SEARCH · Search NASA

Results for “advanced reactors”

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.

391 records · Page 17

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Li 21 Ge 8 P 3 S 34 : New Lithium Superionic Conductor with Unprecedented Structural Type

Abstract Lithium superionic conductors are pivotal for enabling all‐solid‐state batteries, which aim to replace liquid electrolytes and enhance safety. Herein, we report the discovery of an unprecedented lithium superionic conductor, Li 21 Ge 8 P 3 S 34 , featuring a novel structural type and a new composition in the Li–Ge–P–S system. This material exhibits high lithium ionic conductivity of approximately 1.0 mS cm −1 at 303 K with a low activation energy of 0.20(1) eV. It's unique crystal structure was elucidated using three‐dimensional electron diffraction (3D ED) and further refined through combined powder X‐ray and neutron diffraction analyses. The structure consists of alternating two‐dimensional slabs: one of corner‐sharing GeS 4 tetrahedra and the other of isolated PS 4 tetrahedra, enabling efficient lithium‐ion transport through a tetrahedrally interconnected network of 1D, 2D, and 3D diffusion pathways. This distinctive structural motif provides a novel design strategy for next‐generation solid electrolytes, broadening the structural landscape of lithium superionic conductors. With further advancements in compositional tuning and interfacial engineering, Li 21 Ge 8 P 3 S 34 could contribute to the development of high‐performance all‐solid‐state batteries.

Chemistry↗

DOE OSTI2024

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

Environmental Sciences & Ecology↗

DOE OSTI2025

Strategies for Fabricating Molybdenum Structures Using Laser Powder Bed Fusion

Advances in manufacturing techniques are viewed as enabling technologies for development of high performance nuclear fuel forms that couple high uranium density with improvements to key properties such as thermal conductivity unattainable through conventional fabrication routes. Additive manufacturing (AM) enables the fabrication of complex fuel geometries that are difficult or impossible to achieve using conventional manufacturing methods. Melting-based AM processes, such as laser powder bed fusion (LPBF), provide high geometric resolution (>200 µm depending on the feature) across a variety of metal alloys, including those suitable for high-temperature fuel cladding applications, such as Nb, W, and Mo. Molybdenum is particularly attractive due to its high thermal conductivity, low thermal expansion, and excellent mechanical stability at elevated temperatures. However, its high melting temperature and brittle nature at low temperatures pose significant challenges during LPBF processing. Rapid solidification inherent to LPBF induces high residual stresses, often leading to post-solidification cracking, which limits the manufacturability of Mo components via this method.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Mode Multiplexing for Scalable Cavity-Enhanced Operations in Neutral-Atom Arrays

Neutral-atom arrays provide a versatile platform for quantum information processing. However, in large-scale arrays, efficient photon collection remains a bottleneck for key tasks such as fast, nondestructive qubit readout and remote entanglement distribution. We propose a cavity-based approach that enables fast, parallel operations over many atoms using multiple modes of a single optical cavity. By selectively shifting the relevant atomic transitions, each atom can be coupled to a distinct cavity mode, allowing independent simultaneous processing. We present practical system designs that support cavity-mode multiplexing with up to 50 modes, enabling rapid mid-circuit syndrome extraction and significantly enhancing entanglement distribution rates between remote atom arrays. This approach offers a scalable solution to core challenges in neutral-atom arrays, advancing the development of practical quantum technologies.

Aqua, Ziv [Massachusetts Institute of Technology (↗

DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗

DOE OSTI2026

Unlocking superplasticity in medium and high-entropy alloys

Superplasticity, the capacity of materials to sustain extraordinary tensile elongations at elevated temperatures, underpins a range of advanced metal-forming technologies. Conventionally, it is achieved in fine-grained alloys where deformation is dominated by grain-boundary sliding, accommodated by diffusion and dislocation activity. The advent of medium- and high-entropy alloys (M/HEAs), with their high chemical complexity and unconventional phase stability, offers new pathways to superplastic behavior beyond traditional alloy systems. Although investigated only recently, several M/HEAs already exhibit elongations that rival or exceed those of classical superplastic materials, particularly when ultrafine or metastable microstructures are engineered. Here, we review progress in understanding superplastic deformation in M/HEAs, emphasizing the interplay among composition, initial microstructure, thermomechanical processing, and microstructural evolution during high-temperature deformation. We discuss approaches to generating the fine-grained structures necessary for grain-boundary sliding, including severe plastic deformation and tailored heat treatments. We further highlight dynamic phenomena such as phase transformations, evolving grain-boundary chemistry, and deformation-induced grain refinement that can enhance plasticity in these systems. These mechanisms often shift the balance of deformation processes, enabling large elongations even outside classical criteria. Finally, we outline key challenges for application, including cost, scalability, recyclability, and microstructural stability.

klenam, Desmond [University of the Witwatersrand, ↗

DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗

DOE OSTI2025

AL-CRADA-2025-01 Final Report: American-Made Wind Turbine Materials Recycling Prize Accelerate! Phase 2

This report summarizes the Phase 2 achievements of the Wind Turbine Materials Recycling Prize project led by Critical Materials Recycling (CMR) in partnership with Ames National Laboratory. The team demonstrated the technical feasibility and economic viability of recovering and reusing rare-earth Nd-Fe-B magnets from end-of-life (EOL) wind turbine generators. Through mechanical disassembly, demagnetization, and precision processing, magnets were harvested and characterized to assess structural, compositional, and magnetic properties. Results showed that recovered magnets performed on par with commercial-grade counterparts, enabling their integration into actuator and motor prototypes without redesign. System-level modeling and finite-element simulations validated operational performance, while Life Cycle Cost (LCC) analysis revealed up to 20% material cost savings and 2–3% reductions in overall motor system costs. Life Cycle Assessment (LCA) further confirmed substantial environmental benefits, including a ~94% reduction in global warming potential relative to virgin magnet production. The project’s success was enabled by a tightly integrated partnership between industry and national lab experts, illustrating a viable path for domestic magnet-to-magnet recycling. These findings support a scalable, circular solution for rare-earth material recovery, reducing dependence on imported critical minerals and advancing sustainable clean energy technologies.

Lograsso, Thomas [Ames Laboratory (AMES), Ames, IA↗

DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Final Technical Report for DE-SC0022206

This project developed foundational genetic, genomic, and epigenetic tools for anaerobic fungi (Neocallimastigomycota), a group of microorganisms with exceptional natural abilities to deconstruct lignocellulosic biomass. Efficient biomass deconstruction remains a major barrier to economical production of renewable fuels, chemicals, and materials from agricultural and forestry residues. The project sought to enable mechanistic studies and future engineering of anaerobic fungi by improving genomic resources, establishing methods for gene expression, and investigating epigenetic regulation of biomass-degrading pathways. Major accomplishments included generation of the first chromosome-scale genome assemblies for multiple anaerobic fungal species, providing publicly available genomic resources that support both engineering and fundamental biological research. The project established the first reproducible system for heterologous gene expression in anaerobic fungi and identified genomic features and mobile genetic elements that may support future development of stable transformation technologies. In parallel, the project demonstrated direct conversion of untreated lignocellulosic biomass into fuels and specialty chemicals through a fungal-yeast bioprocess and identified anaerobic fungal enzymes with utility for metabolic engineering. The research also revealed that epigenetic regulation plays an important role in controlling fungal gene expression and enzyme production, identifying potential strategies for enhancing biomass degradation. Collectively, this work established anaerobic fungi as a tractable emerging platform for bioenergy and biomanufacturing research, generated valuable public resources, trained the next generation of researchers, and advanced DOE-BER goals related to predictive biology, sustainable bioprocessing, and the circular bioeconomy.

Solomon, Kevin [University of Delaware] (ORCID:000↗

DOE OSTI2026

TRACE Input Modernization

This work presents a Tom’s Obvious Minimal Language (TOML)-based representation of input for the US Nuclear Regulatory Commission’s TRAC/RELAP Advanced Computational Engine (TRACE) thermal hydraulics code. Implemented using the Workbench Analysis Sequence Processor (WASP), the approach maps traditional TRACE input structures to a hierarchical format composed of named parameters, typed values, and native data collections. The resulting representation preserves TRACE’s existing modeling capabilities while providing a modern, structured interface for model development and management. WASP further extends TOML through a file import directive that supports modular model composition and reusable input organization. In addition, WASP provides extended array data entry convenience with various data repeat and interpolation capabilities. Examples of the new TOML syntax are provided for major TRACE input categories, including hydraulic components, heat structures, control systems, and trip logic. The TOML representation establishes a foundation for improved validation, tooling, automation, and model maintainability while remaining compatible with existing TRACE workflows. To facilitate migration to the TOML-based input format, the TRACE executable now supports conversion of native TRACE input into an intermediate JSON representation. A Python utility subsequently transforms the JSON data into an equivalent TOML model. Lastly, the TRACE executable now supports execution using TOML-formatted input.

Lefebvre, Robert A. [Oak Ridge National Laboratory↗

DOE OSTI2026

Reduced Order Models for Liquid Hydrogen Pooling and Vaporization Supported by Experiments

In the event of a leak of liquid hydrogen, a pool can form that vaporizes, disperses, and eventually dilutes to a non-flammable mixture. In this work, we describe fast-running models for the pooling and vaporization of liquid hydrogen in a steady cross-wind. Several pooling models from the literature are compared to solve for the flow and extent of the pool. The size of the pool can serve as the source for a separate dispersion model, which builds upon the existing one-dimensional Gaussian plume model in HyRAM+. Additional terms for the effects of a cross-wind on momentum and entrainment were added so that the model could handle the effects of a cross-wind on a low-speed flow. The models are compared to experimental data on pooling extent and downwind dispersion for steady flow rates of liquid hydrogen in a steady cross-wind. In the two compared experiments, liquid flow rates of 15 and 45 g/s were spilled onto concrete in cross-winds of approximately 1.8 m/s. The rate of growth of the pool and the downwind concentration boundaries are compared to the models, showing good agreement, although additional tuning is needed. These models can contribute to the advancement of codes and standards for liquid hydrogen systems.

dispersion↗

DOE OSTI2026

Metrology for femtosecond pulsed x-ray heating in diamond anvil cell experiments at the European XFEL: Revisiting the iron phase diagram up to 150 GPa

The development of pulsed intense x-ray sources, such as free electron laser, offers new avenues for high pressure experiments. Here, we study the feasibility and metrology of x-ray heating in diamond anvil cells at the European x-ray free electron laser. This method enables one to volumetrically heat the sample while inhibiting chemical migration and probing the crystallographic structure of the sample throughout the heating with a high repetition rate. We focus our study on iron, whose phase diagram is well established up to 100 GPa, to explore the possibilities and limitations of this technique. We volumetrically heat iron samples at starting pressures ranging from 10 to 138 GPa, using the x-ray beam pulsed at 4.5 MHz in a serial pump-and-probe experimental design. Experimental challenges arise from temperature gradients within the sample, changes in temperature at the 100 ns timescale, the difficulty of direct temperature estimates, the effect of thermal pressure, and the presence of metastable crystallites due to rapid cycles of heating and cooling. Hence, we develop a multi-crystal-like data processing method that allows us to account for sample heterogeneity in probed conditions. We then calibrate our measurements using known physical properties of iron under pressure. Thermal pressure in our experiments increases from 4% of the isochoric prediction at 10 GPa to 23% at 138 GPa, and we show that our data are in agreement with most previous observations of iron in this pressure range. The method can now be implemented at higher pressures and temperatures and on materials with unknown phase diagrams.

Materials science↗

DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗

DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗

DOE OSTI2026

Nanoscale Observation and Control of Quasiparticle Induced Magnetic Noise in a Superconducting Resonator

Superconducting circuits are arguably taking a leading role in driving the ongoing quantum technological revolution. A detailed knowledge of the microscopic fluctuating electromagnetic properties plays an important role in advancing the circuitry design, testing, and material integration of cutting-edge superconducting quantum electronics. Here, in this work, we report scanning nitrogen-vacancy (NV) quantum sensing of local magnetic noise environment of an on-chip superconducting resonator. We find that quasiparticle-induced fluctuating magnetic fields can drive NV spin relaxation, which shows a peak value around the superconducting transition point of niobium at the thermal equilibrium state. External microwave driving at the resonator mode frequency significantly increases the quasiparticle density, leading to enhancement of magnetic noise. We further perform optically detected magnetic resonance measurements to demonstrate quasiparticle magnetic noise mediated off-resonant dipole coupling between the NV center and niobium resonator. Our Letter reports experimental observation of the Hebel-Slichter peak signature by an external sensor outside of a superconductor. The presented study also highlights the advantages of quantum sensors in investigating miniaturized superconducting devices, providing insights into their future performance improvements.

Li, Senlei [Georgia Institute of Technology]↗

DOE OSTI2026

Solid-State Syntheses, Crystallographic Spatial Disorders, Thermal Behaviors, and Bandgaps of Hybrid Organic-Inorganic Manganese Halides: A2Mn(Cl/Br)4 (A = NH4+, C(NH2)3+, & C3H4N2+)

By systematically optimizing solid-state synthesis via the concerted use of differential scanning calorimetry and in-situ variable-temperature X-ray diffraction, we report the discovery of four new hybrid organic-inorganic manganese halides A2Mn(Cl/Br)4 (A = NH4+, C(NH2)3+, C3H4N2+), with emphasis on how the organic fragment geometry, polarity, and electron-donating properties influence crystallographic spatial disorders, thermal behaviors, and optical band gaps. With a nonpolar tetrahedral cation, as in (NH4)2MnCl4 (P21/c, mP30) or (NH4)2MnBr4 (C2/c, mC180), the direct optical bandgaps (4.36–4.28 eV) are wider than those with an organic nonpolar planar geometry, as in (C(NH2)3)2MnBr4 (P21/c, mP300, 4.07 eV), or an organic polar planar geometry, as in (C3H4N2+)2MnBr4 (I41/a, tI384, 4.05 eV). Furthermore, the organic components affect the spatial arrangement and dimensionality of the resulting inorganic frameworks comprised of Mn(Cl/Br)6 octahedra, with varying distortions, or spatially disordered MnBr4 tetrahedra. These resulting trends, coupled with the systematic investigation into synthesis, and the motivation behind elucidating the nuanced role of the organic cation, altogether expand upon the phase-space of hybrid materials beyond perovskites and demonstrate the potential for a rational design of hybrid materials with tailored optoelectronic functionalities for advanced energy applications.

Lee, Shannon J.↗

DOE OSTI2026

Enzymatic Nylon Deconstruction: Enzyme Discovery, Engineering, and Opportunities

Nylons are widely used synthetic polyamides valued for their strength, versatility, and durability across diverse applications. However, their petrochemical origin and energy-intensive production underscore the need for efficient, circular solutions. Conventional recycling methods remain limited by incomplete recovery, material degradation, and costly sorting requirements. Enzymatic depolymerization offers a selective, low-energy alternative capable of processing mixed waste streams under mild conditions. While significant progress has been achieved for polyesters, enzymatic degradation of polyamides is still at an early stage. The discovery of nylon hydrolases demonstrated the potential of biological systems to evolve catalysts for synthetic polyamides, yet reported depolymerization yields remain low. These limitations reflect both the structural complexity of nylons and the need for improved enzyme discovery and engineering. In conclusion, this review highlights recent advances, key challenges, and future directions for enzymatic nylon recycling, outlining its potential role enabling mixed polymer waste to be used as a green feedstock for remanufacturing.

Amides↗