SEARCH · Engineering Papers

Results for “Robust optimization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,333 records · Page 74

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Real-space visualization of short-range antiferromagnetic correlations in a magnetically enhanced thermoelectric

Short-range magnetic correlations can significantly increase the thermopower of magnetic semiconductors, representing a noteworthy development in the decades-long effort to develop high-performance thermoelectric materials. Here, we reveal the nature of the thermopower-enhancing magnetic correlations in the antiferromagnetic semiconductor MnTe. Using magnetic pair distribution function analysis of neutron scattering data, we obtain a detailed, real-space view of robust, nanometer-scale, antiferromagnetic correlations that persist into the paramagnetic phase above the Neel temperature $T_N$ = 307 K. In this work, the magnetic correlation length in the paramagnetic state is significantly longer along the crystallographic c axis than within the ab plane, pointing to anisotropic magnetic interactions. Ab initio calculations of the spin-spin correlations using density functional theory in the disordered local moment approach reproduce this result with quantitative accuracy. These findings constitute the first real-space picture of short-range spin correlations in a magnetically enhanced thermoelectric and inform future efforts to optimize thermoelectric performance by magnetic means.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Li x (C 5 H 5 N) y Fe 2– z Se 2 : A Defect-Resilient Expanded-Lattice High-Temperature Superconductor

Two-dimensional iron chalcogenide intercalates display a remarkable correlation of the interlayer spacing with enhancement of the superconducting critical temperature (T c ). In this work, synchrotron X-ray absorption (XAS; at the Fe and Se K-edges) and emission (XES; at the Fe Κβ) spectroscopies allow one to discuss how the important rise of T c (~44 K) in the molecule-intercalated Li x (C 5 H 5 N) y Fe 2-z Se 2 relates to the electronic and local structural changes felt by the inorganic host upon doping (x). XES shows that widely separated layers of edge-sharing FeSe 4 tetrahedra carry low-spin moieties, with a local Fe magnetic moment slightly reduced compared to the parent β-Fe 2-z Se 2 . Pre-edge XAS expresses the progressively reduced mixing of metal 3d-4p states upon lithiation. Doping-mediated local lattice modifications, probed by conventional T c optimization measures (cf. the anion height and FeSe 4 tetrahedra regularity), become less relevant when layers are spaced far away. On the basis of extended X-ray absorption fine structure, such distortions are compensated by a softer Fe network that relates to Fe-site vacancies, alleviating electron-lattice correlations and superconductivity. Density functional theory (DFT) guided modification of the isolated Fe 2-z Se 2 (z, vacant sites) planes, resembling the host layers, identify that Fe-site deficiency occurs at low energy cost, giving rise to stretched Fe sheets, in accordance with experiments. Here, the robust high-T c in Li x (C 5 H 5 N) y Fe 2-z Se 2 , arises from the interplay of electron-donating spacers and the iron selenide layer's tolerance to defect chemistry, a tool to favorably tune its Fermi surface properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗

DOE OSTI2021

RISE: robust iterative surface extension for sub-nanometer X-ray mirror fabrication

Precision optics have been widely required in many advanced technological applications. X-ray mirrors, as an example, serve as the key optical components at synchrotron radiation and free electron laser facilities. They are rectangular silicon or glass substrates where a rectangular Clear Aperture (CA) needs to be polished to sub-nanometer Root Mean Squared (RMS) to keep the imaging capability of the incoming X-ray wavefront at the diffraction limit. The convolutional polishing model requires a CA to be extended with extra data, from which the dwell time is calculated via deconvolution. However, since deconvolution is very sensitive to boundary errors and noise, the existing surface extension methods can hardly fulfill the sub-nanometer requirement. On one hand, the figure errors in a CA were improperly modeled during the extension, leading to continuity issues along the boundary. On the other hand, uncorrectable high-frequency errors and noise were also extended. In this study, we propose a novel Robust Iterative Surface Extension (RISE) method that resolves these problems with a data fitting strategy. RISE models the figure errors in a CA with orthogonal polynomials and ensures that only correctable errors are fit and extended. Combined with boundary conditions, an iterative refinement of dwell time is then proposed to compensate the errors brought by the extension and deconvolution, which drastically reduces the estimated figure error residuals in a CA while the increase of total dwell time is negligible. To our best knowledge, RISE is the first data fitting-based surface extension method and is the first to optimize dwell time based on iterative extension. An experimental verification of RISE is given by fabricating two elliptic cylinders (10 mm × 80 mm CAs) starting from a sphere with a radius of curvature around 173 m using ion beam figuring. The figure errors in the two CAs greatly improved from 204.96 nm RMS and 190.28 nm RMS to 0.62 nm RMS and 0.71 nm RMS, respectively, which proves that RISE is an effective method for sub-nanometer level X-ray mirror fabrication.

36 MATERIALS SCIENCE↗

DOE OSTI2022

LensingETC: A Tool to Optimize Multifilter Imaging Campaigns of Galaxy-scale Strong Lensing Systems

Imaging data is the principal observable required to use galaxy-scale strong lensing in a multitude of applications in extragalactic astrophysics and cosmology. In this paper, we develop Lensing Exposure Time Calculator (LensingETC; to optimize the efficiency of telescope-time usage when planning multifilter imaging campaigns for galaxy-scale strong lenses. This tool simulates realistic data tailored to specified instrument characteristics and then automatically models them to assess the power of the data in constraining lens model parameters. We demonstrate a use case of this tool by optimizing a two-filter observing strategy (in the IR and ultraviolet-visual (UVIS)) within the limited exposure time per system allowed by a Hubble Space Telescope (HST) Snapshot program. We find that higher resolution is more advantageous to gain constraining power on the lensing observables, when there is a trade-off between signal-to-noise ratio and resolution; for example, between the UVIS and IR filters of the HST. We also find that, whereas a point-spread function (PSF) with sub-Nyquist sampling allows the sample mean for a model parameter to be robustly recovered for both galaxy–galaxy and point-source lensing systems, a sub-Nyquist-sampled PSF introduces a larger scatter than a Nyquist-sampled one in the deviation from the ground truth for point-source lens systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

DOE OSTI2025

An improved guess for the variational calculation of charge-transfer excitations in large systems

Ab initio quantum-chemical methods that perform well for computing the electronic ground state are not straightforwardly transferable to electronically excited states, particularly in large molecular systems. Wave function theory offers high accuracy, but is often prohibitively expensive. Methods based on time-dependent density functional theory (TD-DFT) are crucially sensitive to the chosen exchange-correlation functional (XCF) parameterization, and system-specific tuning protocols were therefore proposed to address the method's robustness. Methods based on the variational relaxation of the excited-state electron density showcased promising results for the calculation of charge-transfer excitations, but the complex shape of the electronic hypersurface makes convergence to a specific excited state much more difficult than for the ground state when standard variational techniques are applied. We address the latter aspect by providing suitable initial guesses, which we obtain by two separate constrained algorithms. Combined with the squared-gradient minimization algorithm for all-electrons relaxation in a freeze-and-release scheme (FRZ-SGM), we demonstrate that orbital-optimized density functional theory (OO-DFT) calculations can reliably converge to the charge-transfer states of interest even for large molecular systems. We test the FRZ-SGM method on a phenothiazine-anthraquinone CT excitation in a supramolecular Pd(II) coordination cage complex as a function of the cage conformation. This compound has been studied experimentally prior to our work. We compare this freeze-and-release scheme to two XCF reparameterizations, which were recently proposed as low-cost TD-DFT-based alternatives to variational methods. Two dye-semiconductor complexes, which were previously investigated in the context of photovoltaic applications, serve as a second example to investigate the convergence and stability of the FRZ-SGM approach. Our results demonstrate that FRZ-SGM provides reliable convergence for charge-transfer excited states and avoids variational collapse to lower-lying electronic states, whereas time-dependent DFT calculations with an adequate tuning procedure for the range-separation parameter provide a computationally efficient initial estimate of the corresponding energies, with a computational cost comparable to that of configuration-interaction singles (CIS) calculations.

Bogo, Nicola↗

DOE OSTI2020

Sorption Enhanced Mixed Matrix Membranes for Hydrogen (H 2 ) Purification and Carbon Dioxide (CO 2 ) Capture

The technical objective of this project was to develop sorption enhanced mixed matrix membranes with H 2 permeance of 500 gas permeance units (GPU) and H 2 /CO 2 selectivity of 30 at 150-200 °C. These membranes will be the central component in the design of membrane based systems for 90% capture of CO 2 from coal-derived syngas, with 95% CO 2 purity at a cost of electricity 30% less than baseline capture approaches. The unique approach in this proposal is to design crosslinked polymers containing Pd-based nanoparticles achieving strong H 2 sorption and size sieving ability and thus H 2 /CO 2 selectivity. The specific objectives for each budget period (BP) are described below. BP 1: Identify polymer matrix with strong size sieving ability and palladium (Pd)-containing nanomaterials to prepare freestanding mixed matrix films with H 2 permeability of 50 Barrer and H 2 /CO 2 selectivity of 30 at 150-200°C with simulated syngas. BP 2: Prepare and optimize thin film mixed matrix composite membranes materials with H 2 permeance of 500 GPU and H 2 /CO 2 selectivity of 30 at 150-200 °C, and complete the modification of the membrane test unit for the field test in the BP 3. BP 3: Conduct a 20-day field test of the membranes with real syngas at Center for Advanced Energy Research (CAER) of the University of Kentucky (UKy). During the BP2, we have successfully prepared thin-film composite (TFC) membranes based on mixed matrix materials (MMMs) containing Pd nanoparticles in polymers, and demonstrated their superior and robust performance for H 2 /CO 2 separation at 150 – 225 °C. (1) Production of the Pd based nanoparticles with a diameter of 4 nm has been scaled up to 200 mg/day. (2) We have prepared TFC membranes with H 2 permeance above 500 GPU and H 2 /CO 2 selectivity above 30 at temperatures up to 225 °C, which meet the targets for the BP2. (3) We have conducted parametric studies of TFC membranes with a mixed gas containing H 2 S and H 2 O and demonstrated the stability of the membranes. (4) We have established a new testing plan at the Center for Advanced Energy Studies (CAER) at the University of Kentucky because NCCC decided to shut down their gasifier. During this project, four Ph.D. students received the inter-disciplinary training and graduated, including Shailesh Konda, Maryam Omidvar, Deqiang Yin, and Lingxiang Zhu. One postdoctoral researcher (Dr. Liang Huang) and two Ph.D. students (Abhishek Kumar and Hien Nguyen) are involved in this project. The project leads to one provisional patent application, eight peer-reviewed articles, and one manuscript in preparation. The details are shown below.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Immobilization of Urease for continuous flow conversion of waste urea

An efficient and robust system for the urease catalyzed conversion of urea to ammonia has been developed using urease immobilized on modified agarose beads. Two different immobilization strategies, adsorption and covalent binding were studies using six different types of modified agarose beads. The immobilization of urease on each of the beads was studied at different concentrations and times using immobilization efficiency as a measure of success. The data from these experiments was used to identify potential candidates for immobilization scale up and implementation into the continuous flow system. The enzyme was then immobilized on the candidates in a packed bed reactor and the optimal flow rate and storage stability was determined. Future work will utilize the data obtained from these experiments to expand to other resins and enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities

The execution and analysis of complex experiments are challenged by the vast dimensionality of the underlying parameter spaces. Although an increase in data-acquisition rates should allow broader querying of the parameter space, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting owing to the sheer number of variables. New strategies for autonomous data acquisition are being developed, with one promising direction being the use of Gaussian process regression (GPR). GPR is a quick, non-parametric and robust approximation and uncertainty quantification method that can be applied directly to autonomous data acquisition. We review GPR-driven autonomous experimentation and illustrate its functionality using real-world examples from large experimental facilities in the USA and France. We introduce the basics of a GPR-driven autonomous loop with a focus on Gaussian processes, and then shift the focus to the infrastructure that needs to be built around GPR to create a closed loop. Finally, the case studies we discuss show that Gaussian-process-based autonomous data acquisition is a widely applicable method that can facilitate the optimal use of instruments and facilities by enabling the efficient acquisition of high-value datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2025

High-throughput and data-driven search for stable optoelectronic AMSe 3 materials

The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Uncertainty-Based Design: Finite Element and Explainable Machine Learning Modeling of Carbon–Carbon Composites for Ultra-High Temperature Solar Receivers

Design under uncertainty has significantly grown in research developments during the past decade. Additionally, machine learning (ML) and explainable ML (XML) have offered various opportunities to provide reliable predictable models. The current article investigates the use of finite element modeling (FEM), ML and XML predictions, and uncertain-based design of carbon-carbon (C-C) composites for use in ultra-high temperatures. A C-C composite concentrating solar power (CSP) as a microvascular receiver is considered as a case study. These C-C composites are fiber composites with directly integrated carbonized microchannels to form a lightweight, high-absorptivity material that includes an embedded microvascular network of channels. The topology of these microchannels is engineered to optimize heat transfer to a supercritical carbon dioxide (sCO2) heat transfer fluid. The mechanical characterization of C-C composites is highly challenging. Thus, designing every component made of C-C composites for ultra-high temperature applications needs an uncertainty-based analysis. As a part of a comprehensive project on the development of a novel carbonized microvascular C-C composite, this paper explores C-C composite sensitivity analysis, FEM, ML prediction, and XML analysis. The resulting composite can then be carbonized and coated with an oxidation-resistant coating to form a thermally efficient and mechanically robust C-C composite. An ANSYS 3-D-FE model was used to analyze the CSP’s stress/strain. To consider the variability in the mechanical and thermal properties of C-C composites, various mechanical properties are considered as the ANSYS FEM’s input. A synthetic dataset from 730 ANSYS runs was produced to feed into the ML and XML algorithms for uncertainty analysis and prediction. The ML and XML algorithms could accurately predict the CSP stresses/strains.

Daghigh, Vahid (ORCID:0000000298941620)↗

DOE OSTI2021

Towards Reconstruction of Complex Flow Fields Using Unit Flows

Many complex turbulent flows in nature and engineering can be qualitatively regarded as being constituted of multiple simpler unit flows. The objective of this work is to characterize the coherent structures in such complex flows as a combination of constituent unitary flow structures for the purpose of reduced-order representation. While turbulence is clearly a non-linear phenomenon, we aim to establish the degree to which the optimally weighted superposition of unitary flow structures can represent the complex flow structures. The rationale for investigating such superposition stems from the fact that the large-scale coherent structures are generated by underlying flow instabilities that may be reasonably described using linear analysis. Clearly, the degree of validity of superposition will depend on the flow under consideration. In this work, we take the first step toward establishing a procedure for investigating superposition. Experimental data of single and triple tandem jets in crossflow are used to demonstrate the procedure. A composite triple tandem jet flow field is generated from optimal superposition of single jet data and compared against ‘true’ triple jet data. Direct comparisons between the true and composite fields are made for spatial, temporal, and kinetic energy content. The large-scale features (obtained from proper orthogonal decomposition or POD) of true and composite tandem jet wakes exhibit nearly 70% agreement in terms of modal eigenvector correlation. Corresponding eigenvalues reveal that the kinetic energy of the flow is also emulated with only a slight overprediction. Temporal frequency features are also examined in an effort to completely characterize POD modes. The proposed method serves as a foundation for more rigorous and robust dimensional reduction in complex flows based on unit flow modes.

Kristo, Paul J.↗

DOE OSTI2021

Cosmic Inference: Constraining Parameters with Observations and a Highly Limited Number of Simulations

We look at cosmological probes that pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters that characterize the underlying physical system-our universe, from these observations and theoretical forward-modeling. The only way to accurately forward-model physical behavior on small scales is via expensive numerical simulations, which are further “emulated” due to their high cost. Emulators are commonly built with a set of simulations covering the parameter space with Latin hypercube sampling and an interpolation procedure; the aim is to establish an approximately constant prediction error across the hypercube. In this paper, we provide a description of a novel statistical framework for obtaining accurate parameter constraints. The proposed framework uses multi-output Gaussian process emulators that are adaptively constructed using Bayesian optimization methods with the goal of maintaining a low emulation error in the region of the hypercube preferred by the observational data. In this paper, we compare several approaches for constructing multi-output emulators that enable us to take possible inter-output correlations into account while maintaining the efficiency needed for inference. Using a Lyα forest flux power spectrum, we demonstrate that our adaptive approach requires considerably fewer-by a factor of a few in the Lyα P(k) case considered here-simulations compared to the emulation based on Latin hypercube sampling, and that the method is more robust in reconstructing parameters and their Bayesian credible intervals.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Integrated Multiscale Model for Design of Robust 3D Solid-state Lithium Batteries

In FY23, we successfully established the multiscale modeling framework for probing the effects of materials microstructure on cell performance of 3D solid-state batteries. The framework covers physicochemical processes co-evolving at the atomistic and microstructure scales. Our simulations revealed the mechanism of initial interfacial degradation, formation of secondary phases, and the structure-property relationship for ion transport and mechanical stability at the interface. In addition, we also established the microstructure-performance relationships by performing sensitivity tests of various microstructure features and extracting their impact on cell performance during charge-discharge cycles. We have successfully applied our multiscale, multiphysics modeling capability to common electrode and electrolyte materials that are of interests to VTO and the experimental teams within the US-Germany collaboration. The insights we obtained from these simulations provide valuable design principles to optimize materials properties for advanced 3D solid-state batteries.

25 ENERGY STORAGE↗

DOE OSTI2021

Electrical Energy Storage Data Submission Guidelines, Version 2

Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.

25 ENERGY STORAGE↗

DOE OSTI2025

Hybrid biological-chemical strategy for converting polyethylene into a recyclable plastic monomer using engineered Corynebacterium glutamicum

Converting polyethylene (PE) into valuable materials, particularly ones that are better for the environment than the incumbent plastics, not only helps mitigate environmental issues caused by plastic waste but also alleviates the long-standing problem of microbial fermentation competing with food supplies. However, the inherent robustness of PE due to its strong carbon-carbon bonds and high molecular weight necessitates harsh decomposition conditions, resulting in diverse decomposition outcomes that present significant challenges for downstream applications, especially for bioconversion. In this study, we demonstrate a hybrid biological-chemical conversion process for PE, converting its decomposition products, namely short-chain diacids, into a monomer, β-keto-δ-lactone (BKDL), for highly recyclable polydiketoenimine plastics using engineered Corynebacterium glutamicum. Since BKDL synthesis requires a substantial supply of malonyl-CoA, we employed an alternative biosynthesis pathway that leverages C. glutamicum's natural proficiency in amino acid production. We optimized this pathway in vivo by minimizing carbon loss to CO2 and byproducts, improving the transporter system, and maximizing co-factor regeneration. Furthermore, we co-optimized the PE deconstruction process to produce predominantly C4 to C6 diacids and integrated three catabolic pathways into the engineered strain to enhance diacid utilization, maximizing the carbon conversion from PE. Finally, an engineered polyketide synthase was introduced into C. glutamicum to enable BKDL synthesis. This work demonstrates the potential of a chemo-biological hybrid strategy for recycling plastic waste, highlighting its promise in addressing environmental challenges and promoting sustainable materials.

Zhan, Chunjun↗