N–O groups) are imparted in the shell. Concentration-dependent 1H NMR, inverse-phase gas Chromatography (IGC) measurements, and DFT calculations underscore that the hydrogen bond strength between the acceptor groups and alcohol follows the order of C=O < S=O < N–O. The hydroxyl compound oxidation rate vs the hydrogen bond strength follows a volcano behavior, reminiscent of Sabatier’s principle. The performance variation among catalysts is attributed to the adsorption strength of the substrate. The proposed bioinspired design principle expands the scope of encapsulated catalysts, enabling fine regulation of catalytic activity through precise microenvironment control via weak interactions with substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training

Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train in the context of spiking networks with various ratios of excitatory to inhibitory neurons. The results indicate networks with biologically-realistic excitatory:inhibitory ratios can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

bio-inspired computing↗
DOE OSTI2020

Neuromorphic spintronics

Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems.

42 ENGINEERING↗
DOE OSTI2024

Mechanochemical topological defects in an active nematic

We propose a reaction-diffusion system that converts topological information of an active nematic into chemical signals. We show that a curvature-activated reaction dipole is sufficient for creating a system that dynamically senses topology by producing a concentration field possessing local extrema coinciding with ±$\frac{1}{2}$ defects. The enabling term is analogous to polarization charge density seen in dielectric materials. We demonstrate the ability of this system to identify defects in both passive and active nematics. Our results illustrate that a relatively simple feedback scheme, expressed as a system of partial differential equations, is capable of producing chemical signals in response to inherently nonlocal structures in anisotropic media. Here, we posit that such coarse-grained systems can help generate testable hypotheses for regulated processes in biological systems, such as morphogenesis, and motivate the creation of bio-inspired materials that utilize dynamic coupling between nematic structure and biochemistry.

42 ENGINEERING↗
DOE OSTI2023

Efficient human activity recognition with spatio-temporal spiking neural networks

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks

Recent years have seen an increased importance of neural network inference in edge-based scenarios, which impose size and power constraints requiring novel computing devices. These same edge scenarios may require operating over long periods of time, or exposure to extreme environments, resulting in a drift of neural network weights that cause degraded performance. In searching for ways to develop neural network approaches that perform robustly under these conditions, we propose a biologically-inspired mechanism for the dynamic adaptation of within-neuron parameters that is guided by a global context signal carrying information about perturbations and variability in incoming stimuli. Specifically, we demonstrate that adaptive voltage thresholds or neuronal time constants, when informed by a global context signal, can enable network-level mechanisms to recover from perturbed synaptic weights. Consistent with prior literature, the context-modulated approach is effective for recurrent, but not feedforward networks, by modulating network level dynamics. We demonstrate this approach successfully recovers performance in image classification tasks and spatiotemporal tracking tasks under idealized and Gaussian noise as well as for realistic perturbations from a memristive device when exposed to ionizing radiation. Finally, we discuss how this approach enables the design of robust and energy-efficient neuromorphic systems that perform well, even in resource-constrained scenarios with extreme environments such as edge processing.

context modulation↗
NASA NTRS2006

Biomimetic actuators using electroactive polymers (EAP) as artificial muscles

Evolution has resolved many of nature's challenges leading to lasting solutions with maximal performance and effective use of resources. Nature's inventions have always inspired human achievements leading to effective materials, structures, tools, mechanisms, processes, algorithms, methods, systems and many other benefits. The field of mimicking nature is known as Biomimetics and one of its topics includes electroactive polymers that gain the moniker artificial muscles. Integrating EAP with embedded sensors, self-repair and many other capabilities that are used in composite materials can add greatly to the capability of smart biomimetic systems. Such development would enable fascinating possibilities potentially turning science fiction ideas into engineering reality.

Electroactive Polymers (EAP)↗
NASA NTRS2007

Artificial Muscles Based on Electroactive Polymers as an Enabling Tool in Biomimetics

Evolution has resolved many of nature's challenges leading to working and lasting solutions that employ principles of physics, chemistry, mechanical engineering, materials science, and many other fields of science and engineering. Nature's inventions have always inspired human achievements leading to effective materials, structures, tools, mechanisms, processes, algorithms, methods, systems, and many other benefits. Some of the technologies that have emerged include artificial intelligence, artificial vision, and artificial muscles, where the latter is the moniker for electroactive polymers (EAPs). To take advantage of these materials and make them practical actuators, efforts are made worldwide to develop capabilities that are critical to the field infrastructure. Researchers are developing analytical model and comprehensive understanding of EAP materials response mechanism as well as effective processing and characterization techniques. The field is still in its emerging state and robust materials are still not readily available; however, in recent years, significant progress has been made and commercial products have already started to appear. In the current paper, the state-of-the-art and challenges to artificial muscles as well as their potential application to biomimetic mechanisms and devices are described and discussed.

electroactive polymers↗
NASA NTRS2018

Biocene 2018 - Welcome to the Period of New Life

Biocene is the period of new life. When our descendants look back at this period in time, they will see evidence, in the geologic and electronic record, of anthropic climate change, growing population, and scarcity of resources. But they will also see the rebirth of human ingenuity as we overcame the challenges that faced us through nature-inspired exploration. The Periodic Table of Life (PeTaL) is a proposed tool and open source framework that uses artificial intelligence to aid in the systematic inquiry of biology for its application to human systems. This presentation defines the PeTaL concept and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields have been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. The primary objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence in problem solving. Specifically specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. Blockchain technology may be vital in allowing open source design tools such as PeTaL to democratize design and yet protect intellectual property. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools include text classification, thesaurus, data visualization, and analysis. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life.

Biocene↗
NASA NTRS2016

Auto-Gopher-2 - Wireline Deep Sampler Driven by Percussive Piezoelectric Actuator and Rotary EM Motors

Two of the key purposes of future NASA’s solar system exploration of planetary bodies are the search for potentially preserved bio-signatures and for habitable regions. To address these objectives, a biologically inspired wireline deep rotary-percussive drill, called Auto-Gopher, has been developed. This drill employs a piezoelectric-actuated percussive mechanism for generating impulsive stresses and breaking formations, and an electric motor to rotate the bit to break material and remove the cuttings. Initially, the drill was designed as percussive mechanism for sampling ice and was demonstrated in 2005 at Lake Vida, Antarctica, reaching about 2 meters depth. The lessons learned suggested there is a need to augment the percussive action with bit rotation in order to maximize the penetration rate. The first generation implementation of the rotary augmentation was focused on the demonstration of this capability. In 2012, during the 3-day field test, the drill reached a 3-meter deep in gypsum. A separate mechanism was used to break and remove the cores. The average drilling power consumption was in the range of 100-150 Watts, while the rate of penetration was approximately 2.4 meters per hour. Currently under development is the second-generation drill, called Auto-Gopher-2. The drill will be fully autonomous. In this paper, the capabilities that are being integrated into the Auto-Gopher-2 are described and discussed.

Auto-Gopher Drill↗
NASA NTRS2016

Humanoids and the Potential Role of Electroactive Materials/Mechanisms in Advancing Their Capability

Humanoids are increasingly becoming capable biologically inspired robots that are appearing and behaving lifelike. Making humanlike robots is the ultimate challenge to biomimetics and, while for many years they were considered a science fiction, such robots are increasingly becoming engineering reality. Progress in producing such robots are allowing them to perform impressive functions and tasks. In 2012, in an effort to promote significant advances in developing humanoids, DARPA (Defense Advanced Research Projects Agency) posed a Robotic Challenge to produce such robots that operate in disaster scenarios towards making society more resilient. The challenge was focused on the requirements that have been needed after the Fukushima accident in Japan, hoping to advance the field of disaster robotics. This disaster posed significant challenges to emergency responders since radiation prevented people from going into the station and venting the explosive gas. Another significant development in this field is the fact that major US corporations have entered into the race to produce commercial humanoids. As a result, one can expect significant and rapid progress in this field. Developing humanoids is critically dependent of the use of highly efficient, compact, lightweight actuators and electroactive materials are offering great potential. This paper reviews the state-of-theart of humanlike robots, potential applications and challenges, as well as the actuation materials that are used or could be used.

Electroactive Polymer Actuators↗
NASA NTRS2016

Humanoids and the Potential Role of Electroactive Materials/Mechanisms in Advancing Their Capability

Humanoids are increasingly becoming capable biologically inspired robots that are appearing and behaving lifelike. Making humanlike robots is the ultimate challenge to biomimetics and, while for many years they were considered a science fiction, such robots are increasingly becoming engineering reality. Progress in producing such robots are allowing them to perform impressive functions and tasks. In 2012, in an effort to promote significant advances in developing humanoids, DARPA posed a Robotic Challenge to produce such robots that operate in disaster scenarios towards making society more resilient. The challenge was focused on the requirements that have been needed after the Fukushima accident in Japan, hoping to advance the field of disaster robotics. This disaster posed significant challenges to emergency responders since radiation prevented people from going into the station and venting the explosive gas. Another significant development in this field is the fact that major US corporations have entered into the race to produce commercial humanoids. As a result, one can expect significant and rapid progress in this field. Developing humanoids is critically dependent of the use of highly efficient, compact, lightweight actuators and electroactive materials are offering great potential. This paper reviews the state-of-theart of humanlike robots, potential applications and challenges, as well as the actuation materials that are used or could be used.

Electroactive Polymer Actuators↗
NASA NTRS2021

Bioinspired Honeycomb Core Design: An Experimental Study of the Role of Corner Radius, Coping, and Interface

The honeybee’s nest has inspired the design of engineering honeycomb core that primarily abstract the hexagonal cell shape and exploit its mass minimizing properties to construct lightweight panels. This work explored three additional design features that are part of the natural honeybee comb but have not been traditionally considered as design features of interest in honeycomb design: the radius at the corner of each cell, the coping at the top of the cell walls, and the interface between cell arrays. These features were first characterized in natural honeycomb using optical and X-ray techniques, and then incorporated into honeycomb core design, and fabricated using an additive manufacturing process. The honeycomb cores were then tested in out-of-plane compression and bending, and since all three design features added mass to the overall structure, all metrics were examined per unit mass to assess performance gains. The study concluded that the addition of an interface increases specific flexural modulus but has little benefit in out-of-plane compression; coping radius positively impacts specific flexural strength; but that the corner radius has no significant effect in bending, and actually is slightly detrimental for out-of-plane compression testing.

honeycomb↗
DOE OSTI2020

Spanning the Nexus: Integrated Energy Research on Agriculture & Water Challenges

This presentation will explore the land, food, water, and energy implications of changing energy systems. With an increase in solar development, there are potential conflicts between land use for solar and agricultural. Under the DOE’s InSPIRE project, researchers are gathering data and performing experiments for the co-location of solar development and the agricultural production of crops, pollinator habitat, and grazing. Initial findings from this project highlight mutual benefits for both farmers and the PV installation themselves, all while providing an avenue to reduce land use conflicts. Also, in this presentation, the intersection of water and energy systems will be explored. Energy production requires water and water treatment and transport requires energy. This presentation will highlight past work examining oil and gas water use and detail upcoming water treatment research within the National Water Alliance for Innovation (NAWI).

agriculture↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗