Collection Efficiency Comparison of N95 Respirators Saturated with Artificial Perspiration
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Search NASA
Results for “Respiration, Artificial”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Shewanella oneidensis MR-1 respires CdSe quantum dots for photocatalytic hydrogen evolution
Living bio-nano systems for artificial photosynthesis are of growing interest. Typically, these systems use photoinduced charge transfer to provide electrons for microbial metabolic processes, yielding a biosynthetic solar fuel. Here, we demonstrate an entirely different approach to constructing a living bio-nano system, in which electrogenic bacteria respire semiconductor nanoparticles to support nanoparticle photocatalysis. Semiconductor nanocrystals are highly active and robust photocatalysts for hydrogen (H 2 ) evolution, but their use is hindered by the oxidative side of the reaction. In this system, Shewanella oneidensis MR-1 provides electrons to a CdSe nanocrystalline photocatalyst, enabling visible light-driven H 2 production. Unlike microbial electrolysis cells, this system requires no external potential. Illuminating this system at 530 nm yields continuous H 2 generation for 168 h, which can be lengthened further by replenishing bacterial nutrients.
Science & Technology - Other Topics↗
Interaction of Soil pH and Mineralogy Controls Soil Organic Matter Persistence through Changes in the Composition and Amount of Microbial Necromass
Microbial necromass–mineral associations are key to long-term soil organic matter (SOM) persistence. However, how soil pH and mineralogy interact to regulate SOM stability remains poorly understood. Here, we used artificial soils to test how three clay minerals (bentonite, kaolinite, and goethite), adjusted to four pH levels (5–8), affect microbial activity (respiration), microbial physiology (carbon use efficiency, CUE), microbial-derived residue material (necromass), and the formation and stability of mineral-associated organic matter (MAOM). Artificial soils were inoculated with a rhizosphere-derived microbial community cultured under the same pH conditions and on two representative simulated exudate types (organic acids and carbohydrates) and incubated for 6 weeks. In two complementary experiments, we added necromass from known microbial taxa to the same minerals across pH levels to isolate the role of necromass chemistry and loading. We found that soil pH shaped MAOM chemistry by altering microbial activity and necromass composition. In interaction with mineral type, pH also controlled MAOM thermal stability. Higher necromass loading weakened mineral-organic bonding, reducing MAOM stability, consistent with zonal mineral–organic interaction models. Our results demonstrate that microbial activity, rather than carbon use efficiency, better predicts MAOM formation and that pH-dependent necromass composition and loading govern MAOM persistence. These findings advance mechanistic understanding of SOM stabilization and have implications for predicting soil carbon dynamics under shifting environmental conditions.
carbon use efficiency↗
Modeled Surface-Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks
The eddy covariance (EC) method is one of the most widely used approaches to quantify surface-atmosphere fluxes. However, scaling up from a single EC tower to the landscape remains an open challenge. To address this, we used 63 site years of data to examine simulated annual and growing season sums of carbon fluxes from three paired land-cover type sites of corn, restored-prairie, and switchgrass ecosystems. This was also done across the landscape by modeling fluxes using different land-cover type input data. An artificial neural network (ANN) approach was used to model net ecosystem exchange (NEE), ecosystem respiration (R eco ), and gross primary production (GPP) at one paired site using environmental observations from the second site only. With a mean spatial separation of 11 km between paired sites, we were able to model annual sums of NEE, R eco , and GPP with uncertainties of 20%, 22%, and 8%, respectively, relative to observation sums. When considering the growing season only, model uncertainties were 17%, 22%, and 9%, respectively for the three flux terms. We also show that ANN models can estimate sums of R eco and GPP fluxes without needing the constraint of similar land-cover-type, with annual uncertainties of 12% and 10%. These results provide new insights to scaling up observations from one EC site beyond the footprint of the EC tower to multiple land-cover types across the landscape.
54 ENVIRONMENTAL SCIENCES↗
Physiological, genomic, and sulfur isotopic characterization of methanol metabolism by Desulfovibrio carbinolicus
Methanol is often considered as a non-competitive substrate for methanogenic archaea, but an increasing number of sulfate-reducing microorganisms (SRMs) have been reported to be capable of respiring with methanol as an electron donor. A better understanding of the fate of methanol in natural or artificial anaerobic systems thus requires knowledge of the methanol dissimilation by SRMs. In this study, we describe the growth kinetics and sulfur isotope effects of Desulfovibrio carbinolicus, a methanol-oxidizing sulfate-reducing deltaproteobacterium, together with its genome sequence and annotation. D. carbinolicus can grow with a series of alcohols from methanol to butanol. Compared to longer-chain alcohols, however, specific growth and respiration rates decrease by several fold with methanol as an electron donor. Larger sulfur isotope fractionation accompanies slowed growth kinetics, indicating low chemical potential at terminal reductive steps of respiration. In a medium containing both ethanol and methanol, D. carbinolicus does not consume methanol even after the cessation of growth on ethanol. Among the two known methanol dissimilatory systems, the genome of D. carbinolicus contains the genes coding for alcohol dehydrogenase but lacks enzymes analogous to methanol methyltransferase. We analyzed the genomes of 52 additional species of sulfate-reducing bacteria that have been tested for methanol oxidation. There is no apparent relationship between phylogeny and methanol metabolizing capacity, but most gram-negative methanol oxidizers grow poorly, and none carry homologs for methyltransferase (mtaB). Although the amount of available data is limited, it is notable that more than half of the known gram-positive methanol oxidizers have both enzymatic systems, showing enhanced growth relative to the SRMs containing only alcohol dehydrogenase genes. Thus, physiological, genomic, and sulfur isotopic results suggest that D. carbinolicus and close relatives have the ability to metabolize methanol but likely play a limited role in methanol degradation in most natural environments.
54 ENVIRONMENTAL SCIENCES↗
Mineral reactivity determines root effects on soil organic carbon
Modern conceptual models of soil organic carbon (SOC) cycling focus heavily on the microbe-mineral interactions that regulate C stabilization. However, the formation of ‘stable’ (i.e. slowly cycling) soil organic matter, which consists mainly of microbial residues associated with mineral surfaces, is inextricably linked to C loss through microbial respiration. Therefore, what is the net impact of microbial metabolism on the total quantity of C held in the soil? To address this question, we constructed artificial root-soil systems to identify controls on C cycling across the plant-microbe-mineral continuum, simultaneously quantifying the formation of mineral-associated C and SOC losses to respiration. Here we show that root exudates and minerals interacted to regulate these processes: while roots stimulated respiratory C losses and depleted mineral-associated C pools in low-activity clays, root exudates triggered formation of stable C in high-activity clays. Moreover, we observed a positive correlation between the formation of mineral-associated C and respiration. This suggests that the growth of slow-cycling C pools comes at the expense of C loss from the system.
54 ENVIRONMENTAL SCIENCES↗
Positive correlations between soil C stabilization and loss are regulated by roots, minerals, and microbes
Modern conceptual models of soil organic carbon (SOC) cycling place a strong focus on the microbe-mineral interactions that regulate C stabilization. However, the formation of ‘stable’ soil organic matter, which consists mainly of microbial residues bonded to mineral surfaces, is inextricably linked to C loss through microbial respiration. Therefore, what is the net impact of microbial metabolism on the total quantity of C held in the soil? To address this question, we constructed artificial root-soil systems to identify controls on C cycling across the plant-microbe-mineral continuum, simultaneously quantifying the formation of persistent mineral-associated C and C losses to respiration. Roots and minerals interacted to regulate these processes: while roots stimulated respiratory C losses and depleted mineral-associated C pools in low-activity clays, roots triggered formation of stable C in high-activity clays. Moreover, we observed a strong positive correlation between the formation of mineral-associated C and respiration. This suggests that the growth of persistent C pools which can resist subsequent disturbance comes at the expense of C loss from the system.
58 GEOSCIENCES↗
Evaluating the Persistence of Soil Carbon After a 30-Year Nitrogen Fertilization Experiment at the Fernow Experimental Forest, WV
We sampled soils in a previously nitrogen (N)-fertilized watershed (Watershed 3), which received 35 kg N per hectare annually for 30 years, and a reference watershed (Watershed 7), 4 years after the end of a 30-year N fertilization experiment at the Fernow Experimental Forest in West Virginia. As N deposition has reduced soil pH and plant carbon (C) investments into the rhizosphere, we compared the extent to which removing these potential limitations to microbial decomposition by increasing soil pH, adding artificial root exudates, or elevating soil temperature would increase microbial decomposition (and soil C losses) in soils that have and have not received excess N inputs. As such, we incubated soils with and without the addition of artificial root exudates and dolomitic lime (2 amendment treatments and 1 untreated control), at three different temperatures (15, 20, and 25 C) to simulate warming. We measured soil respiration, enzyme activity (acid phosphatase, N -acetyl-β-glucosaminidase, beta-glucosidase, phenol oxidase, and peroxidase), and microbial biomass carbon in a 15-week microcosm experiment. All of the datasheets include the metadata for all of the treatment combinations from this incubation experiment (ID, incubation temperature, treatment, watershed).The "Soil_Chemistry_Data.csv" data includes (1) the ratio of dry soil weight (g) by wet (or fresh) soil weight (g) after 72 hours at 65 C, (2) the wet (or fresh) weight of the soil samples used to determine soil pH, and (3) soil pH, which was measured in CaCl2. Detailed methods for the measurements of soil pH are described in the datasheet itself.The "Respiration_Data.csv" data includes (1) respiration day for the measurement, and (2) cumulative CO2 respiration (mmol) measured in the microcosms on specific days.The "Microbial_Biomass_Data.csv" data includes (1) mmol microbial biomass carbon per g (dry weight) of soil in each microcosm.The "Enzyme_Data.csv" data includes (1) all extracellular soil enzyme activity (N-acetyl-glucosaminidase (NAG), acid phosphatase (AP), β-glucosidase (BG), phenol oxidase and peroxidase) that we measured in our microcosms.
54 ENVIRONMENTAL SCIENCES↗
Cellular and extracellular C contributions to respiration after wetting dry soil
Wetting of dry soil triggers a pulse of microbial respiration that has been attributed to two broad mechanisms: (1) recycling of microbial cellular carbon (C), and (2) consumption of extracellular organic C made available to microbes by wetting. We evaluated these two mechanisms by measuring cumulative CO 2 release, changes in the size and chemical composition of microbial biomass, and water-extractable organic carbon (WEOC) concentrations following artificial wetting of soil sampled from two depths at each of seven sites across California spanning a range of geologic parent materials. In samples collected from surface soil (0–10 cm depth), we found that cumulative CO 2 release after wetting in the laboratory was most strongly correlated with microbial biomass. In these samples, the relative abundance of trehalose—a putative microbial osmolyte—decreased from 25% (SD = 12) to 16% (SD = 7) of the chloroform-labile fraction of the microbial biomass after wetting. This suggested a role for osmolyte consumption in generating the respiration pulse. In subsoil (40–50 cm depth, or sampled at contact with rock), however, the cumulative CO2 release after wetting was unrelated to microbial biomass and more strongly related to WEOC. The concentrations of selected microbial biomass constituents (e.g. trehalose and amino acids) in WEOC were negligible (< 1%), suggesting that cell lysis was not important in generating WEOC in this study. The amount of WEOC relative to total organic C was greatest in subsoil, and negatively related to ammonium oxalate-extractable Fe (Pearson’s R = 0.42, p < 0.01), suggesting a role for soil mineralogical properties in controlling WEOC release. Together, these findings suggest that microbial cellular C and extracellular C jointly contribute to the respiration pulse, and that their relative contribution depends on depth.
59 BASIC BIOLOGICAL SCIENCES↗
Caution on Using Tetrahydrofuran for Processing Crystalline Silica Samples From Engineered Stone for XRD Analysis
Abstract We conducted laboratory experiments to investigate a suspected effect of tetrahydrofuran (THF) on quantifying crystalline silica in samples collected from working with engineered stone when THF is used to process samples prior to the X-ray diffraction (XRD) analysis. Two groups of samples from grinding either engineered stone or granite were simultaneously taken from a laboratory testing system, with one group of samples using THF for processing and another group using muffle furnace for ashing. For each stone type, we also tested four levels of respirable dust loading on the samples by varying the grinding time from 1 to 8 min. Statistical analysis of the experimental results on crystalline silica contents of the two groups of samples showed that the difference between the two methods was not significant (P ≥ 0.05) for the granite at all four levels of respirable dust loading and for the engineered stone at the two levels of respirable dust loading greater than 0.5 mg. However, the crystalline silica content from using THF processing was significantly lower (P = 0.001) than that from using muffle furnace ashing for engineered stone when the respirable dust loading levels were less than 0.5 mg. For the engineered stone dust samples with grinding times of 1 and 2 min, the average respirable dust loading was about 0.19 and 0.34 mg, respectively; while the crystalline silica content from using THF processing was 30.9 and 21.5% lower than that from using muffle furnace ashing, respectively. Since most full-shift samples from field assessments in this industry are expected to have respirable dust loading less than 0.5 mg, muffle furnace or radio frequency plasma ashing should be specified as the preferred sample processing method instead of the THF processing method for quantification of crystalline silica when engineered stone is expected to present to avoid artificially reduced silica content values, which are likely caused by the reactions between THF and the resins in engineered stone.
Public, Environmental & Occupational Health↗
Data from a four-day long microcosm experiment addressing the destabilization of artificial mineral-associated organic matter by model root exudates embedded in a soil matrix from the Rocky Mountain Biological Laboratory (Gothic, CO, USA), 2019
This dataset provides data collected during a four-day long laboratory soil microcosm experiment testing the efficacy of root exudate-driven mineral-associated organic matter destabilization. This dataset contains four data files in comma-separate values (*.csv). The files provide the metadata and the experimental results on microbial respiration, MAOM-derived respiration, and sequential mineral-extractions. This data was used to produce the figures in Bölscher et al., 2026. The results of the experiment can be found in the open access article Bölscher et al., 2026 (https://doi.org/10.1016/j.soilbio.2026.110276). Abstract: Mineral-associated organic matter (MAOM) is often considered stable, but root exudates can destabilize MAOM via various pathways. Theory and model system studies suggest that direct MAOM destabilization by strong ligands, like oxalic acid, or reducing agents, like catechol, is more effective than indirect, microbial-mediated MAOM destabilization, stimulated by less reactive compounds like glucose. Here, we demonstrate that the presence of a soil matrix alters the efficacy of exudate-driven MAOM destabilization pathways. Glucose and catechol destabilized significantly greater amounts of MAOM from ferrihydrite and aluminum hydroxide (Al (OH)3) embedded in a soil matrix than oxalic acid. Our findings indicate that indirect, microbial-mediated MAOM destabilization may play a larger role than direct MAOM destabilization in soil environments.
Destabilization↗
Maturation of Rhodobacter capsulatus Multicopper Oxidase CutO Depends on the CopA Copper Efflux Pathway and Requires the cutF Product (Final Report)
The goal is to achieve a comprehensive understanding of the nature, mechanism of function and biogenesis of c-type cytochromes and their oligomeric complexes. These cytochrome are ubiquitous heme proteins that are required for cellular bioenergetics, including photosynthesis (Ps) and respiration (Res), which are central to chemical energy (ATP) production by oxidative- and photophosphorylation. We contribute to the molecular characterization of individual and membrane-integral cytochromes as parts of Ps and Res electron transport (ET) pathways, using the facultative phototrophic bacteria of as excellent models for multidisciplinary investigations. Supported by this grant we discovered novel membrane-bound cytochromes and cyts c complexes that are important for Ps- and Res-ET. We worked out their biogenesis including maturation (covalent heme addition) and assembly of mature subunits into complexes. These involve fundamental process found virtually in all organisms and are fundamental processes with major significance to the “Photosynthetic Systems” program of DOE-BES, because the cytochromes are integral components of natural energy production systems. Elucidation of basic principles, and the knowledge gained by their study greatly impact on our ability to design, engineer, and create new natural, bio-hybrid, bio-inspired and artificial systems to better capture light, transduce and store its energy with improved efficacy and are of broad significance.
14 SOLAR ENERGY↗
Linear two-pool models are insufficient to infer soil organic matter decomposition temperature sensitivity from incubations
Abstract Terrestrial carbon (C)-climate feedbacks depend strongly on how soil organic matter (SOM) decomposition responds to temperature. This dependency is often represented in land models by the parameter Q 10 , which quantifies the relative increase of microbial soil respiration per 10 °C temperature increase. Many studies have conducted paired laboratory soil incubations and inferred “active” and “slow” pool Q 10 values by fitting linear two-pool models to measured respiration time series. Using a recently published incubation study (Qin et al. in Sci Adv 5(7):eaau1218, 2019) as an example, here we first show that the very high parametric equifinality of the linear two-pool models may render such incubation-based Q 10 estimates unreliable. In particular, we show that, accompanied by the uncertain initial active pool size, the slow pool Q 10 can span a very wide range, including values as high as 100, although all parameter combinations are producing almost equally good model fit with respect to the observations. This result is robust whether or not interactions between the active and slow pools are considered (typically these interactions are not considered when interpreting incubation data, but are part of the predictive soil carbon models). This very large parametric equifinality in the context of interpreting incubation data is consistent with the poor temporal extrapolation capability of linear multi-pool models identified in recent studies. Next, using a microbe-explicit SOM model (RESOM), we show that the inferred two pools and their associated parameters (e.g., Q 10 ) could be artificial constructs and are therefore unreliable concepts for integration into predictive models. We finally discuss uncertainties in applying linear two-pool (or more generally multiple-pool) models to estimate SOM decomposition parameters such as temperature sensitivities from laboratory incubations. We also propose new observations and model structures that could enable better process understanding and more robust predictive capabilities of soil carbon dynamics.
54 ENVIRONMENTAL SCIENCES↗
Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.
54 ENVIRONMENTAL SCIENCES↗
Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)
This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland. The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).
54 ENVIRONMENTAL SCIENCES↗