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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Understanding the limitations and potential of micro tensile testing of tungsten and needs for crystal plasticity modelling

Tungsten is the leading plasma facing material candidate due to its exceptional properties. Understanding the response to neutron irradiation is crucial for the lifetime evaluation of tungsten. The limited space in nuclear reactors and the high levels of radioactivity of the specimens after irradiation are significant barriers to accurately measuring the mechanical properties after neutron irradiation. Testing of micro tensile specimens is one approach to reduce the total amount of irradiated material needed for a set of mechanical testing experiments. However, micro samples are not necessarily measuring the bulk material properties, as size effects produce higher measured mechanical properties than is observed in engineering size counterparts. Determining the minimal sample size for reliable bulk property measurement, combined with modeling efforts, is essential. We conducted room temperature tensile tests on tungsten micro tensile specimens fabricated with focused ion beam, plasma focused ion beam and femto-second laser ablation system to dimension of 2×2×7?µm³, 7×7×18?µm³, and 80×100×233?µm³ (width×thickness×gauge-length), respectively. Elevated tensile testing was performed up to 475°C on 5×5×18?µm³ specimens fabricated with focused ion beam. The smallest specimens exhibited a high degree of ductility and strength, whereas the largest specimens demonstrated behavior akin to bulk tungsten. To investigate the effects introduced by the micro specimen fabrication processes and to obtain the necessary bulk dislocation density for the crystal plasticity model, we employed X-ray diffraction depth profiling. This measurement was performed using different X-ray sources with varying penetration depths. Finally, the potential and limitations of micro mechanical tests for tungsten will be discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Evaluation of U10Mo Fuel Plate Performance Modeling Over Hot Isostatic Press and Hydraulic Bending for MURR DDE Plates

The United States High Performance Research Reactor Program’s objective is to reduce the amount of highly enriched uranium currently implemented in research reactors. The conversion of these research reactors requires designing a monolithic U10Mo plate fuel, with the fuel plate geometry being dependent on each research reactor. The process of forming the plates includes a hot isostatic pressing (HIP) to manufacture a prototypic plate. In the case of the Missouri University Research Reactor (MURR) design demonstration element (DDE) plate manufacture, plates that have been through HIP are then curved using dies and a hydraulic press to impart the desired curvature. Both fabrication processes impart residual stresses into each fuel plate region, with the curvature of the plates taking some regions of the fuel plate up to their material yield stresses, accompanied by plastic strain. The amount of plastic strain and stress imparted onto each MURR DDE plate is determined by the radius of curvature, thickness of each region, and overall width of the fuel plates. Furthermore, this work aims to predict the yield stresses and strain using ABAQUS to simulate the proposed fabrication process of the MURR DDE plates, accompanied by discussion over the stresses and strains as to their relation to nuclear fuel performance and the impact they will have during early irradiation.

ABAQUS↗
DOE OSTI2025

Engineering 2‐Pyrone‐4,6‐Dicarboxylic Acid Production Reveals Metabolic Plasticity of Poplar

Woody biomass is a promising source of fermentable sugars for biofuels and bio-based chemicals, but its industrial use is limited by the costly biorefinery process. A viable strategy to reduce costs involves enhancing both biomass processability and the generation of high-value co-products. Here, we report the implementation of a synthetic metabolic pathway in Populus tremula × P. alba to produce 2-pyrone-4,6-dicarboxylic acid (PDC), a key building block for biodegradable plastics and high-performance materials. This artificial pathway—comprising microbial genes AroG, QsuB, PmdA, PmdB, and PmdC—enabled de novo PDC production in the stems of transgenic poplar. Pathway expression also induced substantial metabolic reprogramming and altered cell wall composition. These include the hyperaccumulation of simple phenolics like protocatechuic acid (PCA) and vanillic acid (VA), alongside reduced levels of p-hydroxybenzoic acid. A large portion of VA was ester-linked to cell wall lignin, while PCA was incorporated into the lignin backbone, forming novel benzodioxane units; concurrently, lignin in transgenic plants exhibited a drastic reduction in guaiacyl- and syringyl-units, with a notable increase in p-hydroxyphenyl-units. Hemicellulose content, particularly xylan, was also significantly increased. Moreover, expression of the PDC-pathway led to the formation of novel VA-derived suberin aromatics, enhancing suberization in bark and roots and improving salt stress tolerance. These changes led to improved saccharification efficiency, with up to 25% more glucose and 2.5 times xylose released from woody biomass. These results demonstrate the metabolic flexibility of poplar and highlight its potential for engineering cost-effective, stress-resilient bioenergy crops with enhanced biorefinery traits.

2-pyrone-4↗
DOE OSTI2024

Rotational Millimeter-Wave Shoe Scanner Using the Discrete Fourier Transform for Backprojection-Based Image Reconstruction

An active 3D microwave / millimeter-wave shoe scanner was previously developed at the Pacific Northwest National Laboratory (PNNL) using two linear arrays scanned over a rectilinear aperture. The radar system chirps a frequency sweep from 10-40 GHz. These frequencies allow imaging through optically opaque material such as leather, rubber, plastics, and other dielectrics. The system was designed to detect concealed items in the soles of shoes while allowing people to leave their shoes on through a security checkpoint. To shrink the footprint of the system, a new iteration of the design has been developed that scans the two linear arrays over a circular aperture. This new footprint opens the possibility of it being installed in the floor of a cylindrical millimeter-wave body scanner. The backprojection-based multilayer dielectric image reconstruction developed at PNNL can easily handle arbitrary spatial sampling, accommodating the new rotational shoe scanner design. Commonly, the fast Fourier transform (FFT) is used to efficiently compute the range response from the data collected by the system as a preprocessing step to the backprojection algorithm. It was found that converting to range using the discrete Fourier transform (DFT) directly has some advantages over the FFT. For example, nonlinear and non-uniform frequency sweeps can easily be compensated for during the computation of the DFT and only the range bins of interest need to be computed and their spacing can be chosen arbitrarily. Because the range conversion step of the image reconstruction is the fastest part of the process there is very little speed penalty for using the DFT over the FFT and it can even increase the speed of image reconstruction when the ranges of interest are fewer than the total span that is calculated in the FFT.

Millimeter-wave imaging, microwave imaging, shoe s↗
DOE OSTI2025

The Alamo multiphysics solver for phase field simulations with strong-form mechanics and block structured adaptive mesh refinement

Alamo is a high-performance scientific code that uses block-structured adaptive mesh refinement to solve such problems as: the ignition and burn of solid rocket propellant, plasticity, damage and fracture in materials undergoing loading, and the interaction of compressible flow with eroding solid materials. Alamo is powered by AMReX, and provides a set of unique methods, models, and algorithms that enable it to solve solid-mechanics problems (coupled to other physical behavior such as fluid flow or thermal diffusion) using the power of block-structured adaptive mesh refinement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System

Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg 32 (Zn,Al) 49 . It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microstructural and material property changes in severely deformed Eurofer-97

Severe plastic deformation changes the microstructure and properties of steels, which may be favourable for their use in structural components of nuclear reactors. In this study, high-pressure torsion (HPT) was used to refine the grain structure of Eurofer-97, a ferritic/martensitic steel. Electron microscopy and X-ray diffraction were used to characterise the microstructural changes. Following HPT at room temperature to a maximum shear strain of 230, the average grain size reduced by a factor of ~30, with a marked increase in high-angle grain boundaries. Dislocation density also increased by more than one order of magnitude. The thermal stability of the deformed material was investigated via in-situ annealing during synchrotron X-ray diffraction. This revealed substantial recovery between 450 K – 800 K. Irradiation with 20 MeV Fe-ions to ~0.1 dpa caused a 20% reduction in dislocation density compared to the as-deformed material. However, HPT deformation prior to irradiation only had a minor effect in mitigating the irradiation-induced reductions in thermal diffusivity and surface acoustic wave velocity of the material. Microstructural and material property changes are dominated by deformation compared to irradiation. In light of this, the benefits of using HPT to improve the irradiation resistance of Eurofer-97 are limited. These results provide a multi-faceted view of the changes in ferritic/martensitic steels due to severe plastic deformation, and how these changes can be used to alter material properties.

ion-irradiation↗
DOE OSTI2026

Hydrogenolysis Versus Hydrocracking for Polyolefin Upcycling

Global plastic production has reached 413.8 million metric tons in 2024 and is forecasted to surpass 1.2 billion metric tons by 2050. Polyolefins, mainly polyethylene (PE) and polypropylene (PP), dominate single-use packaging and account for approximately 55% of global plastic waste. The chemical inertness that makes these materials desirable for commercial applications also renders them persistent in the environment. Current recycling technologies have proven to be insufficient to divert plastic waste from landfills or environmental loss due to technical limitations and poor economic incentives. Conventional mechanical recycling is a form of downcycling, in which the polymer remelting process results in products with inferior material properties and reduced market value. Pyrolysis, a thermochemical route used for the chemical recycling of plastic wastes into refinery feedstock, requires severe conditions, typically 400–600 °C in an oxygen-free environment. The high temperature drives up energy costs and produces a wide range of poorly defined products, including undesirable light gases and heavy tars that pose challenges for downstream processing. As an alternative, catalytic chemical recycling offers a promising route for converting waste polyolefins back into value-added hydrocarbons—such as fuels, lubricant base oils, and other chemical feedstocks—at temperatures typically below 300 °C. This opinion article focuses on two dominant pathways in heterogeneous catalysis that are used to cleave the C–C bonds of polyolefins: hydrogenolysis and hydrocracking. Both catalytic pathways have the potential to selectively convert polyolefin waste into valuable fuels and chemical feedstocks under mild conditions. Notably, while hydrocracking primarily yields branched hydrocarbon products, hydrogenolysis predominantly produces linear hydrocarbons. Here, we compare the mechanisms and catalyst designs for hydrocracking and hydrogenolysis, analyze critical technical challenges from catalyst stability to process engineering, and provide an outlook on how these complementary pathways can be used to repurpose plastic waste into valuable products.

Zhang, Ruoxi [Iowa State Univ., Ames, IA (United S↗
DOE OSTI2024

ChemPren: a new and economical technology for conversion of waste plastics to light olefins

With the ever-increasing demand for plastics, sustainable recycling methods are key necessities. Here, the current plastics industry can manage to recycle only 10% of the 400 million metric tons of plastic produced globally. Waste plastics, in the current infrastructure, land up mostly in landfills. Although a lot of research efforts have been spent on processing and recycling co-mingled mixed plastics, energy-efficient sustainable and scalable routes for plastic upcycling are still lacking. Catalytic valorization of waste plastic feedstock is one of the potential scalable routes for plastic upcycling. Silica-alumina based materials, and zeolites have shown a lot of promise. A major interest lies in restricting catalyst deactivation, and refining product selectivity and yield for such catalytic processes. This article highlights ChemPren technology as a clean energy solution to waste plastic recycling. Co-mingled, mixed plastic feedstock along with spray dried, attrition resistant, ZSM-5 containing catalysts is preprocessed with an extruder to form optimally sized particles and fed into a fluidized bed reactor for short contact times to produce selectively and in high yields ethylenes, propylenes and butylenes. This techno-economic perspective indicates that the ChemPren technology can produce propylene at $\$$0.16 per lb, whereas the current selling price of virgin propylene is $0.54 per lb. This technology can serve as a platform for mixed plastic upcycling, with more advancements necessary in the form of robust and resilient catalysts and reactor operation strategies for tuning product selectivity.

25 - ENERGY STORAGE↗
DOE OSTI2024

In-Situ and Ex-Situ Studies on the Morphology Changes of Polymer Pipeline Materials for Use in Hydrogen Gas Environments

The US natural gas infrastructure is a national asset that could be used to deliver hydrogen and hydrogen blends of natural gas as a pathway to reduce carbon emissions. The distribution system comprises nearly 50% plastic pipe composed of medium- and high-density polyethylene materials (MDPE and HDPE). While these materials perform adequately for natural gas, research on their hydrogen compatibility is essential to understand if any immediate and long-term risks are associated with hydrogen addition. The Blended Gas CRADA, a HyBlend project, has established a comprehensive test method for evaluating MDPE and HDPE of various plastic resin compositions of pipeline material in pure hydrogen and 20% hydrogen/80% methane blends. Both in-situ and ex-situ measurements were performed to capture hydrogen-induced changes in the polyethylene material's crystalline, amorphous and their interphase regions. We investigated MDPE and HDPE pipeline materials made from different polymer resin systems to evaluate the effects of hydrogen gas. The materials were characterized by their density, diffusion coefficient, free volume ratio, and degree of crystallinity. Various advanced characterization methods, including in situ NMR, ex situ XRD, ex situ DSC, and ex situ TDA, were used to analyze the effects of changes in crystalline, amorphous, and interphase regions due to gas exposure. Time-dependent post-decompression quasi-static tensile tests were conducted to explore the effects of gas exposure time on the mechanical behavior of the pipe materials. This work will highlight the time sensitivities during and after gas exposure. The correlation between gas-induced polyethylene morphology changes and the associated material performance will be addressed for the intended applications. These studies will show that polyethylene resin composition and material exposure are important factors when considering whether hydrogen gas affects pipeline materials positively or negatively.

Simmons, Kevin L.↗
DOE OSTI2025

Grain size effects on slip band development

Crystallographic slip localizations, known as slip bands, concentrate stress in polycrystals, often leading to the nucleation of damage. Slip band development has been experimentally shown to be sensitive to grain size, tending to develop more frequently and with a greater intensity in large grains. In this work, we investigate the influence of grain size on the propensity for crystallographic slip band development. To this end, we employ the slip band-fast Fourier transform method (SB-FFT). SB-FFT is a 3D, full-field crystal plasticity model that permits the incremental development of discrete crystallographic slip bands according to microstructure and material properties. We present a model Inconel 718 tricrystal to isolate the effect of grain size. Our findings show that slip bands in large grains develop at lower applied strain levels and at a faster rate than slip bands in small grains. The grain size effect is due to a backstress produced by the interaction of the slip band and its neighboring grain. The backstress is most intense at small grain sizes, impeding slip activity within a developing slip band and immediately surrounding the slip band.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling low cycle fatigue (LCF) of additively manufactured Hastelloy X using An accelerated crystal plasticity fatigue damage model

This paper presents a microstructure-based model for low cycle fatigue (LCF) behavior and life of Nickel-based alloy Hastelloy X manufactured using laser-powder bed fusion (L-PBF) additive manufacturing (AM). AM Hastelloy X, a solution-strengthened alloy, is tested at elevated temperature under fully reversed LCF conditions at different strain levels. A generalized plane strain finite element model is generated from electron backscatter diffraction (EBSD) characterization. The constitutive behavior of the material under fatigue is modeled using crystal plasticity and calibrated with both monotonic tensile and cyclic stress–strain data. The fatigue micro-crack initiation and propagation in the microstructure is modeled using a modified Chaboche fatigue damage model. An embedded boundary condition with a homogenous medium is used to apply the cyclic deformation and prevent numerically introduced over-constraints during fatigue simulation. A ‘cycle-jump’ method is used to accelerate the fatigue simulation and reduce the computational cost. The simulation results are compared to LCF experiments, showing satisfactory matches in cyclic stress behavior and number of cycles to macro-crack initiation for all applied strain ranges. In addition, the model illustrates the potential for quantifying microscale fatigue life impacting factors such as microstructure and surface roughness, which is needed to accurately quantify the reliability of AM components in service.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Closed‐Loop Recycling of Mixed Plastics of Polyester and CO 2 ‐Based Polycarbonate to a Single Monomer

Abstract Physical blending is an effective strategy for tailoring polymeric materials to specific application requirements. However, physically blended mixed plastics waste adds additional barriers in mechanical or chemical recycling. This difficulty arises from the intricate requirement for meticulous sorting and separation of the various polymers in the inherent incompatibility of mixed polymers during recycling. To overcome this impediment, this work furthers the emerging single‐monomer – multiple‐materials approach through the design of a bifunctional monomer that can not only orthogonally polymerize into two different types of polymers – specifically lactone‐based polyester and CO 2 ‐based polycarbonate – but the resultant polymers and their mixture can also be depolymerized back to the single, original monomer when facilitated by catalysis. Specifically, the lactone/epoxide hybrid bifunctional monomer (BiL O ) undergoes ring‐opening polymerization through the lactone manifold to produce polyester, PE(BiL O ), and is also applied to ring‐opening copolymerization with CO 2 , via the epoxide manifold, to yield polycarbonate, PC(BiL O ). Remarkably, a one‐pot recycling process of a BiL O ‐derived PE/PC blend back to the constituent monomer BiL O in >99 % selectivity was achieved with a superbase catalyst at 150 °C, thereby effectively obviating the requirement for sorting and separation typically required for recycling of mixed polymers.

Shi, Changxia↗
DOE OSTI2024

Closed‐Loop Recycling of Mixed Plastics of Polyester and CO 2 ‐Based Polycarbonate to a Single Monomer

Abstract Physical blending is an effective strategy for tailoring polymeric materials to specific application requirements. However, physically blended mixed plastics waste adds additional barriers in mechanical or chemical recycling. This difficulty arises from the intricate requirement for meticulous sorting and separation of the various polymers in the inherent incompatibility of mixed polymers during recycling. To overcome this impediment, this work furthers the emerging single‐monomer – multiple‐materials approach through the design of a bifunctional monomer that can not only orthogonally polymerize into two different types of polymers – specifically lactone‐based polyester and CO 2 ‐based polycarbonate – but the resultant polymers and their mixture can also be depolymerized back to the single, original monomer when facilitated by catalysis. Specifically, the lactone/epoxide hybrid bifunctional monomer (BiL O ) undergoes ring‐opening polymerization through the lactone manifold to produce polyester, PE(BiL O ), and is also applied to ring‐opening copolymerization with CO 2 , via the epoxide manifold, to yield polycarbonate, PC(BiL O ). Remarkably, a one‐pot recycling process of a BiL O ‐derived PE/PC blend back to the constituent monomer BiL O in >99 % selectivity was achieved with a superbase catalyst at 150 °C, thereby effectively obviating the requirement for sorting and separation typically required for recycling of mixed polymers.

Chemistry↗