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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 163 records · Page 9

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↗

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗

Process-driven roadmap for depositing super duplex stainless steel via wire Arc additive manufacturing

Here, this study systematically investigates the effects of shielding gas, bead spacing, weld mode, and travel speed on the phase balance, porosity, and hardness of wire arc additively manufactured (WAAM) super duplex stainless steel (ER2594). Robotic WAAM was employed to fabricate multilayer walls under systematically varied process conditions, followed by phase transformation simulations, X-ray computed tomography (XCT), electron backscatter diffraction (EBSD), and microhardness evaluation. Thermodynamic simulations predicted rapid cooling of AM process can suppress the potential formation of deleterious precipitates which was later validated via cross-sectional microstructure analyses of printed samples. XCT revealed porosity levels below 0.2% for all deposits, with reduced travel speed significantly lowering defect volume. Microstructural analyses revealed the evolution of various austenite precipitates, including grain boundary austenite (GBA), Widmanstätten austenite (WA), and intergranular austenite (IGA), sequentially upon cooling of the ferrite phase. Among all process parameters, weld transfer mode exhibited the strongest influence on phase balance; pulsed mode promoted higher ferrite retention (~ 36%) compared to RapidX mode. No consistent relationship between stabilized phase fraction and captured microhardness was observed. This work provides critical insights for optimizing WAAM parameters to control phase balance and mechanical performance, which is essential for producing high-integrity super duplex stainless-steel components for nuclear and marine applications.

Grain orientation↗

Simultaneous optimal system and controller design for multibody systems with joint friction using direct sensitivities

Abstract Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Verulkar, Adwait↗

Biofoundries: Principles, Tools, and Applications

This chapter aims to provide a broad overview of biofoundries and introduces the principles, concepts, and case studies. We first outline the underlying principles of the Design-Build-Test-Learn (DBTL) framework and the role of automation, digital integration, and standardization. The chapter then explores core biofoundry technologies including robotic liquid handlers, high-throughput analytical instruments, and digital infrastructure for data management and workflow scheduling. Case studies spanning DNA assembly, protein engineering, metabolic engineering, and mammalian cell culture demonstrate the practical applications of the biofoundries. Economic and societal impacts are assessed alongside current limitations. We discuss the emerging trends including artificial intelligence integration and cloud-based distributed facilities to highlight its potential for biotechnology and the bioeconomy.

Singh, Nilmani↗

Future foundries: A convergent manufacturing platform

This article introduces the Future Foundries platform developed at Oak Ridge National Laboratory, a first-generation research system designed to demonstrate convergent manufacturing. Convergent manufacturing brings together additive, subtractive, and transformative processes in a digitally interconnected environment to enable end-to-end production workflows. By linking traditionally discrete steps, convergent platforms accelerate production, improve repeatability, and support high-mix, low-volume manufacturing. The Future Foundries platform exemplifies this vision in practice by combining four modular, vendor-agnostic process cells that include robotic WAAM, induction heating, optical metrology, and machining, coordinated through an automated pallet handler and a ROS 2-based digital thread. This architecture provides the flexibility and scalability needed for agile production in small and medium-sized manufacturing enterprises and for field deployable manufacturing. Two case studies illustrate the platform’s capabilities. The first presents an integrated workflow for fabricating, transforming, and repairing critical replacement components, showing how consolidated thermal, additive, inspection, and machining operations reduce manual part handling and streamline process flow. The second case study highlights coordinated multi-part production enabled by automated pallet logistics and multi-cell scheduling. Together, these examples showcase convergent manufacturing as a practical and scalable strategy for strengthening domestic casting and forging capacity, improving supply-chain resilience, and enabling rapid, adaptable production of mission-critical components.

Convergent manufacturing↗

High-throughput measurements of CO 2 permeance and solubility in ionic liquid reveal a synergistic role of ionic interactions and void fractions

The factors that govern CO 2 solubility in ionic liquids (ILs) are of great interest for the development of new materials for CO 2 capture and utilization. The cationic functional group (i.e., imidazolium, pyrrolidinium, pyridinium, etc.), alkyl chain length of cation, degree of fluorination of anion, anion size, and the void fraction in IL are known to influence CO 2 solubility. However, a comprehensive explanation of how these factors collectively affect CO 2 solubility has not been developed yet. This knowledge gap is largely attributed to the lack of CO 2 solubility data for IL structures other than imidazolium based ILs. We report here an automated high-throughput (HT) setup for the measurement of CO 2 solubility in room-temperature ILs (RTILs) combining six different anions and nine different cations for a total of 19 different specific ranges of RTILs. The HT setup first dispenses up to 200 µL of RTILs in a 96-well microtiter plate and then utilizes a robotic arm to measure cyclic voltammogram (CV) in each well using maneuverable Ag electrodes. The Cottrell analysis of the CO 2 reduction CV peak provides a direct measurement of CO 2 permeance in RTILs, which yields Henry’s constant from the estimated diffusion coefficient of CO 2 . Henry’s constants thus obtained are in very good agreement with those reported earlier. The measured CO 2 permeance and Henry’s constant of all RTILs seem to follow a first-order dependence on void fraction and a second-order dependence on electrostatic interaction between anion and cation of IL, with some synergistic dependence on the product of a void fraction and electrostatic interaction, making them two important descriptors for the design of novel ILs.

CO2 Solubility↗

An optimization-based approach to tailor the mechanical response of soft metamaterials undergoing rate-dependent instabilities

An optimization-based design framework is proposed to tune the response of soft metamaterials involving both geometric instabilities and nonlinear viscoelastic material behavior. Designing the response of soft metamaterials to harness instabilities and undergo large, tailored configuration changes will enable advancements in soft robotics, shock and vibration mitigation, and flexible electronics. In line with the metamaterial concept, the response of these materials is governed to a large extent by the geometric and topological makeup of their small-scale features. However, the link between structure and response is less intuitive for soft metamaterials due to their reliance upon highly nonlinear responses triggered by geometric instabilities. This is further complicated by the effects of viscoelastic relaxation, which recent studies have shown to alter the emergence of instabilities in non-intuitive ways. Here, these effects are accounted for in our framework to achieve various design objectives, including tailored force–displacement response and maximized energy absorption from both geometric and material effects. To fully automate this process, it is essential to have a completely robust equation solver for forward problems involving instabilities and viscoelastic relaxation. We achieve this by casting the search for stable mechanical equilibrium — i.e. the forward problem — as a minimization problem and utilize a trust region algorithm to robustly handle instabilities and follow energetically-favorable equilibrium paths through critical points.

97 MATHEMATICS AND COMPUTING↗

Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials

Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.

Xu, Pei [Clemson Univ., SC (United States)]↗

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗

From observation to replication: machine-learning-driven quantification and replication of fine-scale fish kinematics and behavior

Long-term quantification of fish behavior is essential for aquatic ecology, wildlife telemetry, and biomechanical device development. However, the observation duration required to obtain reliable behavioral and kinematic metrics remains unclear, and few tools exist to physically reproduce natural swimming motion for controlled experimentation. We address these challenges by developing a generalizable framework that models behavioral reliability (Spearman–Brown reliability index) as a function of observation duration and derives metric-specific monitoring thresholds. Using juvenile white sturgeon (Acipenser transmontanus) as a case study, we demonstrate that the minimum duration needed for reliable estimates varies substantially across kinematic features: to exceed a reliability of 0.8, total distance traveled requires 12 days, average curvature (mm?¹) 15 days, tail-beat frequency (Hz) 8 days, and average speed (body length/s) 17 days. We further bridge digital analysis and physical testing by developing a hardware-in-the-loop simulator that reconstructs machine-learning-derived swimming kinematics with high fidelity (correlation coefficient 0.98–0.99, RMSE 1.22–1.27 mm over a 5-minute segment). This platform enables realistic, repeatable motion stimuli for evaluating aquatic sensing technologies and bio-integrated devices under controlled conditions. Together, these contributions provide a scalable approach for designing long-term behavioral studies and a data-driven connection between ecological observation and robotic experimentation.

Hwang, SungJoo↗

Optimizing Bendability and Hardness of Age-Hardenable Aluminum Sheets through Local Thermo-Mechanical Processing

This paper introduces a novel thermo-mechanical process to modify the local mechanical properties of 6xxx aluminum (Al-Mg-Si-Cu) alloy sheets. In this process, two pairs of rollers travel along the length of a sheet while locally bending and unbending it so that the final shape and thickness of the sheet remains unchanged. Room temperature bending/unbending (B/U) produces a deformation gradient through thickness and local hardening in both T4 and T6 sheets (with 41% and 18% greater Vickers hardness, respectively). High temperature bending/unbending performed at ~ 500°C via induction heating, produces significant improvements in local formability as measured by bend testing. Formability levels equivalent to the as-received T4 temper are achieved within the processed zones of T6 sheets with bend angles of ~ 150°, without disturbing the T6 temper in the remainder of the sheet. Finite element simulations and characterization explained the mechanical property improvements and the heterogenous microstructure with weakened Cube texture. Due to its similarity with the roller hemming process, and its compact dimensions, the apparatus developed for B/U has the potential for seamless integration with roller hemming robots to selectively process high strength aluminum sheet materials within mass-production settings.

Efe, Mert [BATTELLE (PACIFIC NW LAB)]↗

Functional stimuli-responsive polymers on micro- and nano-patterned interfaces

Micro- and nano-patterned surfaces offer precise control over morphology and chemical composition, enhancing the stability, durability, and functionality of coating materials. When combined with stimuli-responsive polymers, these surfaces gain dynamic adaptability, enabling reversible binding, reusable sensing, and selective molecular capture. Furthermore, while recent review articles have explored various aspects of stimuli-responsive materials, from hydrogel patterns for bioanalytical applications to shape-morphing hydrogels for soft robotics and sensors, a comprehensive review focused on the integration of smart polymers with micro- or nano-patterned interfaces remains absent. This review addresses key surface patterning techniques, including soft lithography, colloidal lithography, and polymer brush photolithography, as well as advances in surface-initiated polymerization methods, such as surface-initiated controlled radical polymerization (SI-CRP). In addition, we discuss recent progress in integrating stimuli-responsive polymers with patterned surfaces to create advanced, functional materials.

Colloidal lithography↗

Isomerization kinetics of azobenzene crosslinked to a liquid crystal polymer network

Azobenzene is an important chromophore having numerous applications that are a direct implication of its ability to change shape upon irradiation with a suitable wavelength. Here, in this study, photoisomerization kinetics of a diacrylate azobenzene molecule (A3MA) cross-linked to a liquid crystal polymer network (LCN) is investigated. Isomerization experiments were conducted on planar LCN thin films with varying cross-linking densities for a fixed azobenzene concentration. The experiments were conducted at approximately 26 °C, which is significantly below each film’s glass transition temperature. The forward trans-cis and reverse cis-trans isomerization slows down in crosslinked azobenzene liquid crystal polymer networks (ALCNs) as compared to a solution and an uncrosslinked polymer mixture. The observed slowdown is due to the reduction in the free volume and increased steric hindrances with an increase in crosslinking. A single-term first-order kinetics rate equation is known to describe the isomerization kinetics in solutions or uncrosslinked mixtures while the same form fails to represent the reaction kinetics in moderate to highly cross-linked ALCNs. Our results for the first time indicate that a two-term first-order reaction rate is an accurate way to describe the forward trans-cis and reverse thermal cis-trans isomerization in crosslinked azobenzene liquid crystal polymer networks. In addition, the effect of film thickness and incident intensities on the reaction kinetics is investigated. Finally, the two-term isomerization kinetics is incorporated into a finite element photo-chemo-mechanical model to reveal the consequent slowdown in the actuation response of ALCN films. The current findings will be of particular interest to theoreticians and experimentalists alike, for it will help predict and understand accurately the responses of azobenzene-based coatings, and actuators and may have potential applications in underwater soft robotics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Transforming E-Waste Into Strategic Resources: Techno-Economic Analysis of Gallium and By-products Recovery from LEDs via Bioleaching

The growing demand for gallium in optoelectronics and renewable energy applications raises concerns about supply security and production sustainability. This study evaluates the techno-economic feasibility of recovering gallium and by-products (copper and nickel) from waste GaN-based LEDs via bioleaching. A process flowsheet encompassing transportation, robotic disassembly, ball milling, bioleaching, solvent extraction/electrowinning, and refining was modeled. Based on mass balance analysis, more than 53 tons of LED waste are required annually to yield 1 kg of gallium alongside substantial copper and nickel co-products. Preliminary techno-economic analysis (TEA) shows an average total cost (ATC) of 6.84 USD/kg metal when costs are allocated by mass-weighted economic value (market price) fraction, corresponding to 6.75 USD/kg for copper, 15.91 USD/kg for nickel, and 470.95 USD/kg for gallium. For gallium, direct operational costs account for more than 70% of the cost. Monte Carlo simulations further quantify cost uncertainties under market price fluctuations. This work represents the first TEA of gallium recovery from GaN-based LEDs and highlights potential pathways for future cost reduction.

36 MATERIALS SCIENCE↗

Stimulus-Responsive Modulation of Solvation Environments in Solid Catalysts

Liquid environments play a crucial role in the biological processes occurring in living organisms as well as in many human-made processes involving electrochemistry, photo-, and thermocatalysis. In the majority of these systems, aqueous phases are ubiquitous due to water’s natural abundance. Water molecules, however, can exert large changes in the chemical environment of catalytically active sites, altering the reaction rates, selectivity, and catalyst stability. These solvation effects induced by water molecules near catalytic sites can drastically change the energy landscape and unlock unique reaction pathways with far more favorable kinetics. In nature, living organisms couple these complex interactions with detection, communication, and actuation mechanisms to induce self-regulatory behavior, ensuring stability of the system and thus long-term durability. Extrapolating this behavior to heterogeneous catalysis is desirable because the resulting “smart materials” can potentially unlock new chemical conversion processes with higher atom efficiency, rates, and stability. The combination of polymer chemistry and heterogeneous catalysis has introduced versatile approaches for creating materials that can respond to cues in the reaction medium that alter the accessibility, intrinsic activity, and selectivity of the catalyst. To achieve this, one could combine stimulus-responsive polymers, which undergo a large volumetric phase transition in response to an external stimulus, with a solid catalyst. This chemo-mechanical response has been employed to create a variety of nanoreactor vessels with stimulus-responsive character that turn on- and off- depending on the reaction conditions. In this Account, we focus on the impact of these polymer coatings on the solvation environment around the active site and the implications of these effects on the reaction energy landscape, molecular arrangement of the solvent, electric fields at the catalyst–liquid interface, binding energy, and mobility of surface reaction intermediates. These seemingly subtle changes in solvent molecules induced by the presence of polymers can have a tremendous impact on the development of bioinspired heterogeneous catalysts, reliable chemical clocks, micro/nanoreactors, and robots. The large library of polymer chemistries offers a plethora of combinations of stimulus-responsive mechanisms (e.g., temperature, pH, light, magnetic field, solvent composition), providing the possibility of creating homeostatic catalysts à la carte.

catalysts↗

One Pot Synthesis of Cyan Emitting CdZnSSe Quantum Dots for Human Centric Lighting

A one pot synthesis of blue and green emissive CdZnSSe quantum dots (QDs) from thio- and selenoureas and Cd and Zn carboxylates is optimized using high throughput robotic optimization. A large set of spectral data (N = 192) is used to train machine learning models that accurately predict the photoluminescence emission wavelength (λmax) and full-width half-maximum, and the relative photoluminescence quantum yield (PLQY) from the S:Se and Zn:Cd stoichiometries and reaction time. ZnS shells are deposited on the crude QD heterostructures using 4-tert-butylbenzyl mercaptan, a more reactive source of sulfide that enables shell growth below the temperature where ion diffusion in the QD can broaden its optical spectrum (≤275 °C). These optimized procedures provide gram quantities of blue-green emitting QDs (PLQY = 85–99%) in a single reaction vessel. A solid state lighting device (4260 K) that incorporates cyan emissive QDs achieved a higher luminous efficacy of 179 lm/W and melanopic daylight efficiency ratio (0.71) than existing commercial human centric lighting devices.

Jordan, Abraham J↗

A Perspective into Operando Methods for Probing Catalytic Interfaces

Rational design of electrocatalysts for (photo)electrochemical (PEC) processes like hydrogen and oxygen evolution and CO2 reduction reactions is aided by the recent improvements in capabilities of operando measurements, where morphology, composition, and/or function are probed during active catalysis. Through operando microscopy and spectroscopy, structure, catalytic microenvironment, oxidation state, adsorbates, and products can be measured to gain a better understanding of catalyst behavior and suggest possible improvements. Visualizing evolving catalyst morphologies, surface compositions, and electrochemical behavior also helps address many fundamental research questions for a better understanding of catalytic mechanisms. Correlating morphology with chemical identity or functional behavior using a variety of innovative microscopy methods is particularly promising for guiding development of next generation catalysts, and there are also many recent examples of using AI and robotics tools to innovate and speed development. In this Perspective, advances made over the past few years in operando imaging of catalysts relevant to solar fuels will be explored, followed by an outlook on technological developments in instrumentation, sample design, and computational power that may be applied to this field.

Catalysts↗