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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2007

The Calipso Thermal Control Subsystem

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) is a joint NASA-CNES mission to study the Earth s cloud and aerosol layers. The satellite is composed of a primary payload (built by Ball Aerospace) and a spacecraft platform bus (PROTEUS, built by Alcatel Alenia Space). The thermal control subsystem (TCS) for the CALIPSO satellite is a passive design utilizing radiators, multi-layer insulation (MLI) blankets, and both operational and survival surface heaters. The most temperature sensitive component within the satellite is the laser system. During thermal vacuum testing of the integrated satellite, the laser system s operational heaters were found to be inadequate in maintaining the lasers required set point. In response, a solution utilizing the laser system s survival heaters to augment the operational heaters was developed with collaboration between NASA, CNES, Ball Aerospace, and Alcatel-Alenia. The CALIPSO satellite launched from Vandenberg Air Force Base in California on April 26th, 2006. Evaluation of both the platform and payload thermal control systems show they are performing as expected and maintaining the critical elements of the satellite within acceptable limits.

Gasbarre, Joseph F.↗
NASA NTRS2007

The CALIPSO Integrated Thermal Control Subsystem

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) is a joint NASA-CNES mission to study the Earth's cloud and aerosol layers. The satellite is composed of a primary payload (built by Ball Aerospace) and a spacecraft platform bus (PROTEUS, built by Alcatel Alenia Space). The thermal control subsystem (TCS) for the CALIPSO satellite is a passive design utilizing radiators, multi-layer insulation (MLI) blankets, and both operational and survival surface heaters. The most temperature sensitive component within the satellite is the laser system. During thermal vacuum testing of the integrated satellite, the laser system's operational heaters were found to be inadequate in maintaining the lasers required set point. In response, a solution utilizing the laser system's survival heaters to augment the operational heaters was developed with collaboration between NASA, CNES, Ball Aerospace, and Alcatel-Alenia. The CALIPSO satellite launched from Vandenberg Air Force Base in California on April 26th, 2006. Evaluation of both the platform and payload thermal control systems show they are performing as expected and maintaining the critical elements of the satellite within acceptable limits.

Gasbarre, Joseph F.↗
NASA NTRS2012

Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction

This paper describes a scalable structural model suitable for Hybrid Wing Body (HWB) centerbody analysis and optimization. The geometry of the centerbody and primary wing structure is based on a Vehicle Sketch Pad (VSP) surface model of the aircraft and a FLOPS compatible parameterization of the centerbody. Structural analysis, optimization, and weight calculation are based on a Nastran finite element model of the primary HWB structural components, featuring centerbody, mid section, and outboard wing. Different centerbody designs like single bay or multi-bay options are analyzed and weight calculations are compared to current FLOPS results. For proper structural sizing and weight estimation, internal pressure and maneuver flight loads are applied. Results are presented for aerodynamic loads, deformations, and centerbody weight.

Gern, Frank H.↗
NASA NTRS2019

Ongoing Breakthroughs in Convective Parameterization

While the increase of computer power mobilizes a part of the community towards models with explicit convection or based on machine learning, we review the part of the literature dedicated to convective parameterization development for large-scale forecast and climate models. Recent findings: Many developments are underway to overcome endemic limitations of traditional convective parameterizations, either in unified or multi-object frameworks: scale-aware and stochastic approaches, new prognostic equations or representations of new components such as cold pools. Understanding their impact on the emergent properties of a model remains challenging, due to subsequent tuning of parameters and the limited understanding given by traditional metrics. Summary: Further effort still needs to be dedicated to the representation of the life cycle of convective systems, in particular their mesoscale organization and associated cloud cover. The development of more process-oriented metrics based on new observations is also needed to help quantify model improvement and better understand the mechanisms of climate change.

parameterizations for large-scale models↗
NASA NTRS2022

Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents(GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

Matthew Blandin↗
NASA NTRS2023

The SunPy Project: An Interoperable Ecosystem for Solar Data Analysis

The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform between commonly used coordinate systems. Affiliated packages perform more specialized tasks that do not fall within the more general scope of the sunpy core package. In this article, we give a high-level overview of the SunPy Project, how it is broader than the sunpy core package, and how the project curates and fosters the affiliated package system. We demonstrate how components of the SunPy ecosystem, including sunpy and several affiliated packages, work together to enable multi-instrument data analysis workflows. We also describe members of the SunPy Project and how the project interacts with the wider solar physics and scientific Python communities. Finally, we discuss the future direction and priorities of the SunPy Project.

Solar physics↗
DOE OSTI2016

Enhanced Performance Assessment Models for Generic Deep Geologic Repositories for High-Level Waste and Spent Nuclear Fuel - 16223

Development of an enhanced performance assessment (PA) capability for geologic disposal of spent nuclear fuel and high-level waste has been ongoing for several years in the U.S. repository program. The new Generic Disposal System Analysis (GDSA) modeling and software framework is intended to be flexible enough to evolve through the various phases of repository activities, beginning with generic PA activities in the current Concept Evaluation phase to site-specific PA modeling in the Repository Development phase. The GDSA Framework utilizes modern software and hardware capabilities by being based on open-source software architecture and being configured to run in a massively parallel, high-performance computing (HPC) environment. It consists of two main components, the open-source Dakota uncertainty sampling and analysis software and the PFLOTRAN reactive multi-phase flow and transport simulator. Reference cases or 'generic repositories' have been, and are being developed, based on typical properties for potential salt, clay, and granite host-rock formations and corresponding engineered design concepts for each medium. Past simulations have focused on a generic repository in bedded-salt host rock, while the most recent research has focused on a reference case for a typical clay/shale host rock. A variety of single-realization (i.e., deterministic) and multi-realization (probabilistic) results for the new clay reference case are presented, including an analysis of the effects of heat generation on repository performance, assuming a 100-year out-of-reactor commercial SNF waste form. Order-of-magnitude differences between predicted radionuclide concentrations in thermal versus isothermal simulations imply that mechanistic, coupled-process modeling in three-dimensional (3-D) domains can be important for building confidence in post-closure performance assessments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

White light generation via sequential stepwise absorption and energy-transfer frequency upconversion in Tm{sup 3+}/Er{sup 3+}-codoped glass

White light emission with CIE-1931 coordinates close to the standard equal-energy white light illuminant, and color temperature in the 4000–6500 K range was produced in Tm{sup 3+}/Er{sup 3+}-codoped glasses through frequency upconversion. Multi-Stokes Raman emissions generated in an optical fiber pumped at 1.064 µm were used, seeking out the {sup 3}H{sub 5} thulium absorption band around 1.2 µm as the departure point. The new and efficient excitation approach allowed the generation of near infrared (800 nm), red (650 nm), and blue (480 nm) emission due to frequency upconversion excited via ground-state absorption and excited-state absorption in Tm{sup 3+} ions. Energy-transfer followed by excited-state absorption in Er{sup 3+} ions, provided the generation of the green (530, 550 nm) and red (660 nm) components to compose white light. The excitation mechanism for the Tm{sup 3+} ions is assigned to stepwise multi-wavelength excitation using Stokes Raman emission in an optical fiber, followed by stepwise excited-state absorption of the pump photons. Energy-transfer Tm{sup 3+}[{sup 3}F{sub 4}] + Er{sup 3+}[{sup 4}I{sub 15/2}] →Tm{sup 3+}[{sup 3}H{sub 6}] + Er{sup 3+}[{sup 4}I{sub 9/2}] followed by excited-state absorption of multi-Stokes Raman emissions populate erbium visible emitting level. Excitation power color tuneability of the overall light emission was also achieved. - Graphical abstract: Recorded emission spectrua of visible radiation emanating from the single-doped (Er3+, Tm3+) and codoped (Er3+/Tm3+) samples excited 160 mW average power from the fiber output. Simplified energy-level diagram of the excitation and upconversion emission processes for the Tm3+/Er3+-codoped sample excited using multi-Stokes emissions. Display Omitted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists.

Brugel, Edward W.↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multispectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists. Twenty-one examples of the use of visualization for astrophysical data are included with this report. Sixteen publications related to efforts performed during or initiated through work on this project are listed at the end of this report.

Brugel, E. W.↗
DOE OSTI2020

Multi-cation perovskites prevent carrier reflection from grain surfaces

The composition of perovskite has been optimized combinatorially such that it often contains six components (A x B y C 1-x-y PbX z Y 3-z ) in state-of-art perovskite solar cells. Questions remain regarding the precise role of each component, and the lack of a mechanistic explanation limits the practical exploration of the large and growing chemical space. Here, aided by transient photoluminescence microscopy, we find that, in perovskite single crystals, carrier diffusivity is in fact independent of composition. In polycrystalline thin films, the different compositions play a crucial role in carrier diffusion. In this work, we report that methylammonium (MA)-based films show a high carrier diffusivity of 0.047 cm 2 s -1 , while MA-free mixed caesium-formamidinium (CsFA) films exhibit an order of magnitude lower diffusivity. Elemental composition studies show that CsFA grains display a graded composition. This curtails electron diffusion in these films, as seen in both vertical carrier transport and surface potential studies. Incorporation of MA leads to a uniform grain core-to-edge composition, giving rise to a diffusivity of 0.034 cm 2 s -1 in CsMAFA films. A model that invokes competing crystallization processes allows us to account for this finding, and suggests further strategies to achieve homogeneous crystallization for the benefit of perovskite optoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Report on Resolution of Regulatory Issues for VHTR Materials: Notch Effects

A better understanding of the high temperature behavior of Inconel 617 is needed prior to use in upcoming high temperature nuclear power plants. Previous work has extensively characterized uniaxial creep behavior. The current work continues this effort and examines the effects of discontinuities and multi-axial stress states on creep of 617 at high temperatures (700-900 °C). In addition to macroscopic analysis of creep specimens, local deformation and damage is examined through use of EBSD analysis and quantitative metallography. The results are critical to linking complex geometries and stress states of actual components to the extensive uniaxial creep database.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High Temperature Thermal Barrier Coating Evaluation of Yttrium Aluminum Garnet for Gas Turbine Applications

The Solution Precursor Spray Process (SPPS) process has been shown to overcome the durability and property challenges of applying yttrium aluminum garnet (YAG) coating for high temperature thermal barrier coating (TBC) applications (1200°C+) but only limited testing has been performed in representative application environments. In this study, YAG coatings were developed to optimize microstructure and tailor properties for combustion components in an industrial gas turbine. Thermal conductivity, erosion resistance and furnace cycling durability were used to validate YAG properties against the standard yttria-stabilized zirconia coating (YSZ). Graded and multi-layered microstructures were applied to enhance performance while maintaining durability. Further YAG process development was required to replicate coating properties and optimize deposition on more complex fuel injector nozzle and combustion liner components. Rig testing of both components was performed and compared against the baseline YSZ coating validating the high temperature capability and lower thermal conductivity of the SPPS YAG coating. The components in both tests were cycled for a minimum of ten cycles with the fuel injector test targeting a higher operating temperature while the combustor liner operated at standard conditions. A development engine test is planned to further validate performance in a gas turbine engine of this promising material.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Atacama Cosmology Telescope: Component-separated maps of CMB temperature and the thermal Sunyaev-Zel'dovich effect

Optimal analyses of many signals in the cosmic microwave background (CMB) require map-level extraction of individual components in the microwave sky, rather than measurements at the power spectrum level alone. To date, nearly all map-level component separation in CMB analyses has been performed exclusively using satellite data. In this paper, we implement a component separation method based on the internal linear combination (ILC) approach which we have designed to optimally account for the anisotropic noise (in the 2D Fourier domain) often found in ground-based CMB experiments. Using this method, we combine multi-frequency data from the Planck satellite and the Atacama Cosmology Telescope Polarimeter (ACTPol) to construct the first wide area (≈2100 sq. deg.), arcminute-resolution component-separated maps of the CMB temperature anisotropy and the thermal Sunyaev-Zel'dovich (tSZ) effect sourced by the inverse-Compton scattering of CMB photons off hot, ionized gas. Our ILC pipeline allows for explicit deprojection of various contaminating signals, including a modified blackbody approximation of the cosmic infrared background (CIB) spectral energy distribution. The cleaned CMB maps will be a useful resource for CMB lensing reconstruction, kinematic SZ cross-correlations, and primordial non-Gaussianity studies. The tSZ maps will be used to study the pressure pro les of galaxies, groups, and clusters through cross-correlations with halo catalogs, with dust contamination controlled via CIB deprojection. The data products described in this paper are available on LAMBDA.

Mathew S. Madhavacheril↗
DOE OSTI2024

Hydrogen and its Vital Role in a Clean Energy Future

Large-scale, low -cost hydrogen production can enable an economically competitive, secure, and environmentally beneficial future energy system across multiple sectors. Furthermore, clean hydrogen can address specific sectors that are hard to decarbonize (e.g., heavy-duty trucking, load-following electricity, iron, steel, and cement) and can help the U.S. meet the net zero carbon goal by 2050. To achieve this goal, tens of millions of metric tons of clean, reliable, and affordable hydrogen will be needed annually1. In 2021, the Hydrogen Energy Earthshot was launched, and its goal is to reduce the cost of clean hydrogen to $1 per $1 kilogram in 1 decade (1 1 1) 2. One very promising pathway for large-scale hydrogen production is water splitting. Water splitting technologies range from commercial technologies such as electrolyzers to approaches that are at a much earlier stage of development, such as photoelectrochemical (PEC) and thermochemical (TCH) processes. All these water splitting pathways offer diverse benefits in energy storage, grid services, and cross-sector emissions reductions while taking advantage of the diverse domestic resources. However, critical materials-, component- and system-level challenges must be addressed to improve efficiency and durability and reduce cost. To address these barriers and move these promising and high impact technologies forward, the HydroGEN Advanced Water Splitting Materials (AWSM) and the H2 from the Next-generation of Electrolyzers of Water (H2NEW) consortia were formed and supported by the Department of Energy (DOE) EERE Hydrogen and Fuel Cell Technologies Office (HFTO). HydroGEN (https://www.energy.gov/eere/h2awsm/) consortium, established in 2016, is an Energy Materials Network (EMN) that aims to accelerate the materials R&D of low technology readiness level (TRL) advanced water splitting (AWS) technologies. The consortium comprises five core national laboratories and focuses on four early-stage AWS pathways: alkaline exchange membrane (AEM) electrolysis, proton conducting solid oxide electrolysis (p-SOEC), photoelectrochemical, and thermochemical water splitting. Liquid alkaline and PEM electrolyzers are already commercial and significant advancements in oxygen conducting solid oxide electrolysis cells (o-SOECs) have been realized. Yet, these systems are still too expensive and not sufficiently durable for wide-scale commercialization. To enable high-volume manufacturing of affordable, durable, efficient electrolyzers, H2NEW (https://h2new.energy.gov/), another multi-lab consortium, was established in 2020. This comprehensive, concerted effort is focused on overcoming barriers related to components and materials integration and scale-up to achieve performance, durability, with an initial focus to achieve $2/kg H2 by 2026.

AEM↗
DOE OSTI2022

Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5

Abstract. There is growing interest in data-driven weather prediction (DDWP), e.g., using convolutional neural networks such as U-NET that are trained on data from models or reanalysis. Here, we propose three components, inspired by physics, to integrate with commonly used DDWP models in order to improve their forecast accuracy. These components are (1) a deep spatial transformer added to the latent space of U-NET to capture rotation and scaling transformation in the latent space for spatiotemporal data, (2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and (3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals. To show the benefit and feasibility of each component, we use geopotential height at 500 hPa (Z500) from ERA5 reanalysis and examine the short-term forecast accuracy of specific setups of the DDWP framework. Results show that the spatial-transformer-based U-NET (U-STN) clearly outperforms the U-NET, e.g., improving the forecast skill by 45 %. Using a sigma-point ensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we show that stable, accurate DA cycles are achieved even with high observation noise. This DDWP+DA framework substantially benefits from large (O(1000)) ensembles that are inexpensively generated with the data-driven forward model in each DA cycle. The multi-time-step DDWP+DA framework also shows promise; for example, it reduces the average error by factors of 2–3. These results show the benefits and feasibility of these three components, which are flexible and can be used in a variety of DDWP setups. Furthermore, while here we focus on weather forecasting, the three components can be readily adopted for other parts of the Earth system, such as ocean and land, for which there is a rapid growth of data and need for forecast and assimilation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A multi-terawatt two-color beam for high-power field-controlled nonlinear optics

Two-color laser beams are instrumental in light-field control and enhancement of high-order harmonic, spectral supercontinuum, and terahertz radiation generated in gases, plasmas, and solids. Here we demonstrate a multi-terawatt two-color beam produced using a relativistic plasma mirror, with 110 mJ at 800 nm and 30 mJ at 400 nm. Both color components have high spatial quality and can be simultaneously focused, provided that the plasma mirror lies within a Rayleigh range of the driving fundamental beam. Favorable scaling of second-harmonic generation by plasma mirrors at relativistic intensities suggests them as an excellent tool for multi-color waveform synthesis beyond the petawatt level.

42 ENGINEERING↗
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↗