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Results for “Polymer design and discovery”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Radical Polymer-Based Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) are an emerging platform for bioelectronic applications. Significant effort has been placed in designing advanced polymers that simultaneously transport both charge and ions (i.e., macromolecules that are mixed conductors). Furthermore, the considerations for mixed organic conductors are often different from the established principles that are well-known in the solid-state organic electronics field; thus, the discovery of new OECT macromolecular systems is highly desired. Here, we demonstrate a new materials system by blending a radical polymer (i.e., a macromolecule with a nonconjugated backbone and with stable open-shell sites at its pendant group) with a frequently used conjugated polymer. Specifically, poly(4-glycidyloxy-2,2,6,6-tetramethylpiperidine-1-oxyl) (PTEO) was blended with poly(3-hexylthiophene) (P 3 HT) to create thin films with distinct closed-shell and open-shell domains. Importantly, the sharp and unique oxidation–reduction (redox) potential associated with the radical moieties of the PTEO chain provided a distinct actuation feature to the blended films that modulated the ionic transport of the OECT devices. In turn, this led to controlled regulation of the doping of the P 3 HT phase in the composite film. By decoupling the ionic and electronic transport into two distinct phases and by using an ion transport phase with well-controlled redox activity, never-before-seen performance for a P3HT-based OECT was observed. That is, at loadings as low as 5% PTEO (by weight) OECTs achieved figure-of-merit (i.e., μC*) values >150 F V –1 cm –1 s –1 , which place the performance on the same order as state-of-the-art conjugated polymers despite the relatively common conjugated macromolecular moiety implemented. As such, this effort presents a design platform by which to readily create a tailored OECT response through strategic macromolecular selection and polymer processing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of material discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials, and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of plasma emission lines monitored during co-sputtering from elemental Zn and Ti targets in an N 2 and Ar atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined compositions by minimizing the absolute error between desired and measured optical emission signals. We validated our approach by autonomously fabricating Zn x Ti 1-x N y films that deviate from the targeted cation composition by a relative ±3.5%, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with a specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity lines depend non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Tuning Film Stresses for Open-Air Processing of Stable Metal Halide Perovskites

Challenges to upscaling metal halide perovskites (MHPs) include mechanical film stresses that accelerate degradation, dominate at the module scale, and can lead to delamination or fracture. In this work, we demonstrate open-air blade coating of single-step coated perovskite as a scalable method to control residual film stress after processing and introduce beneficial compression in the thin film with the use of polymer additives such as gellan gum and corn starch. The optoelectronic properties of MHP films with compression are improved with higher photoluminescence yields. MHP film stability is significantly improved under compression, under humidity, heat, and thermal cycling. By measuring the evolution of film stresses, we demonstrate for the first time that stress relaxation occurs in MHP films with tensile stress that correlates with film degradation. Finally, this discovery of a new mechanism underpinning MHP degradation shows that film stress can be used as a parameter to screen MHP devices and modules for quality control before deployment as a design for reliability criterion.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Selection, Production and Properties of Regolith Polymer Composite for Lunar Construction

NASA’s Artemis program has the goal of creating a sustained lunar presence to provide unprecedented opportunities for scientific discovery and to ensure industry’s access to the unlimited resources and commercial potential in space. To achieve this goal, NASA must incrementally develop and expand its capabilities beyond the short lunar stays of the Apollo program to a robust continued presence with infrastructure and equipment to reduce mission risk. Kennedy Space Center’s Granular Mechanics and Regolith Operations laboratory (a.k.a. Swamp Works) has partnered with SpaceFactory and LERA Consulting Structural Engineers to develop the architectural and structural design of a robotically constructable unpressurized shelter. The shelter, called Lunar Infrastructure Asset (LINA), is designed to protect astronauts and surface assets from radiation, meteoroid impact, thermal gradients, and to withstand moonquakes. A Fused Granular Fabrication (FGF) construction process using regolith polymer composites was developed. The construction system and associated print parameters are discussed along with the environmental simulation equipment and a summary of test conditions. Test samples were printed in dirty thermal vacuum conditions (~10-3 torr, ~-200 °C,) and subscale versions of LINA were printed on a regolith simulant substrate in vacuum (~10-4 torr). Full scale LINA design optimization, simulation, and construction concept of operations are discussed.

lunar infrastructure↗
NASA NTRSDate not supplied

Application of Regolith Polymer Composite Fused Granular Fabrication Construction in Simulated Lunar Conditions

NASA’s Artemis program has the goal of creating a sustained lunar presence to provide unprecedented opportunities for scientific discovery and to ensure industry’s access to the unlimited resources and commercial potential in space. To achieve this goal, NASA must incrementally develop and expand its capabilities beyond the short lunar stays of the Apollo program to a robust continued presence with infrastructure and equipment to reduce mission risk. Kennedy Space Center’s Granular Mechanics and Regolith Operations laboratory (a.k.a. Swamp Works) has partnered with SpaceFactory and LERA Consulting Structural Engineers to develop the architectural and structural design of a robotically constructable unpressurized shelter. The shelter, called Lunar Infrastructure Asset (LINA), is designed to protect astronauts and surface assets from radiation, meteoroid impact, thermal gradients, and to withstand moonquakes. A Fused Granular Fabrication (FGF) construction process using regolith polymer composites was developed. The construction system and associated print parameters are discussed along with the environmental simulation equipment and a summary of test conditions. Test samples were printed in dirty thermal vacuum conditions (~10-3 torr, ~-200 °C,) and subscale versions of LINA were printed on a regolith simulant substrate in vacuum (~10-4 torr). Full scale LINA design optimization, simulation, and construction concept of operations are discussed.

lunar infrastructure↗
NASA NTRSDate not supplied

Application of Regolith Polymer Composite FFF Construction in Simulated Lunar Conditions

NASA’s Artemis program has the lofty goal of creating a sustained lunar presence to provide unprecedented opportunities for scientific discovery and to ensure American industry’s access to the unlimited resources and commercial potential in space. To achieve this goal, NASA must incrementally develop and expand its capabilities beyond the short lunar stays of the Apollo program to a robust continued presence with infrastructure and equipment to reduce mission risk by providing safer and more reliable access to the surface, its resources, and protection from the environment. Kennedy Space Center’s Swamp Works Granular Mechanics and Regolith Operations laboratory has partnered with AI SpaceFactory to develop the architectural and structural design of a robotically constructable unpressurized shelter. The shelter, called Lina is designed to protect astronauts and surface assets from radiation, meteoroid impact, moon quakes and thermal gradients. A Fused Filament Fabrication (FFF) construction process using regolith polymer composites was developed. The construction system and associated print parameters are discussed along with the environmental simulation equipment and a summary of test conditions. Test samples were printed in simulated lunar dirty thermal vacuum conditions (~-180 C, ~10-3 torr) and subjected to a series of tests. The resulting strength properties are summarized here, the full details of the material characteristics are provided in a related paper. Lina’s structural design criteria and the resulting initial structure design are detailed in two related papers, this paper presents an update to Lina’s design based on the achieved material strength properties. Finally, a scaled down version of the updated Lina was built on a regolith simulant surface using a novel anchoring technique in simulated lunar vacuum conditions.

lunar infrastructure↗
DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Selection, Production, and Properties of Regolith Polymer Composites for Lunar Construction

NASA’s Artemis Program seeks to establish a long-term presence on the moon to enable scientific discoveries and utilization of lunar resources through public-private partnerships. Over the next decades, a lunar spaceport enabling regular transportation from and to Earth will need to be established to provide the services and facilities that are necessary to achieve this goal. Robotic construction technologies using in-situ materials must be developed to build up enabling infrastructure such as launch/landing pads, blast protection, power/communications towers, improved trafficability pathways and radiation protection shelters. Kennedy Space Center’s Granular Mechanics and Regolith Operations laboratory at Swamp Works has partnered with AI SpaceFactory to develop the architectural and structural design of an unpressurized shelter. The shelter, called Lina, is designed to protect astronauts and surface assets from radiation, meteoroid impact, moon quakes and thermal gradients. Lina’s structural design criteria and the resulting structure design are detailed in separate papers. A Fused Filament Fabrication (FFF) construction process using regolith polymer composites has been developed. This paper presents the material formulations and selection rationale for each of the composite components. Formulations include 70:30, 80:20, and 85:15 wt.% mixture ratios of lunar mare simulant Black Point-1 (BP-1): Polylactic Acid (PLA), 80:20 Lunar Highlands Simulant (LHS-1): PLA and an 80:20 BP-1:PLA formulation with a processing aid/compatibilizer additive. Test samples were printed in simulated lunar dirty thermal vacuum conditions (~-180 °C, ~10 -3 torr). The test environment evolved throughout the duration of the print process. A series of tests were performed to characterize the mixture ratios achieved for each formulation, the off-gassing products during vacuum printing, the strength properties, and porosity of printed products. The LHS-1: PLA formulation yielded an advantageous combination of properties and was used in a final test that additively constructed a sub-scaled Lina on regolith simulant in dirty vacuum conditions.

lunar infrastructure↗
DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

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

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. Here, we show that using a self-termination method with a minimum Spearman’s rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Superionic conduction in solid polymer electrolytes – decoupling ion transport from segmental relaxation

Solvent-free, solid polymer electrolytes (SPEs) are promising candidates for next-generation, electrochemical energy storage systems due to their potential to enhance safety and performance, enable flexible device architectures, and streamline manufacturing processes. Conventional SPEs suffer from limited ionic conductivity due to the strong coupling between ion transport and (generally slow) polymer segmental relaxation. The realization of superionic conduction in SPEs, in which ions move faster than the structural relaxation of the polymers, requires a shift in design principles to promote this type of decoupled ion motion. In this perspective, we discuss how polymer architecture, ion–ion correlations, and ion–polymer interactions can unlock superionic behavior. We highlight several key design features, such as crystallinity, bulky side groups, high molecular weight, and percolating ionic aggregation, with a focus on creating low-barrier transport pathways in various polymer systems. We also demonstrate opportunities to combine polymer chemistry and data science through high-throughput and automated screening approaches to reveal how phase behavior, ion dynamics, and ionic interactions govern transport, thereby potentially enabling data-driven discovery of superionic polymer electrolyte materials.

Yang, Mengying [Univ. of Delaware, Newark, DE (Uni↗
DOE OSTI2026

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗
NASA NTRS1991

NASA Space Engineering Research Center for utilization of local planetary resources

Because of a change in the NASA funding cycle, the present reporting period covers only the six months from March to September 1991. Nevertheless, remarkable progress was made in a number of areas, some of the most noteworthy of which are: (1) Engineering operation of a breadboard CO2 yields O2 demonstration plant that produced over 10 grams of oxygen per day during several runs of over 100 hours each with a single electrolytic cell. Complete automation of controls, monitoring of various inputs/outputs and critical internal variables, diagnostics, and emergency shutdown in an orderly manner were also included. Moreover, 4-cell and 16-cell units, capable of much higher rates of production, were assembled and tested. (2) Demonstration of a 200 percent increase in the carbothermal reduction of ilmenite through vapor deposition of carbon layers on particles of that material. (3) Demonstration of the deposition of strong iron films from carbonyl chemical vapor deposition, establishing the crucial role of additive gases in governing the process. (4) Discovery of an apparent 800 percent increase in the conversion rates of a modified ilmenite simulant in a plasma-augmented reactor, including direct enhancement by solar radiation absorption. (5) Proof that test specimens of lunar soil with small amounts of metallic additives, recrystallized at moderate temperatures, exhibit an improvement of several orders of magnitude in ductility/tensile strength. (6) Experiments establishing the feasibility of producing silicon-based polymers from indigenous lunar materials. (7) Application of CCD technology to the production of maps of TiO2 abundance, defining primary ilmenite deposits, on the disk of the full moon. (8) Attainment of a discovery rate of approximately 3 new near-Earth asteroids per month by Spacewatch, more than doubling the previous global rate. (9) Coordination of industry and university magma electrolysis investigations in a workshop designed to define remaining problem areas and propose critical experiments.

Ramohalli, Kumar↗
DOE OSTI2021

Bias free multiobjective active learning for materials design and discovery

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Advanced manufacturing provides tailor-made solutions for crystallography with x-ray free-electron lasers

Serial crystallography at large facilities, such as x-ray free-electron lasers and synchrotrons, evolved as a powerful method for the high-resolution structural investigation of proteins that are critical for human health, thus advancing drug discovery and novel therapies. However, a critical barrier to successful serial crystallography experiments lies in the efficient handling of the protein microcrystals and solutions at microscales. Microfluidics are the obvious approach for any high-throughput, nano-to-microliter sample handling, that also requires design flexibility and rapid prototyping to deal with the variable shapes, sizes, and density of crystals. Here, we discuss recent advances in polymer 3D printing for microfluidics-based serial crystallography research and present a demonstration of emerging, large-scale, nano-3D printing approaches leading into the future of 3D sample environment and delivery device fabrication from liquid jet gas-dynamic virtual nozzles devices to fixed-target sample environment technology.

36 MATERIALS SCIENCE↗