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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Atmospheric Brown Carbon on the Tibetan Plateau: Regional Differences in Chemical Composition and Light Absorption Properties

In this work, fine particulate matter (PM 2.5 ) filter samples were collected from two high-altitude remote sites located in the southern (QOMS) and northern (WLG) regions of the Tibetan Plateau (TP) to explore the regional differences in brown carbon (BrC) properties. Chemical differences in BrC composition representative of these two areas were inferred from molecular-level analysis of the PM 2.5 samples using high-performance liquid chromatography coupled with photodiode array and high-resolution mass spectrometry detectors. The results show that more polar BrC chromophores were abundant in QOMS samples, while contributions from polar and less polar chromophores were comparable in WLG samples. A higher mass absorption coefficient of BrC was observed at QOMS than at WLG. Strong BrC chromophores in QOMS samples were identified as oxygenated aromatics and nitrophenol compounds, while organosulfate compounds were found in the WLG sample. The results of this study indicate regional differences in BrC chromophores and provide insights into their sources and chemical processes, which should be considered for predictive understanding and modeling of the radiative forcing of aerosol in the TP area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

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

Chemical structure–property relationships in nanocelluloses

Abstract Nanocelluloses, nanoscale cellulosic particles or fibrils, are an appealing class of nanomaterials with enormous potential for sustainable development. They have great promise to target some of the 17 Sustainable Development Goals outlined by the United Nations. Nanocelluloses are derived from the most abundant biopolymer in the world, cellulose, which have multiple hydroxyl groups on their surface, enabling a vast range of chemical modifications. The interplay between the chemical structure and physicochemical properties of nanocelluloses is crucial to engineering the next generation of green, functional nanomaterials. In this review paper, we provide a comprehensive account of the structure–property relationships in nanocelluloses, which may establish the basis for the design of precision bio‐based materials. Chemical modifications of nanocelluloses, including hydrolysis, oxidation, esterification, amidation, and polymer grafting, provide an array of chemical and physical properties that open opportunities in a diverse spectrum of applications. We review how chemical modifications affect the physicochemical properties of nanocelluloses, such as thermal stability, hydrophobicity, and dispersibility in nonpolar media. Understanding nanocellulose chemical structure–property relationships is integral to the development of sustainable material platforms based on the most renewable biopolymer on earth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Correlated Electrical and Chemical Nanoscale Properties in Potassium-Passivated, Triple-Cation Perovskite Solar Cells

Perovskite semiconductors are an exciting class of materials due to their promising performance outputs in photovoltaic devices. To boost their efficiency further, researchers introduce additives during sample synthesis, such as KI. However, it is not well understood how KI changes the material and, often, leaves precipitants. To fully resolve the role of KI, multiple microscopy techniques are applied and the electrical and chemical behavior of a Reference (untreated) and a KI-treated perovskite are compared. Upon correlation between electrical and chemical nanoimaging techniques, it is discovered that these local properties are linked to the macroscopic voltage enhancement of the KI-treated perovskite. The heterogeneity revealed in both the local electrical and chemical responses indicates that the additive partially migrates to the surface, yet surprisingly does not deteriorate the performance locally, rather, the voltage response homogeneously increases. The research presented within provides a diagnostic methodology, which connects the nanoscale electrical and chemical properties of materials, relevant to other perovskites, including multication and Pb-free alternatives.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Handling and Properties of Methanol as a Marine Fuel

Given the increasing concern around greenhouse gas emissions and the decline in the availability of fossil fuels, there is increasing global demand to develop alternate fuels for maritime transportation that are sustainable and which have lower greenhouse gas emissions. Methanol is one such alternative fuel that has garnered considerable attention given its potential to be produced by more sustainable processes and its more favorable greenhouse gas emission profile in comparison with current fossil fuels. Understanding the physical and chemical properties of methanol under a range of conditions is essential for its development as a marine fuel. In this study, we seek to define physical and chemical properties of different methanol samples to simulate real-world storage conditions as these data are lacking in the literature. Several methanol samples were evaluated: nearly pure methanol; International Organization for Standardization (ISO) marine methanol (MM) grades A, B, and C; and methanol plus higher alcohols. We first evaluated all methanol samples for impurities, acetic acid content, density, and distillation range. We then characterized the effects of water absorption and found that methanol can easily absorb unacceptable water content from humid air within hours, necessitating storage conditions that prevent this process. In eight-week aging experiments at 20 °C and 40 °C in ambient air, we did not observe significant oxidation for any of the methanol samples; however, we did observe increases in acid number. We assessed the impact of contamination of methanol with water, marine gas oil (MGO), and an MGO–biodiesel mixture on density, viscosity, distillation range, and lubricity. Finally, we show that MGO contamination of methanol results in a slight increase in sooting tendency. In aggregate, our results provide an in-depth analysis of physical and chemical properties of methanol as well as the impacts of storage conditions and impurities on the properties of fuel methanol.

09 BIOMASS FUELS↗
DOE OSTI2021

Characterizing Reactive Transport Behavior in a Three-Dimensional Discrete Fracture Network

While several studies have linked network and in-fracture scale properties to conservative transport behavior in subsurface fractured media, studies on reactive transport cases remain relatively underdeveloped. In this study, we explore the behavior of an irreversible kinetic reaction during the interaction of two solute plumes, one consisting of species A and the other species B. When the plumes converge, these species react kinetically to form a new species C via A+B→kC. This reactive system is studied using a three-dimensional discrete fracture network (DFN) model coupled with reactive Lagrangian particle tracking. We find that the interplay of network topology and chemical properties of the reactive solutes controls reactive transport processes. The network topology drives species A and B together, and the chemical properties dictate whether and how quickly a reaction occurs. Results demonstrate that reactions are most likely to occur in high-velocity fractures that make up the network backbone. The interplay between species’ chemical properties and transport is characterized by a non-dimensional Damköhler (Da) number. We show that the spatial distribution of reactions is sensitive to Da, which subsequently influences late-time tailing behavior in outlet breakthrough time distributions. The results of this study provide initial insights into how an irreversible reaction occurs during transport in a fracture network, using a methodology that can be applied to study reactive transport in a wide range of fractured media environments and contexts.

58 GEOSCIENCES↗
DOE OSTI2021

Surface Functionalization of Black Phosphorus with Nitrenes: Identification of P=N Bonds Using Isotopic Labeling

Abstract: Surface functionalization of two-dimensional crystals is a key path to tuning their intrinsic physical and chemical properties. However, synthetic protocols and experimental strategies to directly probe chemical bonding in modified surfaces are scarce. Introduced here is a mild, surface-specific protocol for the surface functionalization of few-layer black phosphorus nanosheets using a family of photolytically generated nitrenes (RN) from the corresponding azides. By embedding spectroscopic tags in the organic backbone, a multitude of characterization techniques are employed to investigate in detail the chemical structure of the modified nanosheets, including vibrational (infrared, Raman), Xray (emission, photoelectron), CPMAS NMR, and UV-vis spectroscopy. To directly probe the functional groups introduced on the surface, R fragments were selected such that in conjunction with vibrational spectroscopy, 15N-labeling experiments, and DFT methods, diagnostic P=N vibrational modes indicative of iminophosphorane units on the nanosheet surface could be conclusively identified.

Walz, Kendall↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗
DOE OSTI2023

Limitations of cetane number to predict transient combustion phenomena in high-pressure fuel sprays

Fundamental understanding of in-cylinder processes in diesel engines is important to screen emerging biofuels and advanced combustion modes that can reduce greenhouse gas emissions and regulated pollutants including soot. In this study, the role of fuel properties on spray development and combustion is investigated by systematically isolating chemical and thermophysical effects. Three different fuels are considered, two with similar chemical properties and two with similar thermophysical properties with one fuel common to both groups. Experiments are performed in a constant-pressure flow chamber de-signed to provide stable test conditions and facilitate acquisition of at least 150 injections in quick succession for each fuel under reacting conditions at high-pressure, high-temperature ambient conditions using a modified conventional diesel engine injector. Further, high speed optical diagnostics including rainbow schlieren deflectometry, OH* chemiluminescence, and a two-color pyrometry system are employed to simultaneously image the transient spray and reacting jets. Image analysis is performed to determine liquid length, vapor penetration length, timing and location of first stage and main ignition events, lift-off location, total soot mass, and more. Results show that fuels with similar chemical properties or cetane number (CN) exhibit similar delay times for first stage and main ignition events as may be expected, but very different liquid length, first stage and main ignition locations, lift-off length, apparent turbulent flame speed, and soot formation. As such, the ability to characterize candidate biofuels with CN or other parameters derived from simple flame configurations is called into question. In this study, thermo-physical properties controlling the liquid length are identified as the main contributing factor for the observed differences.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

High‐Resolution Electron Tomography of Ultrathin Boerdijk–Coxeter–Bernal Nanowire Enabled by Superthin Metal Surface Coating

Abstract The rapid advancement of transmission electron microscopy has resulted in revolutions in a variety of fields, including physics, chemistry, and materials science. With single‐atom resolution, 3D information of each atom in nanoparticles is revealed, while 4D electron tomography is shown to capture the atomic structural kinetics in metal nanoparticles after phase transformation. Quantitative measurements of physical and chemical properties such as chemical coordination, defects, dislocation, and local strain have been made. However, due to the incompatibility of high dose rate with other ultrathin morphologies, such as nanowires, atomic electron tomography has been primarily limited to quasi‐spherical nanoparticles. Herein, the 3D atomic structure of a complex core–shell nanowire composed of an ultrathin Boerdijk–Coxeter–Bernal (BCB) core nanowire and a noble metal thin layer shell deposited on the BCB nanowire surface is discovered. Furthermore, it is demonstrated that a new superthin noble metal layer deposition on an ultrathin BCB nanowire could mitigate electron beam damage using an in situ transmission electron microscope and atomic resolution electron tomography. The colloidal coating method developed for electron tomography can be broadly applied to protect the ultrathin nanomaterials from electron beam damage, benefiting both the advanced material characterizations and enabling fundamental in situ mechanistic studies.

Song, Xiaohui↗
DOE OSTI2023

Chemical composition, coordination, and stability of Ca–organic associations in the presence of dissolving calcite

Environmental biotic and abiotic factors and soil physical, mineralogical, and chemical properties control the chemical composition of soil organic matter (SOM). Particularly, soil mineralogy and the presence of multivalent cations affect SOM labile fraction composition, stability, and environmental persistence. The persistence of SOM in aridic or limestone deposit derived soils, i.e., calcareous soils, has been partially attributed to SOM stabilization through adsorption or inclusion into the calcite mineral structure. Recently, however, it was shown that Ca(aq) released during calcite dissolution formed aqueous Ca–organic associations with OM components, which were unbound to mineral surface sites. This study investigates the structure, composition, and coordination of these associations by characterizing lyophilized Ca–organic containing solutions with spectromicroscopy [Scanning Transmission X-ray Microscopy (STXM)] and with a nanoimaging chemical probe [Infrared scattering-Scanning Nearfield Optical Microscopy (IR s-SNOM)]. Chemical stability of Ca–organic associations is furthermore determined with pyrolysis mass spectrometry analysis. The results demonstrate that Ca–organic associations are formed in the presence of dissolving calcite and OM components relevant to soil chemistry, i.e., lignin and amino acids. This study further reveals a spatial homogeneity of solution-derived (bi) carbonate in Ca–organic associations indicating for the first time that an inorganic anion, such as (bi)carbonate, may be part of these associations. Most likely, Ca ions are bound to both the (bi)carbonate and the organic components. These Ca (bi)carbonate–organic associations seem to have greater chemical stability than the pristine organic mixtures and, possibly, a higher environmental stability and reduced mineralization rate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Production of Cf-252 and other transplutonium isotopes at Oak Ridge National Laboratory

In 1957 Glenn T. Seaborg conceived and advocated for the construction of the High Flux Isotope Reactor (HFIR) and the Transuranium Processing Plant (since then renamed the Radiochemical Engineering Development Center, or REDC) at Oak Ridge National Laboratory. There, heavily shielded hot cells, glove boxes, and laboratories allow recovery of transuranium elements produced in substantial quantities. Seaborg’s vision of HFIR and REDC producing milligram quantities of berkelium, californium, and einsteinium has been fulfilled beginning in 1966 through May 2019 with 78 production campaigns yielding a cumulative totals of 1.2 g of 249 Bk, 10.2 g of 252 Cf, 39 mg of 253 Es, and 15 pg of 257 Fm. Notably, 252 Cf is a neutron source used in many industrial applications including oil exploration; process control systems for the cement industry, coal analysis, and power production; sources to start nuclear reactors and perform nondestructive materials analyses; homeland security and national defense detection devices; and medical research. Isotopes made available through transplutonium production at HFIR/REDC have enabled scientists to study the nuclear properties and reactions, chemical properties, optical properties, and solid-state properties of transplutonium elements. Long-lived isotopes have served as targets in heavy ion accelerators to produce heavier elements leading to the discovery of 104 Rf, 105 Db, 106 Sg, 113 Nh, 114 Fl, 115 Mc, 116 Lv, 117 Ts, and 118 Og. This paper reviews the evolution of the processing flowsheets to produce, separate, and purify transplutonium isotopes, which have evolved over 50 years of operation at HFIR and REDC, and summarizes directions of future work to improve the efficiency of the production operations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Intercalation-Induced Reversible Electrochromic Behavior of Two-Dimensional Ti 3 C 2 T x MXene in Organic Electrolytes

MXenes, a large family of two-dimensional materials, have attracted tremendous attention due to their unique physical and chemical properties. Reversible ion intercalation between MXene layers allows modification of the optical, thermal, magnetic, and chemical properties. The electrochemical charge/discharge of MXenes in aqueous electrolytes was reported to lead to reversible electrochromic behavior. In this work, the electrochromic effect of semitransparent Ti 3 C 2 T x MXene film was probed by electrochemical intercalation of Li ions. Correspondingly, a peak shift of 100 nm was observed in the UV-vis spectrum. By combining in-situ Raman spectroscopy, in-situ X-ray diffraction, and density functional theory calculations, we show that the electrochromic shift is primarily due to the formation of robust O-Li bonds and the emerging bands induced changes of inter-band excitations. Finally, understanding the mechanism of electrochromic behavior in Ti 3 C 2 T x lays the foundations of designating 2D materials with durable, controllable, and efficient intercalation-induced electrochromic behaviors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Numerical Study of PAHs and Soot Emissions from Gasoline–Methanol, Gasoline–Ethanol, and Gasoline– n -Butanol Blend Surrogates

Soot formation is an intricate phenomenon, and soot propensity of a fuel is interwoven with the fuel composition, physical and chemical properties, and combustion environment. Here, the present study examines the hypothesis that in addition to the chemical composition of the fuel, the sooting nature of the fuel is closely coupled with its chemical property known as octane sensitivity (S). With this motivation, the present study numerically investigates the effects of gasoline surrogate composition and its property, octane sensitivity (S), on polycyclic aromatic hydrocarbons (PAHs) and soot emissions. Four-component toluene primary reference fuel (TPRF)–alcohol blends, comprising iso-octane, n-heptane, toluene, and one of the three different alcohols- methanol, ethanol, and n-butanol, are used as gasoline surrogates. A total of 320 TPRF–alcohol mixtures, with S in the range of 1–10, are examined under laminar counterflow diffusion flame conditions. A detailed chemical mechanism coupled with a comprehensive soot model, which includes reactions for soot inception, surface growth, PAH condensation, and oxidation, is adopted. The analysis indicates that the toluene content in the fuel mixture has a prominent effect, while the alcohol content and octane sensitivity of the fuel have a weak correlation with the PAHs and soot. Thus, it is not clear if any of these three variables, namely, toluene content in the fuel, alcohol content in the fuel, and S, are individually sufficient to characterize the PAHs and soot across various blends. For this reason, a new variable (X CHO ) based on the elemental composition of the fuel mixture is identified and it is shown that X CHO along with S of the fuel characterize soot emissions satisfactorily. Further, a reaction path analysis indicates that the efficacy of alcohols in reducing soot emissions follows the order: methanol > ethanol > n-butanol.

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

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain

Language models are transforming materials-aware naturallanguage processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Moreover, small language models are on the rise because some of them can perform better than large language models (LLMs) when given domain-specific questionanswer tasks, especially about an application area that relies on a highly specialized vernacular, such as materials science. We therefore present a new class of MechBERT language models for understanding mechanical stress and strain in materials. These employ Bidirectional Encoder Representations for transformer (BERT) architectures. We showcase four MechBERT models, all of which were pretrained on a corpus of documents that are textually rich in chemicals and their stress–strain properties and were fine-tuned on question-answering tasks. We evaluated the level of performance of our models on domain-specific as well as general English-language question-answer tasks and also explored the influence of the size and type of BERT architectures on model performance. We find that our MechBERT models outperform BERT-based models of the same size and maintain relevancy better than much larger BERT-based models when tasked with domain-specific question-answering tasks within the stress–strain engineering sector. These small language models also enable much faster processing and require a much smaller fraction of data to pretrain them, affording them greater operational efficiency and energy sustainability than LLMs.

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

Tailoring the Properties of Chemically Recyclable Polyethylene‐Like Multiblock Polymers by Modulating the Branch Structure

Abstract Developing plastics that fill the need of polyolefins yet are more easily recyclable is a critical need to address the plastic waste crisis. However, most efforts in this vein have focused on high‐density polyethylene (PE), while many different types of PE exist. To create broadly sustainable PE with modular properties, we present the synthesis, characterization, and demonstration of materials applications for chemically recyclable PE‐like multiblock polymers prepared from distinct hard and soft blocks using ruthenium‐catalyzed dehydrogenative polymerization. By altering the branching pattern within the soft blocks, a series of PE‐like multiblock polymers were synthesized with tunable glass transition temperatures ( T g ) while maintaining consistent high melting temperatures ( T m ). A clear U‐shape trend between T g and mechanical properties was found, showcasing their potential as sustainable materials with tailored properties spanning commercial linear low‐density polyethylene (LLDPE) and low‐density polyethylene (LDPE). These materials offer adjustable adhesive strength to metal and demonstrate chemical recyclability and selective depolymerization in mixed plastic streams, promoting circularity and separation.

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

Tailoring the Properties of Chemically Recyclable Polyethylene‐Like Multiblock Polymers by Modulating the Branch Structure

Abstract Developing plastics that fill the need of polyolefins yet are more easily recyclable is a critical need to address the plastic waste crisis. However, most efforts in this vein have focused on high‐density polyethylene (PE), while many different types of PE exist. To create broadly sustainable PE with modular properties, we present the synthesis, characterization, and demonstration of materials applications for chemically recyclable PE‐like multiblock polymers prepared from distinct hard and soft blocks using ruthenium‐catalyzed dehydrogenative polymerization. By altering the branching pattern within the soft blocks, a series of PE‐like multiblock polymers were synthesized with tunable glass transition temperatures ( T g ) while maintaining consistent high melting temperatures ( T m ). A clear U‐shape trend between T g and mechanical properties was found, showcasing their potential as sustainable materials with tailored properties spanning commercial linear low‐density polyethylene (LLDPE) and low‐density polyethylene (LDPE). These materials offer adjustable adhesive strength to metal and demonstrate chemical recyclability and selective depolymerization in mixed plastic streams, promoting circularity and separation.

Zhao, Yucheng↗