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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Performance and cost of fuel cells for off-road heavy-duty vehicles

Hydrogen fuel cell-powered off-road vehicles can replace hydrocarbon-powered off-road vehicles to reduce carbon dioxide and criteria pollutant emissions in the agriculture, construction, and mining industries. Vehicle manufacturers and customers recognize health and environmental benefits of emissions reductions but are ultimately compelled by the total cost of ownership of their vehicles. In this study, we conduct a technoeconomic evaluation for three hallmark off-road vehicles, tractors, wheel loaders, and excavators, comparing vehicles with hydrogen fuel cell + battery hybrid powertrains to traditional diesel powertrains. Stakeholder-valued performance metrics include fuel cell engine power, engine durability, hydrogen consumption rate, hydrogen storage system volume, and energy-regenerative drivetrain efficiency. The total cost of ownership includes capital, operating and maintenance, fuel, and fuel storage costs. State-of-the-art fuel cell powered wheel loaders and excavators are currently cost competitive with diesel platforms. If targeted improvements to cost, performance, and durability improvements of fuel cell stacks and storage systems are achieved, fuel cell systems would be cost competitive for tractors and significantly lower total cost of ownership options for wheel loaders and excavators.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia

Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.

58 GEOSCIENCES↗
DOE OSTI2021

Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Bioconversion and quantification of humic substances from low rank coals using indigenous fungal isolates

The major portion of coals around the world belongs to lignitic origin, and burning of these Low Rank Coals (LRCs) not only produces energy, but also release harmful gases into the environment. On the other hand, coal is a mega source of fossilized carbon compounds that can be utilized in diverse range of carbon/organic industries. Biosolubilization is one of the safest and economical way to consume these LRCs for the harvest of humic and fulvic acids for industrial applications. Present studies deal with solubilization of coal samples from five different mines of Punjab, Pakistan by indigenously isolated fungus. From a number of indigenously isolated fungal isolates, an isolate NF-2 was selected based on its solubilization potential on solid Sabouraud medium. The intensity of coal biosolubilization was found maximum for coal sample B-1 (59%) and lowest for sample B-26 (17.2%) with 5% (w/v) pulp density at 28 ± 2°C. Humification index value (A 4 /A 6 ) for biosolubilized products of coal sample B-1 was found 6.18 which was much higher than the A 4 /A 6 value (4.27) of commercially available standard humic acid. Biosolubilized products from coal sample B-1 was found maximum with 8681 mg/L humic acid concentration as determined by standard curve method. FTIR and FE-SEM analyses of sample B-1 treated with NF-2 confirmed the structural break down and solubilization of coal. Molecular identification of fungal isolate NF-2 showed 100% similarity to Aspergillus flavus. Here, this work paves the way to utilize lignitic coals for the environment friendly and low-cost production of humic acids by fungal biosolubilization as an alternate to chemical methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2025

Ammonium-coordinated exchanger (ACE) functionalized silica sorbents for recovering/removing aqueous anionic contaminants

There are limited studies of functionalized silica anion exchange sorbents used for critical/heavy metal recovery/removal relative to polymeric and other inorganic materials. This work features ammonium-coordinated exchanger (ACE) anion exchange particle sorbents prepared by either acid-washing epoxy-crosslinked polyethylenimine (PEI) hydrogen bonded within/to a silica particle sorbent (two-step method) or reacting a di-chlorinated crosslinker, α,α-dichloro-p-xylene (DPX), with PEI within silica (single-step method). Energy dispersive X-ray spectroscopy (EDS) and infrared spectroscopy confirmed the presence of -NH 2 + ···Cl - and -NH 3 + ···Cl - groups, which removed oxyanionic species –arsenate, selenate, chromate, sulfate, phosphate, and nitrate– plus bromide from ideal solutions, authentic acid mine drainage (AMD), and authentic flue gas desulfurization (FGD) wastewater. Affinity of the anions for ACE varied across single- and mixed-element solutions. However, affinity for CrO 4 2- was among the highest in both cases. Total anion uptake by the optimized ACE, PEI-E3-HCl_1.1, reached 1.2 mmol anion/g-sorb. (0.56 mmol CrO 4 2- /g), or ∼2.3 mmol negative charge/g-sorb. This was close to the 0.52 mmol CrO 4 2- /g of a commercial anion exchange resin. Near-consistent removal of 20–80 % of each anion from FGD during an eight-cycle adsorption-desorption (1 M NaCl) test predicted good ACE viability for testing under practical conditions at larger scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The adsorption of As(V) on poorly crystalline Fe oxyhydroxides, revisited: Effect of the reaction media and the drying treatment

Arsenic (As) adsorbed on Fe oxyhydroxides (adsorbent) is widely occurring in many environmental settings such as in acid mine drainage systems or in the hydrometallurgical operations to form Fe-As coprecipitates. However, the influence of the reaction media and the drying treatment on the microstructure of the directly formed adsorbents at various pHs was still not fully understood. In this work, As adsorption behaviors on various forms of Fe oxyhydroxides were systematically investigated by using XRD, FTIR, Raman, XANES, and HRTEM. Overall, the results revealed that at weak acidic pH, more As could adsorbed on the suspension adsorbent formed in sulfate and chloride media than that in nitrate media, possibly due to the microstructure alteration of the adsorbent in the presence of sulfate and chloride. Besides, the increasing crystallinity of the Fe oxyhydroxides and the aggregation effect after drying were the major reasons why less As could be hold by the dried adsorbents than that of the corresponding suspension adsorbents. These findings could shed more light on the nature of the Fe oxyhydroxides which may be helpful for more precisely predicting the fate of some toxic metal(loid)s in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Superior removal of As(III) and As(V) from water with Mn-doped β-FeOOH nanospindles on carbon foam

Arsenic pollution of water is one of the severest environmental challenges threatening human health. Iron-based nanomaterials have been demonstrated effective in arsenic removal. However, they generally suffer from low removal efficiency towards highly toxic As(III), loss of active sites owing to agglomeration, and poor reusability. Herein, we report a carbonized melamine foam supported Mn(IV)-doped β-FeOOH nanospindles(CF@Mn-FeOOH NSp) for tackling the technical hurdles. The designed CF@Mn-FeOOH NSp appears as a free-standing monolith through a low-cost and straightforward hydrothermal method. The atomic-scale integration of Mn(IV) into β-FeOOH enables an oxidation-adsorption bifunctionality, where Mn(IV) serves as oxidizer for As(III) and Fe(III) acts as adsorber for As(V). The maximal adsorption capacity for As(V) and As(III) can reach 152 and 107 mg g -1 , respectively. Meanwhile, As in simulated high arsenic groundwater can be decreased to below 10 μg L -1 within 24 h. By simple "filtrating-washing", 85% and 82% of its initial adsorption capacity for As(V) and As(III) can be easily recovered even after 5-cycles reuse. Kinetics and isotherm adsorption study indicate that the arsenic adsorption behavior is mainly through chemical bonding during single-layer adsorbing process. The as-prepared CF@Mn-FeOOH offers a scalable, efficient, and recyclable solution for arsenic removal in groundwater and wastewater from mines and industry.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Molecular structures of dissolved and colloidal As V –Fe III complexes and their roles in the mobilization of As V under strongly acidic conditions

The effect of high concentration of iron (Fe III ) on the speciation and mobility of arsenic (As) under strongly acidic conditions remains unclear. This work studied the redistribution and speciation of As V and Fe III at Fe/As molar ratio of 1–14 and pH 1.5–2.0 in the dissolved, colloidal, and solid phases. Results showed that the elevated Fe III induced the decomposition of the precipitated poorly crystalline ferric arsenate by forming dissolved (< 3 kDa) and colloidal (3 kDa–0.1 µm) As–Fe complexes. The fraction of particulate As (> 0.1 µm) decreased from 70–90% to less than 20% when the Fe/As molar ratio increased from 1 to 14. The particle size of the bulk samples decreased significantly with the increase of Fe III concentration. The FTIR results suggested that AsV in dissolved/colloidal As–Fe complexes dominantly occurred as HAsO 4 2– species. The EXAFS results indicated that each HAsO 4 2– coordinated with approximately two Fe atoms in dissolved/colloidal As–Fe complexes at Fe/As ≥ 2. Furthermore, the findings suggest that high aqueous Fe III concentration can promote the mobility of As by forming dissolved/colloidal Fe–As complexes in acidic waters, potentially accelerating As transport from source to downstream in acid mine drainage systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Impact of neutron irradiation on the thermophysical properties of additively manufactured stainless steel and inconel

The deployment of additive manufacturing (AM) technologies to support the nuclear energy industry is limited by the current lack of data related to the irradiation performance of AM parts. The Colorado School of Mines is conducting a multi-year irradiation and post irradiation examination project to study the impacts of neutron irradiation on stainless steel 316L and Inconel 718 and 625 specimens produced by several commercially available AM techniques and irradiated in the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL). This paper presents initial results on the impact of neutron irradiation on the heat capacity, the thermal diffusivity, and the thermal conductivity of the specimens at temperatures between 100 oC and 400 oC. The unirradiated AM specimens have heat capacity values similar to, or lower than, the reference values, regardless of manufacturing method or material. The irradiated AM specimens have average heat capacities greater than the unirradiated specimens and reference values in every case. Additionally, the thermal diffusivities of the unirradiated AM specimens are either larger or statistically similar to the reference values. The irradiated AM specimens exhibit lower thermal diffusivities compared to the reference thermal diffusivity values. One of the stainless steel 316L laser powder bed fusion samples exhibited thermal diffusivity values with statistically significant anisotropy prior to and after irradiation. The thermal conductivities of the unirradiated AM stainless steel 316L specimens and the unirradiated Inconel 718 specimens are lower or statistically similar to the reference values. The unirradiated Inconel 625 specimens exhibit slightly higher thermal conductivity values than the reference values. The irradiated specimens show a significant increase in thermal conductivity compared to the unirradiated specimens and the reference values for all materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Direct recycling of spent nickel-rich cathodes in reciprocal ternary molten salts

Lithium-ion batteries (LIBs) have revolutionized portable electronics and electric vehicles (EVs), but the growing accumulation of end-of-life (EOL) batteries poses environmental challenges. Recycling high-value cathodes from EOL LIBs can minimize waste and reduce the need for mining critical minerals. Here, this study focuses on the direct recycling of Ni-rich cathodes, particularly lithium-manganese-cobalt-oxide (NMC) 622 in a “reciprocal ternary molten salts (RTMS)" system. The ionothermal relithiation in the RTMS system successfully restores the layered structure, lithium content, and electrochemical performance of the NMC 622 cathode, comparable to the pristine material. The cost analysis reveals that cathode regeneration through ionothermal relithiation is more economical than virgin production or conventional recycling methods.

25 ENERGY STORAGE↗
DOE OSTI2023

Unlocking the value of recycling scrap from Li-ion battery manufacturing: Challenges and outlook

The growing global trend toward mobile electrification is primarily driven by the rising popularity of electric vehicles, leading to an unprecedented surge in demand for lithium-ion batteries. As a result, the importance of battery recycling has become increasingly apparent. Battery recycling aims to recover valuable materials from both spent batteries and battery manufacturing scraps. By recycling these resources, the reliance on raw material extraction is reduced, which benefits resource conservation and minimizes the need for new mining operations. While significant attention has been given to the recycling of spent batteries, less emphasis has been placed on the recycling and recovery of battery scraps. However, it is important to recognize that many gigafactories are still taking steps to improve their manufacturing processes, and end-of-life batteries take approximately 10 years to become suitable for recycling, the manufacturing scraps will serve as the primary sources for recycling in this decade. This review delves into the progress in recycling technologies associated with battery manufacturing scraps, shedding light on the challenges, opportunities, and evolving perspectives surrounding battery manufacturing scrap recycling. In conclusion, we aim to contribute to the knowledge base of battery manufacturing scrap recycling and promote a more comprehensive understanding of the subject.

25 ENERGY STORAGE↗
DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Micro-photoluminescence mapping and Chemometrics for the rapid classification of rare earth materials

This article introduces advancements in chemically mapping rare earth materials using photoluminescence (PL) and chemometrics. By leveraging the high sensitivity and selectivity of PL compared to alternative optical techniques, as well as its compatibility with microscopy, we present enhanced capabilities for noninvasive material screening and characterization. Exemplary PL spectra of samarium(III) and europium(III) in oxide, nitrate, and chloride forms demonstrated the ability to extract detailed chemical information of diverse rare earth particles. Additionally, we introduced efficient PL mapping sequences capable of covering a 9 mm diameter carbon tab within minutes, which highlighted the benefits of rapid, large-area imaging. Furthermore, an integrated approach combining PL mapping with principal component analysis and a random forest classifier enabled the resolution of overlapping spectral peaks from different chemistries and provided accurate material classification. In conclusion, these advancements underscored the versatility and robustness of PL for chemically mapping rare earth materials, with the potential to support applications in mining, energy, environmental monitoring, isotope production and beyond.

Chemometrics↗
DOE OSTI2021

The effect of caustic soda treatment to recover rare earth elements from secondary feedstocks with low concentrations

Caustic soda (NaOH) treatment has enhanced rare earth element (REE) extraction from monazite. Here, this study examined the effectiveness of caustic soda for RE-phosphates with lower concentrations for extraction from coal overburden, an alternative feedstock derived from mining waste. Pourbaix (Eh-pH) diagrams were constructed for La, Ce, Nd, Eu, and Y-PO 4 -H 2 O systems to understand the thermodynamics of NaOH-facilitated acid leaching for low-concentration RE-phosphates and oxides and to elucidate the difference in recoveries among individual REEs. The Eh-pH diagrams indicated that low crystallinity is beneficial for extraction. Nd was thermodynamically most favorable for the extraction process among the five elements examined, and Y was the least promising. Experiments were conducted using three Pennsylvania coal overburden samples to validate the effect of caustic soda on rare earth leaching. The experimental results showed that for NaOH (6 M), a leaching time of 2 h at 125 °C was sufficient to obtain twice the recovery values compared to those without the NaOH treatment. After implementing the caustic soda treatment, a high total REE recovery (>60%), especially for Nd and Pr, could be achieved with diluted HCl and more environmentally friendlier citric acid. The results were similar for both coarse and fine particles. This novel approach is likely to improve the feasibility of REE extraction from coal-associated feedstock in a more environmentally benign manner.

42 ENGINEERING↗
DOE OSTI2022

A review study of rare Earth, Cobalt, Lithium, and Manganese in Coal-based sources and process development for their recovery

The world’s majority predominantly relies on foreign sources for supplying critical minerals. This renders the countries susceptible to supply chain disruption and urges them to develop alternative sources to compete in technological advancements. Among many proven newly identified sources, coal and coal-based materials present their opportunities. Due to the complex structure of coal, many mineral forms can be found in coal-related sources, including critical element-containing minerals. Among the fifty critical elements and minerals defined by the USGS, the rare earth elements, cobalt, lithium, and manganese, are particularly significant as they have enormous importance in high-technology product development. These critical metals can be found in high concentrations in coal-derived sources, such as coal refuse, coal combustion products, and coal acid mine drainage, and be recovered through various separation techniques. Further, this study aims to advance the understanding of these elements' extraction and commercial potential from coal and coal-related sources and help address the world's challenges in the critical mineral supply chain. The concentrations and occurrences of these four commodities in coal-related sources were first reviewed. Later, their recovery processes were discussed, and a conceptual process flowsheet was proposed to selectively recover the rare earth elements, lithium, cobalt, and manganese from coal-based sources.

58 GEOSCIENCES↗
DOE OSTI2023

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

59 BASIC BIOLOGICAL SCIENCES↗