Search NASA⌕ Search

SEARCH · Search NASA

Results for “Feature engineering”

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.

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

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
DOE OSTI2025

How Interface Evolution at the Nanoscale Dictates Protonic Ceramic Cell Behavior

Understanding the effects of heterointerfaces in nanodevices remains a challenge in interface engineering for systems applied in solid oxide fuel cells, catalysis, sensors, and memristors [1]. Previous studies have shown that the presence of interfaces in ceramic oxide nanodevices affects their impedance properties compared to bulk systems [2]. In this work, we analyze a thin-film model system designed to study the interfaces between representative electrode and electrolyte materials employed in protonic ceramic electrochemical cells for the reversible conversion of hydrogen o electricity. The investigated device features a 21-layer architecture that alternates between an electrode material PBSCF (PrBa 0.5 Sr 0.5 Co 1.5 Fe 0.5 O 5+ϵ ) and a proton-conducting electrolyte BZY (BaZr 0.8 Y 0.2 O 3-δ ), with uniform 20 nm thickness for each layer. Here, the device was fabricated using Pulsed Laser Deposition (PLD) and supported on a conductive Nb-doped STO (Nb-SrTiO 3 ) substrate.

dos Santos, Gabriel T. [Northwestern University, E↗
DOE OSTI2026

Towards libraries of different ultrasmall amorphous silica cage topologies

Cage topology controlled at the nanometer length scale is expected to enable original functionalities, e.g., in catalysis and therapeutics. Cages are fundamental structural motifs of clathrates and their topological duals, Frank-Kasper phases, and are constituents of mesoporous silica frameworks. However, with one exception, they have not been synthesized as discrete particles. By varying reactant ratios of surfactant, oil, and silane, we report the discovery of 5(12)6(2) and 5(12)6(8) amorphous silica polyhedra, each coexisting with the previously identified 5(12) cage, using combined cryo-transmission electron microscopy (cryo-TEM) and single-particle reconstruction (SPR). Notably, the 5(12)6(8) polyhedron represents a topology not previously observed in mesoporous silica frameworks. Control over structural features is demonstrated, and insights into cage formation mechanisms are provided. Structural outcomes are summarized in a ternary morphology diagram alongside prior results, bridging serendipitous discovery and intentional design of silica cages displaying a level of control over silica polymerization rivaling nature.

Jang, Dong June [Department of Materials Science a↗
DOE OSTI2024

Approaches for the Simulation of Coupled Processes in Evolving Fractured Porous Media Enabled by Exascale Computing

Models have historically represented fractured porous media with continuum descriptions that characterize the media using bulk parameters. The impact of small-scale features is not captured in these models, although they may be controlling the performance of subsurface applications. Pore-scale models can simulate processes in small-scale features by representing the pore space geometry explicitly but are computationally expensive for large domains. The alternative multiscale approach entails the combination of pore-scale and continuum-scale descriptions in a single framework. We use Chombo-Crunch, a computational capability that discretizes complex geometries with an adaptive, embedded boundary method to contrast these two approaches. Chombo-Crunch takes advantage of recent computational performance and memory bandwidth improvements resulting from the emergence of exascale computing resources. These combined improvements enable the efficient simulation of reactive transport in fractured media with a high degree of fidelity and the ability to capture the control small-scale processes exert on the overall medium evolution.

42 ENGINEERING↗
DOE OSTI2025

Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb 2 S 3

The ability to control material heat transport properties over space and time can drive advanced functionalities in thermal management for electronics and system-on-chip, and enable thermal circuits. Despite the technological relevance, there are limited demonstrations of local thermal property control. Rotating lattice single (RLS) crystals—formed via laser-induced crystallization of an amorphous substrate—offer a novel avenue for local crystal engineering, unlocking opportunities for microscale property patterning. Here, thermal conductivity (𝜅) imaging is applied to RLS crystals of Sb2S3 to resolve microscale 𝜅 variations across patterned regions. Amorphous areas exhibit 𝜅 as low as 0.6 Wm −1 K −1 , while crystalline regions display periodic 𝜅 variations from 0.7 to over 2.5 Wm −1 K −1 . These variations correspond to changes in crystal orientation, revealing marked 𝜅 anisotropy. The crystal out-of-plane direction (c axis)—featuring van der Waals bonds—shows amorphous-like transport, whereas in-plane directions (a, b axes) exhibit 3.5x and 1.7x larger 𝜅, respectively. First-principles calculations, in excellent agreement with experiments, suggest that the in-plane anisotropy originates from expressed Sb lone pairs, which impart a corrugation along the b axis affecting bond stiffness and 𝜅. These findings demonstrate microscale control of thermal properties via laser-processed metastructures, with significant implications for next-generation thermal management.

14 SOLAR ENERGY↗
DOE OSTI2024

Design and Development of a Fixtureless, Pass-through Machine Tool for Extrusion Machining

The aerospace, construction/architecture, and transportation manufacturing industries rely heavily on the mass production of near-net shape metallic and composite extrusions. While the production of raw extrusions is a relatively fast process, adding functional features such as holes and slots require additional time, cost, and energy to produce. To compensate for the inherent flexibility of extrusions, conventional machining requires rigid purpose-built fixtures for operations such as trimming, drilling and thinning. This approach requires that the machine tools be as large or larger than the parts themselves. This results in the need for excess shop floor space, energy for auxiliary equipment and motion systems, and significant capital expenditure. Considerable engineering expense and time involved in the designing, building, and proving out of part-specific fixtures for holding the components in specific configurations while machining add to the overall manufacturing cost. The primary objective of the technical collaboration between Oak Ridge National Laboratory and Fairmount Technologies (FT) is to improve the XM-3, a fixtureless CNC milling machine designed by FT. The machine was developed to trim, drill, and thin extrusions without part specific fixturing to make the manufacturing process more efficient and flexible. Dynamic measurements of the existing structure were collected, and modeling efforts were made to evaluate optimal machining parameters for the current system. Areas of improvement to increase the system stiffness, manufacturability, and machining efficiency were evaluated and highlighted for the next generation design. The impact of this effort may enable agile manufacturing across the commercial and defense aerospace industries, and other industries where extrusions are utilized like in the construction, architecture, and transportation industries.

42 ENGINEERING↗
DOE OSTI2025

Design and Development of a Fixtureless, Pass-through Machine Tool for Extrusion Machining

The aerospace, construction/architecture, and transportation manufacturing industries rely heavily on the mass production of near-net shape metallic and composite extrusions. While the production of raw extrusions is a relatively fast process, adding functional features such as holes and slots require additional time, cost, and energy to produce. To compensate for the inherent flexibility of extrusions, conventional machining requires rigid purpose-built fixtures for operations such as trimming, drilling and thinning. This approach requires that the machine tools be as large or larger than the parts themselves. This results in the need for excess shop floor space, energy for auxiliary equipment and motion systems, and significant capital expenditure. Considerable engineering expense and time involved in the designing, building, and proving out of part-specific fixtures for holding the components in specific configurations while machining add to the overall manufacturing cost. The primary objective of the technical collaboration between Oak Ridge National Laboratory and Fairmount Technologies (FT) is to improve the XM-3, a fixtureless CNC milling machine designed by FT. The machine was developed to trim, drill, and thin extrusions without part specific fixturing to make the manufacturing process more efficient and flexible. Dynamic measurements of the existing structure were collected, and modeling efforts were made to evaluate optimal machining parameters for the current system. Areas of improvement to increase the system stiffness, manufacturability, and machining efficiency were evaluated and highlighted for the next generation design. The impact of this effort may enable agile manufacturing across the commercial and defense aerospace industries, and other industries where extrusions are utilized like in the construction, architecture, and transportation industries.

42 ENGINEERING↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Online LIBS–ML Framework for Dynamic Characterization of Heterogeneous Waste-Derived Gasification Feedstocks

LIBS−ML framework for real time feedstock characterization during continuous conveyor transport Heterogeneous waste derived feedstocks (e.g., waste coal, biomass and blends) introduce rapid variability in heating value and ash chemistry that affect gasifier operation, yet conventional laboratory characterization techniques are too slow to support proactive control. To address this gap, this study reports on an online, in situ, dynamic characterization framework that couple’s laser-induced breakdown spectroscopy (LIBS) with leakage safe machine learning (ML) regression to deliver real time, decision quality predictions of gasifier relevant properties. A controlled sample matrix spanning two different waste coals, two different biomasses, and engineered blends under two particle size conditions were constructed and benchmarked using standardized laboratory analyses for proximate/ultimate properties and ash composition. LIBS spectra were acquired dynamically as material flowed on a conveyor belt, using high energy 1064 nm laser ablation and shot averaging to improve repeatability and precision. Supervised regression models (multi layer perceptron (MLP) /artificial neural network (ANN), random forest (RF), and support vector regression (SVR)) and an optimized weighted ensemble were trained on emission line feature sets using nested cross validation with Bayesian hyperparameter tuning and validated against an independent hold out set. The proposed LIBS−ML workflow achieves near laboratory predictive fidelity across parametric targets (including higher heating value (HHV), ash content, fixed carbon, sulfur, major ash forming oxides, and initial deformation temperature (IDT)), with the weighted ensemble providing a robust default predictor under dynamic measurement conditions. These results demonstrate a practical pathway for real time feedstock characterization that can enable feedforward adjustments and more resilient gasifier operation for variable quality waste derived fuels.

Biomass↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
DOE OSTI2024

Recycling of Titanium Scrap by Shear Assisted Processing and Extrusion (ShAPE)

Titanium and its alloys are used in the aviation and automobile industries due to their remarkable strength to weight ratio, but, commonly, machining loss is high with ~90 wt.% of the material being converted to scrap. Recycling post-consumer Ti scrap directly into solid bulk products is a potential solution for repurposing valuable material. Further, reducing or even eliminating fresh Ti sponge during recycling might lead to lower energy and greenhouse gas emissions. In this study, a solid-phase process known as friction extrusion was utilized to recycle Ti-6Al-4V machining chips into solid wires which could be used as feedstock in additive manufacturing. The friction consolidation technique was first used to convert chips with varying degrees of oxygen content into solid billets for its use as feedstock material in subsequent friction extrusion. The extrudates were fabricated above the beta transition temperature, which was achieved by selecting rotation rate and feed rate, to process the billets near 1000°C using a tungsten-lanthana extrusion die. This work presents the first occurrence of friction extruded titanium alloy wires. The effect of friction extrusion on microstructural features, tensile properties, and texture are reported. Overall, the friction extrusion method is capable of recycling Ti-6Al-4V scrap directly into extruded wire.

36 MATERIALS SCIENCE↗
DOE OSTI2024

RANGERS: Modeling Report on Integrity and Performance Assessment of Engineered Barrier Systems in a Salt Repository for HLW/SNF

The Engineered Barrier System (EBS) plays an important role in ensuring the long-term safety and containment of high-level waste (HLW) and spent nuclear fuel (SNF) in deep geological repositories in salt formation. As part of a multi-barrier system, the EBS works alongside the natural barrier, which is the salt formation itself and the technical barrier comprising the disposal casks. The primary function of the EBS is to maintain containment during a defined period until the backfill used in the repository made of crushed salt, develops its sealing capacity through compaction. Over the time, the backfill eventually compacts to a state of low porosity and permeability, acting as a long-term seal. However, until this process is complete, the EBS must retain its structural and functional integrity. Regulatory guidelines in Germany currently require the EBS to remain effective for up to next ice age, that is expected in 50,000 years. The significant hydro-geological and topographic changes expected during an ice age could make it impossible to accurately predict the hydro-chemical conditions within the repository system at that time. In response to these challenges, BGE TECHNOLOGY GmbH (BGE TEC) and Sandia National Laboratories (SNL) have jointly developed a comprehensive methodology for the design and safety assessment of engineered barrier systems within the scope of the RANGERS project. This methodology is tailored for repositories in salt formations. The developed methodology provides a structured approach for designing and assessing the performance of the EBS in salt-based repositories. It begins with defining a sealing concept based on the geological characteristics of the selected site and the overall repository design. The entire repository system, comprising the geological site, repository infrastructure, and EBS, is then subjected to a Features, Events, and Processes (FEP) analysis, focusing solely on those FEPs that affect the EBS. The derived FEPs help identify the loads and stresses acting on the EBS, which serve as the foundation for conducting an integrity assessment. This analysis helps predict the EBS’s evolution and performance over the regulatory time frame, feeding into integrated performance assessment simulations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

The Dependence of Gamma-Ray Burst Jet Collimation on Black Hole Spin

Gamma-ray bursts (GRBs) are the most luminous events in the Universe and are excellent laboratories to study extreme physical phenomena in the cosmos. Despite a long trajectory of progress in understanding these highly energetic events, there are still many observed features that are yet to be fully explained. Observations of the jet opening angle of long gamma-ray bursts (LGRBs) suggest that LGRB jets are narrower for those GRBs at higher redshift. This phenomenon has been explained in the context of collimation by the stellar envelope, with denser (lower metallicity) stars at higher redshifts able to collimate the jet more effectively. However, until now, the dependence of the jet opening angle on the properties of the central engine has not been explored. We investigate the effect of black hole spin on the jet collimation angle for a magnetically launched jet, using the general relativistic radiation magnetohydrodynamical code ν bhlight . We present 3D results for a range of spin values. The simulations show that higher-spinning black holes tend to create narrower jets. If indeed LGRB progenitors in the early Universe are able to produce black hole central engines with higher spin, this could account for at least some of the observed jet opening angle-redshift correlation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗
DOE OSTI2026

Measuring X-Ray Emission Line Shapes in Neutral Species for XRISM Calibration

Space X-ray spectrometers such as the Resolve instrument on XRISM require precise calibration in order to interpret the spectra of astrophysical objects. Key components of the calibration are the energy scale and the core line spread function, both of which vary with photon energy. A major issue in the calibration of high-resolution spectrometers is locating good calibrators with well-known and stable intrinsic line shapes. Neutral fluorescence is widely used, but inner-shell transitions in neutral atoms often exhibit complex, poorly documented line shapes that vary with excitation conditions. Here, in this study, we present empirical measurements of K-shell transitions in neutral O and F below 1 keV using an engineering model XRISM calorimeter array, an electron bombardment modulated X-ray source, and an electron beam ion trap (EBIT) to provide a precise energy reference. In addition, we report measurements of the Mo Lα complex with the transition-edge microcalorimeter spectrometer (TEMS), which reveal strong satellite structure and sensitivity of the line shape to the incident exciting spectrum. Together, these results demonstrate the need for empirical line-shape models, highlight the nonstationary nature of neutral fluorescence features, and define a path toward developing transfer standards for XRISM and future precision instruments such as Athena/X-IFU.

Astronomy and AstroPhysics↗
DOE OSTI2026

Bioproduction, bioprotection, and biocontainment in multi-kingdom microbial systems with 3D spatial control

Engineered living materials (ELMs) are a class of hybrid materials that include engineered microbes encapsulated by a polymer matrix. The biotic and abiotic components define the ELMs design space and can be altered to improve performance and function. While current synthetic materials in the field display robust biocompatibility with both native and engineered living systems, we have a limited understanding of how to leverage three-dimensional (3D) form factors to spatially organize and control microbial dynamics within the material. Motivated by this knowledge gap, we employed extrusion-based 3D printing to fabricate multi-kingdom hydrogel constructs for the encapsulation of both single and multi-kingdom microbial systems. Core–shell cubic constructs enabled the spatial organization of a constitutive multi-kingdom system of levodopa (L-DOPA)-producing E. coli and betaxanthins (BXN)-producing S. cerevisiae. This spatial organization in 3D materials can introduce precise control over bioproduction, bioprotection, and biocontainment features that are critical to the efficacy of current ELMs. The relative spatial organization of the organisms, as well as the surface area-to-volume ratio were investigated to determine how these design elements impact microbial behavior (metabolite production, growth, expression, and cell distribution) over time. We demonstrated that F127-bis-urethane methacrylate (F127-BUM) core–shell geometries enable the hierarchical 3D printing of multi-kingdom constructs, offering customizable control over bioproduction, bioprotection, and biocontainment. With the optimization of these core–shell structures for continuous bioproduction, these ELMs could be deployed as compact and sustainable bioreactors in remote environments.

additive manufacturing↗