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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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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗
NASA NTRS2019

Effect of Out-Gassing on the Onset of Hypersonic Boundary Layer Transition

Prediction and control of the onset of transition and the associated variation in aerothermodynamic parameters in high-speed flows is key to optimize the performance and design of Thermal Protection Systems (TPS) of next-generation aerospace vehicles [1]. Boundary Layer Transition (BLT) characteristics can influence the surface heating budget determining the TPS thickness and consequently its weight penalty. Ablative heatshields are designed to alleviate the high heat flux at the surface through pyrolysis of their polymeric matrix and subsequent fiber ablation [2]. Pyrolysis leads to out-gassing and non-uniform ablation lead to surface roughness, both of which are known to influence the transition process. An ablator impacts BLT through three main routes: gas injecting into the boundary layer from the wall, changing the surface heat transfer due to wall-flow chemical reactions, and modifying surface roughness [3]. In preparation to Mars 2020 mission post-flight analysis, the predictive transition capability has been initiated toward hard-coupling porous material response analysis and aerothermal environment calculation.

Karimi, Mona↗
DOE OSTI2024

The Mobility of Mo during Microbially Mediated Ferrihydrite Phase Transformation

Molybdenum (Mo) is an essential nutrient for almost all organisms. However, at high concentrations, it can be toxic to animals and plants. This study investigated the interactions of Mo(VI) with iron oxyhydroxides during ferrihydrite bioreduction in the presence of the Fe(III)- reducing Geobacter sulfurreducens. Here, in this study, we showed that Mo concentration controlled ferrihydrite phase transformation, leading to Mo release. With the biotic reduction of ferrihydrite and Fe(II) production, Mo(VI) reduction and Mo(IV)O 2 formation were observed for the first time, which further immobilised Mo after surface adsorption of Mo(VI). At low Mo levels (Mo/Fe molar ratios of 1-2 %), sufficient Fe(II) adsorption onto ferrihydrite resulted in its transformation into magnetite nanoparticles (>80%, ~25 nm) which catalysed the reduction of Mo(VI) to form Mo(IV)O 2 and immobilised Mo. Contrastingly, at high Mo concentrations (Mo/Fe molar ratios of 5-10%), Mo(VI)O 4 2- adsorption onto ferrihydrite limited Fe(II) adsorption, subsequently less magnetite (<8-12%) formed while more goethite (~30- 50%, width & length > 15 & 100 nm, respectively) and siderite (~20-30%, width & length > 100 & 200 nm, respectively) with larger particle sizes formed instead, causing Mo(VI) release due to lower Mo adsorption. This study provides a comprehensive understanding of the interaction mechanisms among Geobacter sulfurreducens, Mo(VI), and iron oxyhydroxides, enabling predictions and controls of long-term Mo mobility and Fe mineral transformation under a variety of biogeochemical scenarios.

Geobacter sulfurreducens↗
NASA NTRS2007

Modeling and Parameter Estimation of Spacecraft Fuel Slosh with Diaphragms Using Pendulum Analogs

Prediction and control of liquid slosh in moving containers is an important consideration in the design of spacecraft and launch vehicle control systems. Even with modern computing systems, CFD type simulations are not fast enough to allow for large scale Monte Carlo analyses of spacecraft and launch vehicle dynamic behavior with slosh included. It is still desirable to use some type of simplified mechanical analog for the slosh to shorten computation time. Analytic determination of the slosh analog parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices such as elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks, these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the hand-derived equations of motion for the mechanical analog are evaluated and their results compared with the experimental results. This paper will describe efforts by the university component of a team comprised of NASA's Launch Services Program, Embry Riddle Aeronautical University, Southwest Research Institute and Hubert Astronautics to improve the accuracy and efficiency of modeling techniques used to predict these types of motions. Of particular interest is the effect of diaphragms and bladders on the slosh dynamics and how best to model these devices. The previous research was an effort to automate the process of slosh model parameter identification using a MATLAB/SimMechanics-based computer simulation. These results are the first step in applying the same computer estimation to a full-size tank and vehicle propulsion system. The introduction of diaphragms to this experimental set-up will aid in a better and more complete prediction of fuel slosh characteristics and behavior. Automating the parameter identification process will save time and thus allow earlier identification of potential vehicle performance problems.

Chatman, Yadira↗
NASA NTRS1994

Fabric geometry distortion during composites processing

Waviness and tow misalignment are often cited as possible causes of data scatter and lower compression stiffness and strength in textile composites. Strength differences of as much as 40 percent have been seen in composites that appear to have the same basic material and structural properties -- i.e., yarn orientation, yarn size, interlacing geometry. Fabric geometry distortion has been suggested as a possible reason for this discrepancy, but little quantitative data or substantial evidence exists. The focus of this research is to contribute to the present understanding of the causes and effects of geometric distortion in textile composites. The initial part of the study was an attempt to gather qualitative information on a variety of textile structures. Existing and new samples confirmed that structures with a significant direction presence would be more susceptible to distortion due to the compaction process. Thus, uniweaves (fiber vol frac: 54-72 percent) biaxial braids (vf: 34-58 percent) demonstrated very little fabric geometry distortion. In stitched panels, only slight buckling of z-direction stitches was observed, primarily near the surface. In contrast, for structures with high compaction ratios -- e.g., large cylindrical yarns (2.5:1) orpowder towpreg (4:1) -- there were visible distortions where previously smooth and periodic undulations were transformed to abrupt changes in direction. A controlled study of the effect of forming pressure on distortion was conducted on type 162 glass plain weave fabrics. Panels (6 x 6 in) were produced via a resin infusion type setup, but with an EPON 815 epoxy resin. Pressures ranging from hand layup to 200 psi were used (vf: 34-54 percent). Photomicrographs indicated that at pressures up to 50 psi, large changes in thickness were due primarily to resin squeeze out. At higher pressures, when intimate contact was made between the layers, there was some tow flattening and in-plane shifting to optimize nesting. However, even at 200 psi the period and amplitude of the tow undulation remained constant, suggesting that for this relatively fine fabric, distortions from compaction were not a problem. Because of the interest in using larger tows (to reduce cost) and more complex structures, tests were also run on 2D triaxial glass braid (113 yd/lb at 0, 225 yd/lb at +/- 45). Forming pressures of 20, 50, 200, and 500 psi were used, and short block compression tests were run. The 500 psi specimen had a 10 percent decrease in modulus and an almost 50 percent decrease in strength (vs. 20 psi). Because the total fiber wgt/panel was kept constant, the thickness varied from 0.32 to 0.22 in (49-70 percent vf). Yet, the strength value is clearly below what would be expected, even with the decrease in thickness. Photomicrographs of these samples will be taken to determine if more fabric distortion exists in the 500 psi specimens. Finally, because the ultimate goal is to be able to predict and control distortion in a variety of textile structures, a model compaction test was developed to directly measure the deformation of the tows during compaction. Layers of dry glass fabric were placed in a mold with a clear plexiglass window. The yarn amplitude and period was then calculated using image analysis of the videotaped deformation. Preliminary tests demonstrated the feasibility of this technique for simple fabrics with large tows.

Chen, Julie↗
DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗
DOE OSTI2025

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks↗
DOE OSTI2024

Melting and solidification dynamics during laser melting of reaction-based metal matrix composites uncovered by in-situ synchrotron X-ray diffraction

Laser additive manufacturing (AM) of reaction-based metal matrix composites (MMCs) involves highly complex and non-equilibrium material transformation behavior, including melting, dissolution, precipitation, and solidification. Yet, the dynamics and interplay of these phase transformation processes remain poorly understood, posing substantial challenges in identifying the microstructure formation mechanism, and predicting and controlling the microstructure in the printed parts. Here, in this study, we performed the in-situ X-ray diffraction experiment to characterize the phase evolution dynamics of the 316L + 10 vol.%TiC system during laser melting, which provides direct and quantitative insights of the complex phase reaction and evolution dynamics under rapid heating and cooling conditions relevant to additive manufacturing of reaction-based MMCs. Further in-depth thermodynamic and kinetic calculations revealed that most of the phase evolution behavior observed in the in-situ X-ray diffraction experiment cannot be solely explained by widely used equilibrium thermodynamic models, and diffusion-controlled nonequilibrium dissolution and precipitation kinetics must be considered to elucidate the complex phase evolution behavior, including incomplete TiC dissolution, and three-step TiC precipitation. The three distinct types of precipitates generate unique hierarchical TiC micro- and nanostructures, which enhances the yield strength from 513 MPa to 877 MPa by 71 %, tensile strength from 628 MPa to 1054 MPa by 68 %, and Young's modulus from 193 GPa to 221 GPa by 14 %. The findings of our research provide the knowledge foundation for the design of unique microstructures and advanced MMC materials through laser AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Opportunities in multiscale modeling of mosquito-borne flaviviruses

Mosquito-borne flaviviruses, such as Zika, dengue, West Nile, and yellow fever virus, represent a growing public health concern due to their widespread distribution and the severe diseases they cause. These viruses are difficult to control as climate change and urbanization help mosquitoes expand into new areas, increasing the risk of outbreaks. Mathematical models play a key role in understanding their spread, providing insights at every level—from how the virus multiplies inside cells to how it circulates through entire populations. This review examines various approaches used in modeling arboviruses, including microscale models that focus on cellular and molecular dynamics, mesoscale models that address within-host processes, and macroscale models that capture population-level transmission. We briefly summarize the methodology used for models at each scale, which primarily consists of sets of differential equations with parameters that represent physical rates of change for different subprocesses. We particularly highlight how temperature affects virus transmission, which is key to understanding the impact of climate change. We also show how multiscale models can connect viral replication, immune response, and the spread of infection at a larger scale. This is essential for developing better vaccines and treatments, evaluating disease control measures, predicting the impact of climate change, and improving public health responses to outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2024

The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes

The nature of ion–ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion–ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene’s electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.

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

Direct Ab Initio Simulation of the Synthesis of BaZrO 3 and the Microstructure Impacts on Proton Transport

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.

organic↗
DOE OSTI2026

Understanding Electrochemical Alcohol Hydrogenolysis Enabled by Carbonyl Reduction in Lignocellulosic Biomass-Derived Aromatic Oxygenates

Molecules derived from lignocellulosic biomass are oxygenates with multiple oxygen-containing functional groups, such as hydroxyl and carbonyl groups. Therefore, the ability to selectively reduce a specific oxygenate group is essential for the reductive upgrading of such molecules. Previous studies on electrochemical biomass conversion have shown that alcohol hydrogenolysis, which involves cleavage of the σ(C–O alcohol ) bond, is extremely challenging for furfural and 5-hydroxymethylfurfural (HMF) derivatives, including furfuryl alcohol, 5-methylfurfuryl alcohol (MFA), and 2,5-bis(hydroxymethyl)furan (BHMF). In contrast, HMF itself undergoes alcohol hydrogenolysis relatively easily in acidic aqueous media. Considering that the only structural difference between HMF and BHMF or MFA is the presence of a carbonyl group, this observation raises the question of whether the carbonyl group in HMF facilitates alcohol hydrogenolysis. In this study, we designed systematic experiments to provide a coherent explanation of when and how a carbonyl group enables hydrogenolysis of a copresent alcohol group. Specifically, we show that alcohol hydrogenolysis in HMF proceeds via reduction of the carbonyl group to a ketyl radical, followed by a spin-center shift (SCS) through extended π conjugation. We also elucidate the effect of pH on the selectivity between carbonyl hydrogenation and alcohol hydrogenolysis, both of which share the ketyl radical intermediate. This mechanistic understanding enhances our ability to predict and control alcohol hydrogenolysis in the reductive upgrading of biomass-derived oxygenates.

alcohols↗
DOE OSTI2025

Cesium exhibits different mesoscale segregation and ion pairing than lithium, sodium, or potassium in concentrated alkaline aqueous nitrite solutions

Understanding ion pairing in concentrated alkaline electrolytes facilitates the prediction and control of chemical processes in nuclear waste. While sodium is the dominant alkali metal in such systems, cesium often exhibits distinct bulk behavior relative to lighter alkali cations. Aqueous mixtures of cesium hydroxide and sodium nitrite were compared to alkali hydroxide analogs using small-angle x-ray scattering, revealing that cesium disrupts the sodium nitrite electrolyte structure, forming cesium-rich domains. This mesoscale segregation contrasts with the near-ideal mixing observed in other mixed hydroxide–nitrite systems. Raman spectroscopy indicates cesium–nitrite ion pairing, evidenced by vibrational shifts distinct from those associated with sodium. Multinuclear magnetic resonance spectroscopy further supports cesium–nitrite and cesium–hydroxide association, revealing a distinct local environment for nitrite in cesium-containing solutions. Together, these findings show that cesium promotes a unique solution structure dominated by specific ion pairing within segregated domains. This structural organization may influence radical generation pathways in high-ionic-strength alkaline media relevant to nuclear waste processing and management.

Nienhuis, Emily T. [Pacific Northwest National Lab↗
DOE OSTI2025

Transport and confinement physics: Chapter 2 of the special issue: on the path to tokamak burning plasma operation

Progress in physics understanding and theoretical model development of plasma transport and confinement (TC) in the ITPA TC Topical Group since the publication of the ITER Physics Basis (IPB) document (Doyle et al 2007 Nucl. Fusion 47 S18) was summarized focusing on the contributions to ITER and burning plasma prediction and control. This paper provides a general and streamlined overview on the advances that were mainly led by the ITPA TC joint experiments and joint activities for the last 15 years (see JEX/JA table in appendix). This paper starts with the scientific strategy and scope of the ITPA TC Topical group and overall picture of the major progress, followed by the progress of each research field: particle transport, impurity transport, ion and electron thermal turbulent transport, momentum transport, impact of 3D magnetic fields on transport, confinement mode transitions, global confinement, and reduced transport modeling. Cross references with other Topical Groups are given in order to highlight overlapped topics, such as the 3D effect on the plasma transport in the edge and L-H transition physics. The increasing overlap between the topical groups is a reflection of the progress on integrating the known physics into comprehensive models that are better and better able to reproduce the plasma transport. In recent years, such integration has become increasingly prevalent when considering transport from the SOL, through the edge pedestal, and into the plasma core. In the near future, increased collaboration also with the magneto-hydrodynamic and energetic particles community will be important as we approach burning plasma conditions in next-step fusion devices. A summary of remaining challenges and next steps for each research field is given in the Summary section.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Design of the Electromagnetic Particle Injector (EPI) for Tokamak Deployment

Both predicting and controlling disruptions are critical and urgent issues for ITER as some disruptions with a short warning time may be unavoidable. For these cases, a rapid response disruption mitigation system (DMS) referred to as the electromagnetic particle injector (EPI) is being developed. The primary advantages of the EPI are its fast response time and high velocity, which have been demonstrated in offline experiments (Raman et al., 2021). The EPI is capable of accelerating a metallic sabot electromagnetically using a rail gun to the required velocities ( > 2 km/s) within 2 ms. Two high-field racetrack magnets able to generate fields over 2 T are positioned above and below the rails to permit high velocity at low rail currents, a requirement to minimize electrode erosion. At the end of the acceleration phase, a sabot capture mechanism retains the spent sabot inside the vacuum chamber that houses the EPI. At this point, it releases well-defined microspheres, or a shell pellet, of a radiative payload into the disrupting plasma. A remotely operated sabot loading system positioned behind the injector contains several pre-equipped sabots that can be loaded by an operator from the tokamak control room. The injector is interfaced to the tokamak through a guide tube attached to the front of the EPI vacuum chamber. Finally, the advantages of the EPI system over other DMSs under consideration are described in conjunction with the details of an EPI system designed for near-term test on an existing large tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗
DOE OSTI2026

Biomanufacturing and Scale-Up: Pathways to Biochemicals, Biofuels, and Biomaterials

Advancing the bioeconomy requires the development of large-scale microbial bioprocesses capable of converting waste carbon streams into biofuels, biochemicals, and biomaterials at industrially relevant scales. While biomanufacturing has been successfully demonstrated at the laboratory scale for a wide range of chemicals, only a few have reached industrial-scale production. This is partly due to the inherent complexity of microbial systems, which rely on living cells with intricate metabolic pathways that are highly sensitive to environmental changes, making large-scale production difficult to optimize and predict. As a result, scaling-up bioprocesses remains a high-stakes challenge that requires deeper exploration. This involves integrating feedstock and microbial selection, upstream and downstream processes, and computational modelling, among other research efforts. Bulk and specialty chemicals derived from biological processes also face competition from fossil-based production routes, which have been refined through decades of technological advancements. While biologically derived molecules may offer more environmentally friendly production pathways than traditional chemical manufacturing, their widespread adoption depends on achieving cost parity-or superiority-relative to fossil-based methods. This emphasizes the importance of holistic research, including techno-economic analyses and life cycle assessments, to ensure both economic viability and environmental sustainability. This editorial and special issue explores state-of-the-art strategies for converting waste carbon sources into valuable products. It discusses how enzymes, single microbes (e.g., extremophiles), and microbiomes (e.g., through division of labor) can be integrated with upstream and downstream process innovations-such as consolidated bioprocessing and in situ product recovery-to improve the efficiency and scalability of biomanufacturing. The editorial further highlights the role of computational modelling in understanding, predicting, and controlling bioprocess performance across scales, and concludes by emphasizing the importance of techno-economic modelling to identify technologies that can move to market.

09 BIOMASS FUELS↗