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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 973 records · Page 54

pH- and Redox-Responsive Pickering Emulsions Based on Cellulose Nanocrystal Surfactants

Nanoparticle surfactants (NPSs), formed by using dynamic interactions between nanoparticles and complementary ligands at the liquid-liquid interface, have emerged as “smart emulsifiers” with attributes of high emulsification efficiency, long-term stability, and on-demand emulsification/demulsification capabilities. However, only pH-responsiveness can be adopted for the assembly of reported NPSs formed by electrostatic interactions. Here, we propose an alternative design strategy, by taking advantage of the ferrocenium (Fc + ) sulfate ion pair, to develop a new type of cellulose nanocrystal (CNC) surfactant. In conclusion, the Fc + groups are sensitive to pH, redox reagents and voltage, imparting the CNC surfactants and derived Pickering emulsions with multi-stimuli-responsiveness, and showing promising applications in controllable delivery, release, and biphasic biocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spatiotemporal Control over Polynucleotide Brush Growth on DNA Origami Nanostructures

DNA nanotechnology provides an approach to create precise, tunable, and biocompatible nanostructures for biomedical applications. However, the stability of these structures is severely compromised in biological milieu due to their fast degradation by nucleases. Recently, we showed how enzymatic polymerization could be harnessed to grow polynucleotide brushes of tunable length and location on the surface of DNA origami nanostructures, which greatly enhances their nuclease stability. Here, in this study, we report on strategies that allow for both spatial and temporal control over polymerization through activatable initiation, cleavage, and regeneration of polynucleotide brushes using restriction enzymes. The ability to site‐specifically decorate DNA origami nanostructures with polynucleotide brushes in a spatiotemporally controlled way provides access to “smart” functionalized DNA architectures with potential applications in drug delivery and supramolecular assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improving Miscibility of Polymer Donor and Polymer Acceptor by Reducing Chain Entanglement for Realizing 18.64 % Efficiency All Polymer Solar Cells

All-polymer solar cells have experienced rapid development in recent years by the emergence of polymerized small molecular acceptors (PSMAs). However, the strong chain entanglements of polymer donors (P D s) and polymer acceptors (P A s) decrease the miscibility of the resulting polymer mixtures, making it challenging to optimize the blend morphology. Herein, we designed three P A s, namely PBTPICm-BDD, PBTPICγ-BDD and PBTPICF-BDD, by smartly using a BDD unit as the polymerized unit to copolymerize with different Y-typed non-fullerene small molecular acceptors (NF-SMAs), thus achieving a certain degree of distortion and giving the polymer system enough internal space to reduce the entanglements of the polymer chains. Such effects increase the chances of the P D being interspersed into the acceptor material, which improve the solubility between the P D and P A . The PBTPICγ-BDD and PBTPICF-BDD displayed better miscibility with PBQx-TCl, leading to a well optimized morphology. As a result, high power conversion efficiencies (PCEs) of 17.50 % and 17.17 % were achieved for PBQx-TCl : PBTPICγ-BDD and PBQx-TCl : PBTPICF-BDD devices, respectively. In conclusion, with the addition of PYFT-o as the third component into PBQx-TCl : PBTPICγ-BDD blend to further extend the absorption spectral coverage and finely tune microstructures of the blend morphology, a remarkable PCE of 18.64 % was realized finally.

36 MATERIALS SCIENCE↗

Rewritable Surface‐Grafted Polymer Brushes with Dynamic Covalent Linkages

Abstract Surface grafting of polymer brushes drastically modifies surface properties, including wettability, compatibility, responsiveness, etc. A broad variety of functionalities can be introduced to the surface via different types of polymers. Bringing together properties of two or more types of polymer brushes to one surface opens up even more possibilities in brush‐modified materials. However, while it is generally feasible to introduce several chemical compositions along the brushes via copolymerization, it is challenging to vary the types of polymer brushes along a surface. Although previous studies have demonstrated binary brushes via orthogonal polymerization techniques or partial deactivation/regrafting, they commonly limit the number of polymer types to two. Here, we propose a strategy to introduce dynamic covalent diketoenamine linkages at the root of polymer brushes. The grafting density could be precisely tuned during surface functionalization. The surface‐anchored polymer brushes were cleaved by the addition of small molecule amines. New polymer brushes can be regrafted from the surface following refunctionalization of exposed sites. The maneuverability allows tuning of the types and densities of the polymer brushes, pointing the way to the preparation of a new generation of well‐defined brush‐modified materials with mixed grafts, with potential applications in the design of smart materials and surfaces.

Cui, Feichen↗

Direct Synthesis of Layer-Tunable and Transfer-Free Graphene on Device-Compatible Substrates Using Ion Implantation Toward Versatile Applications

Direct synthesis of layer-tunable and transfer-free graphene on technologically important substrates is highly valued for various electronics and device applications. State of the art in the field is currently a two-step process: a high-quality graphene layer synthesis on metal substrate through chemical vapor deposition (CVD) followed by delicate layer transfer onto device-relevant substrates. Here, we report a novel synthesis approach combining ion implantation for a precise graphene layer control and dual-metal smart Janus substrate for a diffusion-limiting graphene formation to directly synthesize large area, high quality, and layer-tunable graphene films on arbitrary substrates without the post-synthesis layer transfer process. Carbon (C) ion implantation was performed on Cu–Ni film deposited on a variety of device-relevant substrates. A well-controlled number of layers of graphene, primarily monolayer and bilayer, is precisely controlled by the equivalent fluence of the implanted C-atoms (1 monolayer ~4 × 10 15 C-atoms/cm 2 ). Upon thermal annealing to promote Cu-Ni alloying, the pre-implanted C-atoms in the Ni layer are pushed toward the Ni/substrate interface by the top Cu layer due to the poor C-solubility in Cu. As a result, the expelled C-atoms precipitate into a graphene structure at the interface facilitated by the Cu-like alloy catalysis. After removing the alloyed Cu-like surface layer, the layer-tunable graphene on the desired substrate is directly realized. The layer-selectivity, high quality, and uniformity of the graphene films are not only confirmed with detailed characterizations using a suite of surface analysis techniques but more importantly are successfully demonstrated by the excellent properties and performance of several devices directly fabricated from these graphene films. Molecular dynamics (MD) simulations using the reactive force field (ReaxFF) were performed to elucidate the graphene formation mechanisms in this novel synthesis approach. With the wide use of ion implantation technology in the microelectronics industry, this novel graphene synthesis approach with precise layer-tunability and transfer-free processing has the promise to advance efficient graphene-device manufacturing and expedite their versatile applications in many fields.

36 MATERIALS SCIENCE↗

Bio‐Based Piezo‐ and Thermoresistive Photocurable Sensing Materials from Acrylated Epoxidized Soybean Oil

Abstract Bio‐based photocurable polymers are increasingly in demand as environmentally friendly materials for advanced applications. Together with functional fillers, these represent a next step for the generation of functional and active smart materials, compatible with additive manufacturing technologies. Herein, acrylated epoxidized soybean oil (AESO) mixed with different amounts of reduced graphene oxide (rGO) up to 6 wt% in order to obtain UV‐curable piezoresistive and thermoresistive materials, is reported. It is shown that the addition of rGO to AESO hinders the curing process, but always maintains double bond conversions higher than 50%. Composites are characterized by a good dispersion of micrometric filler clusters. Further, the thermal stabilities are close to 300 °C and crosslinking degrees are above 1.75 mmol cm –3 . The Young modulus of the composites decreases with the addition of the rGO fillers, in particular for the higher filler contents, and electrical conductivities up to 0.13 S m –1 are obtained for the composites with the highest rGO content. UV‐curable composites with piezoresistive and thermoresistive responses suitable for applications are thus obtained, characterized by gauge factors around 26 for deformations up to 2% and maximum thermoresistive sensitivity of S = 0.43, values similar to the values obtained for petroleum‐based materials.

Mendes‐Felipe, Cristian↗

Structure, properties, and applications of polyacrylonitrile/carbon nanotube ( CNT ) fibers at low CNT loading

Abstract Bi‐component, polyacrylonitrile (PAN)/carbon nanotube (CNT) fibers were processed, at different core‐sheath area ratios, by gel spinning. A percolated CNT network at 10 wt% CNT in the sheath enhanced electrical conductivity as compared to the neat PAN fiber, while PAN polymer in the core contributed to the good mechanical properties. Fibers with relatively thin sheath allowed overall CNT loading as low as 3.7 wt% to be made with good electrical conductivity, and PAN stabilization by Joule heating was demonstrated. Such fibers with combined good mechanical properties and electrical conductivity can also potentially be used for electrical heating of fabrics, for making smart textiles, and for electromagnetic interference shielding.

Lu, Mingxuan↗

Dielectric Engineering of ZnO@ZIF‐8 for High‐Performance Triboelectric Nanogenerators and Self‐Powered Humidity Sensors

Porous metal oxide–metal-organic framework (MO x @MOF) hybrids offer synergistic effects that enhance surface charge density, electronic structure, and textural properties, making them ideal for self-powered sensing applications. Here, uniform, and pinhole-free ternary porous ZnO–PTFE@ZIF-8 hybrid films are developed on room-temperature co-sputtered ZnO–polytetrafluoroethylene (PTFE) composite films, where ZnO serves as a self-sacrificial precursor to precisely regulate ZIF-8 (Zn-MeIm 2 ) growth through solvothermal methods. The resulting TENG achieves a high output power density of 0.67 mW cm −2 , attributed to the synergistic effects of fluorine-rich PTFE and methyl-functionalized ZIF-8, which enhance surface charge density and dielectric response. By tuning PTFE content in the ZnO–PTFE composite, the dielectric constant and triboelectric output is optimized. The ZP60@ZIF-8-based device also demonstrates excellent humidity sensing performance, with a wide detection range (20–99% RH) and ultrahigh sensitivity (R V % of 19 900%, R I % of 19 325% at 99% RH). These results position ZP@ZIF-8-based TENGs as promising platforms for next-generation self-powered sensors and smart wearable electronics.

42 ENGINEERING↗

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↗

Robust multi‐loop control of a floating wind turbine

Abstract A principal challenge facing the control of floating offshore wind turbines (FOWTs) is the problem of instability, or “negative damping,” when using blade pitch feedback to control generator speed. This closed‐loop instability can be attributed to non‐minimum phase zeros in the transfer function from blade pitch to generator speed. Standard approaches to improving stability and performance include robust tuning of control gains and introducing multiple feedback loops to respond to platform motion. Combining these approaches is nontrivial because multiple control loops complicate the impact of coupling in the system dynamics. The single‐loop approach to analyzing stability robustness neglects inter‐loop coupling, while a simplistic multi‐loop approach is highly sensitive to dimensional scaling and overestimates the robustness of the single‐loop controller. This work proposes a sensitivity representation that separates some of the natural FOWT dynamic coupling into a parallel feedback loop in the sensitivity function loop to address both of these concerns. The modified robustness measure is used with a simplified linear FOWT model to optimize scheduled multi‐loop control parameters in an automated tuning procedure. This controller is implemented for the 10‐MW Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) and compared against conventional single‐ and multi‐loop controllers tuned using frequency‐domain analysis and high‐fidelity OpenFAST simulations. The multi‐loop robust controller shows the highest overall performance in generator speed regulation and tower load reduction, though consideration of power quality, actuator usage, and other structural loading leads to additional trade‐offs.

Stockhouse, David↗

Dynamic Line Rating Models and Their Potential for a Cost‐Effective Transition to Carbon‐Neutral Power Systems

Most transmission system operators (TSOs) currently use seasonally steady-state models considering limiting weather conditions that serve as reference to compute the transmission capacity of overhead power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions, and the degradation of lines in a cost-effective way. DLR can also be used in the long run in grid extension and new power capacity planning. In the short run, it should be used to help operate power systems with congested lines. The operation of the power systems is planned to have the market trading into account; thus, it computes transactions hours ahead of real-time operation, using power flow forecasts affected by large errors. In the near future, within a “smart grid” environment, in real-time operation conditions, TSOs should be able to rapidly compute the capacity rating of overhead lines using DLR models and the most reliable weather information, forecasts, and line measurements, avoiding the current steady-state approach that, in many circumstances, assumes ampacities above the thermal limits of the lines. Here, this work presents a review of the line rating methodologies in several European countries and the United States. Furthermore, it presents the results of pilot projects and studies considering the application of DLR in overhead power lines, obtaining significant reductions in the congestion of internal networks and cross-border transmission lines.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Microgrid's Role in Enhancing the Security and Flexibility of City Energy Systems

Smart cities depend on flexible and secure energy systems to ensure resilient power for critical infrastructure; however, recent weather-related events and cyberattacks have highlighted weaknesses in our energy systems, with the potential for widespread economic and security impacts. As stated by the Executive Office of the President, "the resilience of the US electric grid is a key part of the nation's defense against severe weather." To address the energy delivery security challenge, microgrids are rising as a viable solution that enhances the flexibility and resilience of the distribution grid and boosts the reliability of the local supply for the end-user. Traditionally, high capital investment has been a barrier to large-scale adoption of microgrid technology. Understanding the flexibility and resilience benefits of microgrids and accounting for the associated value streams can make the microgrid's proposition economically viable. In this chapter, microgrids' utility and their potential to serve as a flexible and resilient resource for the utility grid by providing capabilities such as peak shaving, demand response, and frequency regulation is presented. Moreover, other value streams, such as (1) their ability to island during a disaster and sustain critical loads which makes them a robust resilience solution for end-users, in the event of the utility grid outage and (2) microgrids also provide a flexible platform for integrating distributed energy resources in conjunction with storage and conventional generation technologies, strengthen microgrid's role in reducing the over-arching goal of emission reduction. Given the myriad of benefits associated with microgrids, we present strategies which can be employed for making microgrid itself resilient against physical and cyberthreats by employing hardware, software, and personnel training solutions to operate the microgrid before, during, and after a potential disaster. This chapter, thus, provides a holistic study of the microgrid as a resilience resource, for the utility grid, and a self-contained end-user for the end-user.

cyber-physical system↗

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.

97 MATHEMATICS AND COMPUTING↗

Data Center Facility Monitoring with Physics Aware Approach

U.S. Department of Energy's National Renewable Energy Laboratory (NREL) hosts one of the world's most energy-efficient HPC data centers; this system uses component-level warm-water liquid cooling to efficiently remove heat from the data center and capture it for reuse in the building or rejection to the atmosphere. Given the complexity of this system, building data-driven tools for holistically monitoring and operating the entire data center is a priority for ensuring maximal efficiency and resiliency. In this advanced smart facility, over one million metrics are recorded per minute using state-of-the-art streaming data architecture and software to capture and process the state of the system in real time. Here we detail two efforts to effectively analyze, visualize, and interpret this large volume streaming data. We have developed a novel, flexible system for identifying and visualizing individual metric anomalies and component performance across the data center through automatic metadata extraction and physically-motivated visualization for quick interpretation. Additionally, to directly connect system maintenance to data stream processing we explore a physics informed multi-metric drift and anomaly detection application to detect scale-build up in heat exchangers.

anomaly detection↗

The INTERSECT Open Federated Architecture for the Laboratory of the Future

A federated instrument-to-edge-to-center architecture is needed to autonomously collect, transfer, store, process, curate, and archive scientific data and reduce human-in-the-loop needs with (a) common interfaces to leverage community and custom software, (b) pluggability to permit adaptable solutions, reuse, and digital twins, and (c) an open standard to enable adoption by science facilities world-wide. The Selfdriven Experiments for Science/Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with autonomous experiments, “self-driving” laboratories, smart manufacturing and artificial intelligence (AI) driven design, discovery and evaluation. It creates an open federated architecture for the laboratory of the future using a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture.

Engelmann, Christian↗

In situ embedment of type K sheathed thermocouples with directed energy deposition

Advanced nuclear reactor systems require new technologies for heat transfer and system monitoring. Additive manufacturing (AM) offers the design flexibility to allow in-situ sensor embedment through smart manufacturing for real-time monitoring, and performance of these systems. Here, this study focuses on experiments investigating the feasibility of in-situ sensor embedment using directed energy deposition (DED). Type K thermocouples are embedded into 316L stainless steel (SS) samples using two different configurations (e.g., exposed and embedded tips) and two designs (e.g., flush to substrate) within an AM base. Embedded sensor samples are analyzed via in-situ measurements and high-temperature performance validation tests at 350ºC and 900ºC. Temperature performance results at both temperature tests show good agreement with manufacturer specifications proving that these sensors could still capture accurate temperature readings after in-situ embedment during DED processing. An additional optimization experiment was conducted on the exposed tip configuration using a surrogate thermocouple to improve tolerances and the embedment process. Results improved tolerances, lower porosity, smaller gaps between the sensor and base, and better junction contact for the sensor. Although further optimization of this embedment strategy is necessary to improve the structural stability and tolerances within the component, this research strategy provides a proof-of-feasibility for DED embedment with commercial sheathed thermocouples. This research provides early impact on embedment of sensor for multiple materials and complex geometric components..

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Weinberg’s Proposal of 1990: A Very Personal View

My personal encounter with Weinberg's proposal of 1990 was a really entertaining one: My collaborator David Entem and I had embarked to show that Weinberg's idea, though smart and beautiful, was essentially useless in practice (like so many of those genius ideas of the 1980s where people claimed to have "derived the nuclear force from QCD"). Furthermore, in trying to do so, we showed the opposite; namely, we showed that Weinberg's idea worked better than allowed by any reasonable means.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK

Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Results show that electricity consumption is influenced by a broader range of factors compared to gas. Household energy use is best explained by physical dwelling characteristics, socio-demographic variables, and certain behavioural and attitudinal measures. Notably, pro-environmental attitudes, frugality, and conscientiousness correlate with lower energy use, while income and consumerism are linked to higher consumption. We discuss how these findings can inform efforts to decarbonise home energy use in the UK.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗