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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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126 records · Page 7

Understanding and tuning organocatalysts for versatile condensation polymer deconstruction

Plastics are widely used for their durability and versatility, but recycling remains a major challenge, especially for mixed or contaminated waste. Mechanical recycling works well for clean, single-polymer streams like PET but has limited efficiency for complex waste streams. Chemical recycling, particularly glycolysis, is often employed to selectively deconstruct condensation polymers under mild conditions. This study explores catalyst design for glycolysis using linear free energy (Hammett) analysis to evaluate how catalyst structure influences polymer deconstruction. Polycaprolactone (PCL) is used as a model polyester due to its solubility and low deconstruction temperature. Triazabicyclodecene (TBD) paired with benzoic acid derivatives depicts a clear linear trend in depolymerization rates with Hammett values. TBD with p-aminobenzoic acid (PABA) stands out for its catalytic efficiency, thermal stability, and scalability, along with PABA's commercial availability as vitamin B-10. The TBD : PABA catalyst not only effectively breaks down PCL but also enables sequential deconstruction of polycarbonate, PET, and Nylon in mixed waste streams. These results highlight the value of Hammett-guided catalyst design and establish TBD : PABA as a promising, scalable organocatalyst for mixed plastic recycling, enabling recovery of individual polymer building blocks from blended waste and offering a practical route toward circular plastics.

Zheng, Jackie [Univ. of Tennessee, Knoxville, TN (↗

Camelina circRNA landscape: Implications for gene regulation and fatty acid metabolism

Abstract Circular RNAs (circRNAs) are closed‐loop RNAs forming a covalent bond between their 3′ and 5′ ends, the back splice junction (BSJ), rendering them resistant to exonucleases and thus more stable compared to linear RNAs. Identification of circRNAs and distinction from their cognate linear RNA is only possible by sequencing the BSJ that is unique to the circRNA. CircRNAs are involved in the regulation of their cognate RNAs by increasing transcription rates, RNA stability, and alternative splicing. We have identified circRNAs from C. sativa that are associated with the regulation of germination, light response, and lipid metabolism. We sequenced light‐grown and etiolated seedlings after 5 or 7 days post‐germination and identified a total of 3447 circRNAs from 2763 genes. Most circRNAs originate from a single homeolog of the three subgenomes from allohexaploid camelina and correlate with higher ratios of alternative splicing of their cognate genes. A network analysis shows the interactions of select miRNA:circRNA:mRNAs for regulation of transcript stabilities where circRNA can act as a competing endogenous RNA. Several key lipid metabolism genes can generate circRNA, and we confirmed the presence of KASII circRNA as a true circRNA. CircRNA in camelina can be a novel target for breeding and engineering efforts.

Utley, Delecia [Department of Plant and Microbial ↗

A Novel Measurement of the Anomalous Muon Spin Precession Frequency in the Muon $g-2$ Experiment at Fermilab

The Muon $g-2$ Experiment operated at Fermi National Accelerator Laboratory (FNAL, or Fermilab) between 2018 and 2023 to produce the world's most precise measurement of the muon's \textit{anomalous magnetic moment}, $a_\mu = \frac{g_\mu - 2}{2}$, which expresses the relative deviation in the muon's $g$-factor from a baseline theoretical expectation that $g_\mu = 2$. In the Standard Model of particle physics, $g_\mu > 2$ and hence $a_\mu > 0$ by a calculable amount that depends on all possible interactions between the muon and all other fundamental particles, including any potentially undiscovered interactions beyond the Standard Model. For this reason, measurements of the electron anomaly $a_e$ and later the muon anomaly $a_\mu$ have helped guide the development of the Standard Model since the inception of quantum field theory, and the measured value of $a_\mu$ provides a valuable constraint for new hypotheses that extend the Standard Model. As of 2006, the leading measurement and Standard Model prediction for $a_\mu$ exhibited tension at the level of about three standard deviations, motivating an improved measurement at Fermilab that could test the tension more precisely. The experiment functions by storing a polarized beam of $\mu^+$ in a uniform magnetic field, which simultaneously induces circular motion and spin precession. As the stored muons undergo the Michel decay $\mu^+ \to e^+ + \nu_e + \bar{\nu}_\mu$, mediated by the parity-violating weak interaction, the rest-frame $e^+$ emission direction is correlated with the parent $\mu^+$ spin orientation. Boosting into the laboratory frame encodes this correlation in the decay $e^+$ energy, which is higher when the emission (i.e. $\mu^+$ spin direction) is aligned with the $\mu^+$ momentum, and lower when opposite. Detectors then count the rate of high-energy decay $e^+$, which modulates at the difference between the $\mu^+$ revolution and spin precession frequencies. This observed frequency, called the \textit{anomalous spin precession frequency} $\omega_a$, is directly proportional to $a_\mu$. The extraction of $\omega_a$ proceeds by fitting the time spectrum of detected $e^+$, which requires precise modeling of the $\omega_a$ oscillation as well as any perturbations from beam dynamics and detector acceptance. Using the $\omega_a$ analysis presented in this work, based on Runs 4 -- 6 of the Muon $g-2$ Experiment at Fermilab, we find that $a_\mu = 0.001\,165\,920\,738(162)$ with a relative uncertainty of 139 parts per billion.

Barrett, Tyler [Cornell U.]↗

Influence of Control and Limiter Schemes on Sequence-Domain Fault Models of Grid-Forming Inverter-Interfaced Distributed Generators

Unlike synchronous generators, the fault response of grid-forming (GFM) inverter-interfaced distributed generators (IIDGs) is notably governed by the selection of control and current limiting strategies rather than inherent physical traits. While recent research has focused on the sequence domain fault model of GFM IIDGs, a research gap exists in elucidating the influence of control and current limiting schemes on this model's characteristics. This article aims to fill this void by examining how different control and current limiting schemes influence the positive and negative sequence impedances in the phasor-domain fault model of GFM IIDGs. This investigation encompasses droop-based, virtual synchronous machine-based, and virtual oscillator-based reference generation controls alongside rotating and stationary reference-frame-based voltage controls. Furthermore, saturation-based, latching-based, circular and virtual impedance-based current limiting schemes are analyzed. To achieve this goal, a thorough numerical simulation study is conducted. Findings indicate that outer reference generation controls exhibit minimal impact. Conversely, the choice of voltage control and various current limiting schemes emerge as the predominant factors shaping the sequence models of GFM IIDGs. These analyses and results are instrumental in devising reliable protection strategies within inverter-based grids, as a comprehensive understanding of electrical elements in the sequence domain is imperative for effective protective measures.

current limiters↗

Global Regulations for Sustainable Battery Recycling: Challenges and Opportunities

With the rapid expansion of transportation electrification worldwide, the demand for electric vehicles (EVs) has increased dramatically, creating new and sustainable growth opportunities for the global economy. However, as the most expensive component of EVs, lithium-ion batteries pose significant sustainability challenges due to raw material consumption and supply chain constrains, as well as the complexities of end-of-life battery disposal and recycling. To address these concerns, many countries are actively establishing regulations to promote sustainable pathways for battery reuse and recycling. Despite these efforts, existing battery recycling regulations remain often inefficient and vary significantly across different countries in legal enforcement, producer responsibility, waste classification, recycling targets, design standards, public engagement, and financial incentives, particularly given the complexities of the global supply chain and resource distribution within the battery industry. Understanding these regulatory differences and establishing a unified framework are therefore crucial to ensuring sustainable and efficient battery recycling. This review provides a comprehensive analysis of the necessity of establishing robust regulations for sustainable development of battery recycling industry. The evolution and refinement of battery recycling regulations are deeply reviewed to identifying persistent gaps and challenges in key countries. Furthermore, we discuss the challenges associated with regulatory enforcement and propose strategies for developing a more cohesive legislative framework to ensure the effective utilization of retired batteries.

25 ENERGY STORAGE↗

ZPRD Database: ZPPR-15 Monte Carlo Results

The ZPPR-15 experiments [1] were mockups of a 330 MWe Integral Fast Reactor (IFR). The ZPPR-15 assembly consisted of a clean, two zone, approximately circular core surrounded by a thin depleted uranium (DU) blanket with sodium (Na) cooling and a thick stainless steel reflector. The ZPPR-15 program was conducted in four phases: A, B, C, and D. Each phase was marked by a particular composition of the reference assembly, with the last three being representative of the three stages of the IFR fuel cycle. This report documents all MCNP [2] runs of the ZPPR-15 loadings that were included in the ZPRD database on GitLab. In the present work, eigenvalues and associated standard deviations were computed for each ZPPR-15 loading with the use of the three data libraries (ENDF/B-VII.0 [3], ENDF/B-VII.1 [4] and ENDF/B-VIII.0 [5]). Relevant results from the obtained outputs are also discussed in this report with two main objectives: a) provide and evaluate updated calculated values with respect to previous reports, notably Refs. [6] and [7] that were based on the use of the ENDF/B-VII.0 library. b) address any change in the observed reactivity effects relevant to the analysis of the experimental data when a different data library is used (previously reported results were mostly based on the use of ENDF/B-VII.0 data only).

Aliberti, Gerardo↗

The DESI Y1 RR Lyrae catalog I: Empirical modeling of the cyclic variation of spectroscopic properties and a chemodynamical analysis of the outer halo

We present the catalog of RR Lyrae stars observed in the first year of operations of the Dark Energy Spectroscopic Instrument (DESI) survey. This catalog contains 6,240 RR Lyrae stars out to $\sim100$ kpc from the Galactic center and over 12,000 individual epochs with homogeneously-derived stellar atmospheric parameters. We introduce a novel methodology to model the cyclical variation of the spectroscopic properties of RR Lyrae from single-epoch measurements. We employ this method to infer the radial velocity and effective temperature variation of fundamental mode and first-overtone RR Lyrae stars and to determine their systemic velocities and mean temperatures. For fundamental mode pulsators, we obtain radial velocity curves with amplitudes of $\sim$30--50 km s$^{-1}$ and effective temperature curves with 300--1,000 K variations, whereas for first-overtone pulsators these amplitudes are $\sim20$ km s$^{-1}$ and $\sim 600$ K, respectively. We use our sample to study the metallicity distribution of the halo and its dependence on Galactocentric distance ($R_{\rm GC}$). Using a radius-dependent mixture model, we split the data into chemodynamically distinct components and find that our inner halo sample ($R_{\rm GC}\lesssim50$ kpc) is predominantly composed of stars with [Fe/H] $\sim-1.5$ dex and largely radial orbits (with an anisotropy parameter $\beta\sim0.94$), that we associate with the Gaia-Sausage-Enceladus merger event. Stars in the outer halo field exhibit a broader and more metal-poor [Fe/H] distribution with more circular orbits ($\beta\sim0.39$). The metallicity gradient of the metal-rich and the metal-poor components is found to be $0.005$ and $0.010$ dex kpc$^{-1}$, respectively. Our catalog highlights DESI's tantalizing potential for studying the Milky Way and the pulsation properties of RR Lyrae stars in the era of large spectroscopic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

Techno-Economic and Life Cycle Assessment of Chemical Recycling and Upcycling of Mixed Plastics Waste Containing Poly-vinyl-chloride

Developing technologies that completely remove chlorine from plastic waste can allow its chemical recycling and upcycling with catalytic methods. Here, this study compares eight processes involving different dechlorination methods (absorption columns, adsorption in beds of zeolites, catalytic dechlorination, and dissolution in ionic liquids) and chemical conversion technologies (incineration, pyrolysis, hydrogenolysis) to upgrade mixed plastics waste to various products (e.g., electricity, fuels, virgin polymers, and lubricant oil). The analysis determines that the absorption of chlorine in columns with basic aqueous solutions is limited to plastics waste with PVC concentrations below 0.1%. Dissolution in ionic liquids is not cost-competitive. On the contrary, two-step processes with catalytic dechlorination followed by thermochemical catalytic depolymerization, either pyrolysis or hydrogenolysis, significantly improve process economics and emissions. The most economically viable alternative is hydrogenolysis for producing lubricants, while the technology with the lowest global warming potential is chemical recycling via catalytic pyrolysis.

circular economy↗

Contextualizing Wind Turbine Blade Waste: Comparison to Other Global Waste Streams

Worldwide wind energy generation capacity has grown rapidly over the past several decades, and wind turbines installed at the beginning of this wave of growth are approaching the end of their design lifetimes. As an increasing number of wind power plants reach their end of life, both decommissioning and repowering (i.e., dismantling or refurbishing existing turbines and commissioning new ones) will produce waste material from the retired wind turbines, foundations, and balance of plant. However, the amount and type of waste, particularly for wind blades, is often mischaracterized. Although wind turbine components are largely recyclable, the blades are typically made of fiberglass composites, which can present challenges for material recovery and reuse. Within the USA, the accumulation of wind turbine blades in landfills has raised questions about whether the continued expansion of wind energy is sustainable if it results in substantial future waste. This study compares the mass and volume of potential global wind blade waste to other waste streams. It also discusses the materials used to manufacture wind turbine blades and summarizes current options for material redesign, recycling (recovery and reuse), repurposing, and disposal of used blades. The analysis indicates that, although wind turbine blades could represent 14% of the composite market by 2027, the potential future mass and volume of wind turbine blade waste is relatively small compared to other industries. These findings suggest that although the development of scalable, economically viable, and environmentally sustainable methods for wind turbine manufacturing, repurposing, and recycling is important, it may make sense to take advantage of synergies among multiple industries in recycling composite waste, rather than focusing solely on wind turbine blades. From a global perspective, larger sustainability, recycling, and waste stream reduction impacts can be made in other industries, such as transportation and construction.

17 WIND ENERGY↗

Critical Clearing Time for Droop-Controlled Grid-Forming Inverters With Circular Current Limiting and Virtual Impedance Current Limiting

The critical clearing time is a crucial parameter for assessing the large-signal stability of grid-forming (GFM) inverters. Variations in current limiters can significantly impact an inverter's transient behavior during large disturbances. Here, this letter introduces a method based on the power-angle relationship to evaluate the fault recovery of a droop-controlled GFM inverter under a three-phase bolted fault scenario, considering both the circular current limiter and the virtual impedance method. High-fidelity simulations validate the accuracy of the proposed formulas in estimating the critical clearing time of the GFM inverter.

42 ENGINEERING↗

Towards low-carbon low-energy concrete alternatives: Life cycle assessment of carbonated cementitious material-based precast panels

Cement is responsible for 22 % of all global CO 2 emissions from industrial processes. Technological innovation for developing and deploying of alternative materials will be required to decarbonize the cement industry. Carbonated cementitious materials (CCMs) are building materials that rely on carbon mineralization for their strength. A process-based cradle-to-gate life cycle assessment (LCA) was conducted to evaluate the global warming potential (GWP), cumulative energy demand, and water consumption of a lab-scale CCM-based precast panel compared to a conventional precast concrete panel. Since the CCM process is currently a lab-scale early-stage process, the CCM panel showed higher environmental impacts compared to the conventional panel. However, scenario analyses include mature production process scenarios. In conclusion, a sensitivity analysis revealed that the GWP of CCM can be lowered to below that of the conventional panel using polymers, fillers, low-carbon electricity sources, and optimized carbonation parameters.

36 MATERIALS SCIENCE↗

Cement and concrete as carbon sinks: Transforming a climate challenge into a carbon storage opportunity

Cement and concrete, while traditionally recognized as the main contributors to anthropogenic CO 2 emissions, also have untapped capacity to serve as substantial and scalable carbon sinks. This perspective examines how engineered mineral carbonation can transform cement-based materials into functional carbon storage systems, generating both environmental and economic value. We review the fundamental mechanisms of CO 2 uptake in cementitious systems, highlighting current limitations in reaction kinetics, phase control, and durability under varying environmental conditions. Emphasis is placed on the utilization of alkaline industrial residues and emerging magnesium-based cements, which offer synergistic pathways for carbon sequestration and circular resource use. We further assess the performance trade-offs associated with CO 2 uptake and the feasibility of deploying these technologies on industrial scales. A strategic roadmap is proposed that integrates scientific innovation, regulatory alignment, and carbon accounting in the life cycle to accelerate the adoption of carbon-storing concrete. This perspective provides a comprehensive framework to advance cement and concrete as engineered carbon sinks and supports the transition to a climate-positive construction industry.

Carbon storage↗

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗

Development of Advanced, Radiation Resistant, Optical-based Detector Technology for Future Experiments.

The primary objective of this project has been to advance the design of high-performance electromagnetic (EM) calorimeters for future particle physics experiments, to identify and measure the timing, position and energy of electrons, positrons and gamma rays, particularly in high-luminosity environments with intense radiation and pileup conditions. To meet such challenges, the proposed research has focused on the development of ultra-compact, radiation-hard calorimeter modules, to provide excellent timing, spatial, and energy resolution. The work aligns with the DOE’s Basic Research Needs (BRN) for High Energy Physics (HEP) Instrumentation and the research team contributes actively to the Coordinating Panel on Advanced Detectors (CPAD) RDC9 calorimetry collaboration in the USA and the European Committee on Future Accelerators (ECFA) DRD-CALO calorimetry collaboration at CERN, the European Laboratory for Particle Physics located in Geneva, Switzerland. The research builds on the RADiCAL (radiation-hard, ultra-compact) modular sampling calorimeter approach, developed by the research team, which employs dense and very bright optical materials such as LYSO:Ce scintillator plates that are interleaved with very dense tungsten plates to minimize detector size while optimizing performance. The modules are comparable in size to a human index finger, dimensionally 14 mm x 14 mm in cross section and 135 mm in length. And despite the small size, the structure is capable of providing excellent timing and energy resolution. This is facilitated through the use of specialized quartz capillaries filled with wavelength-shifting filaments, positioned at various depths along the length of a module, to collect and guide light signals to silicon photomultipliers (SiPMs) which detect and convert the optical signals to electronic signals for analysis. The primary goals of this project have been: (1) Achieve a timing resolution to σ t ≤ 30 ps for high-energy electrons and photons, important for their association with specific events produced in colliding-beam experiments and for the detection of decays-in-flight of long-lived particles. The project has achieved this goal in beam tests of a single RADiCAL module at CERN, during which a timing resolution of σ t = 27 ps was measured for electrons of energy E = 150 GeV. Based upon a mathematical fit to the data measured over a broad energy range from low energy to high energy, a resolution of σ t ≤ 18 ps has been estimated for electrons of very high (TeV) energy. From these measurements and with further expected technical improvements, the timing resolution should reach σ t ≤ 10 ps, important for searches for discovery physics in upcoming and future experiments. (2) Achieve an energy resolution of σ E / E ≤ 10% / $\sqrt{E}$. The project has yet to achieve this goal, but is close to it, having measured a value of σ E / E ≤ 15.9% / $\sqrt{E}$ using a modular array. Ultimately, the resolution goal is expected to be reached by adjustments to material thicknesses within the modules, which will improve the sampling fraction to measure more precisely the shower energy for lower energy particles. The versatility of the modular RADiCAL approach enables the testing of advanced materials, photosensors and electronics, developed in collaboration with CPAD RDC and ECFA DRD-CALO groups. The structure can distinguish electrons, positrons and gamma rays from hadrons and muons and beam-induced backgrounds, making it a valuable tool in a variety of detector environments, including future circular colliders (FCC-ee, FCC-hh) proposed for the European Laboratory for Particle Physics (CERN), the muon-collider proposed for Fermi National Accelerator Laboratory (Fermilab), and searches for new physics in beam-dump, fixed target and forward-physics experiments. And, while designed with particle physics applications in mind, the technologies developed in this project have the potential for application more broadly in particle and nuclear physics, materials science, and medical physics, underscoring the far-reaching potential of this line of instrumentation research and development.

47 OTHER INSTRUMENTATION↗

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗

The real-time data processing and acquisition system for Project 8 Phase II

In Phase II of the Project 8 neutrino mass experiment, electrons from the decays of tritium or 83m Kr are detected via their ≈26 GHz cyclotron radiation while contained within a circular waveguide. The signal from a given electron is characterized as a brief chirp, lasting ≲10 ms and changing in frequency by ≲1 MHz/ms. To detect these signals, the Project 8 collaboration developed a Data Acquisition (DAQ) system tailored to the signal properties. The DAQ is responsible for simultaneously selecting up to three 100 MHz-wide frequency windows to study, detect, and trigger on likely signals from different electron kinetic energies, and for writing the relevant data to disk. Finally, we describe the Phase II DAQ system in detail and address how the system was used for data-taking operations.

Data acquisition↗

Absorption dissymmetry factor enhancement: A data-driven approach to unravel the synthesis knobs of chiral 2D perovskites

Chiral 2D metal halide perovskites (MHPs) are promising for spin-optoelectronic applications, yet their absorption dissymmetry factor (g abs ) exhibits significant variability due to complex, co-dependent structural and experimental factors. Here, we established a data-driven framework using Pearson’s correlation, ANOVA, and Gaussian process regression to identify and model key synthesis “knobs” governing these properties. The analysis revealed that solvent choice is the primary factor driving variability. For acetonitrile-based films, g abs was maximized by optimizing annealing temperature and film thickness. Conversely, films from higher boiling point solvents showed complex dependencies on annealing temperature, excitonic integral intensity, and film texture. These statistical correlations provide a roadmap for the rational design of high-performance chiral MHPs and establish a foundation for future machine learning-driven material exploration.

ANOVA↗

Polymer Deconstruction and Redesign Strategies for Plastics Recycling

Advancing plastics recycling requires both the selective deconstruction of existing polymers and the design of new materials that enable efficient reuse without loss of performance. This perspective highlights an integrated approach that is rooted in polymer chemistry, catalysis, and process engineering which can enable a circular plastics economy. Here, we outline recent advances in catalytic, solvolytic, and enzymatic pathways for plastic deconstruction, and examine the molecular design principles driving next-generation recyclable-by-design and bio-based polymers. Despite these advances, major knowledge gaps remain in understanding the evolution of polymer morphology and catalyst structure during deconstruction, assessing deconstruction processes with realistic polymers, and offering redesigned polymers with competitive cost and environmental advantage over conventional plastics. United States Department of Energy (U.S. DOE) national laboratories offer unique capabilities to address these challenges through in situ and operando characterization, high-throughput experimentation, environmental studies, technoeconomic and life cycle assessment, scale-up support, and collaboration networks. Advances made in understanding plastic deconstruction mechanisms and structure-property correlations of redesigned polymers inform emerging research directions including autonomous experimentation, real-time feedback-enabled process optimization, and protein engineering for enzymatic depolymerization.

36 MATERIALS SCIENCE↗