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149 records · Page 9

Impacts of sulfonic acids on fungal manganese oxide production

Microbial manganese (Mn) oxidation plays a critical role in Mn(III/IV) oxide formation in modern day environmental systems. These oxidation processes and resulting biominerals are sensitive to aqueous conditions, such as pH and dissolved constituent concentrations. With Mn and sulfur (S) biogeochemical cycling closely linked in many environmental systems, and dissolved organic sulfur comprising a substantial pool of total sulfur in several environments, the impact of dissolved organic sulfur compounds on Mn redox processes is important to consider. Sulfonic acids, environmentally ubiquitous organosulfur compounds, play substantial roles in S cycling in many natural and contaminated systems. Research to assess the effects of these abundant sulfonic acids on Mn biogeochemical cycling, microbial Mn oxidation processes, and Mn biominerals is needed for understanding and predicting the impact of coupled S and Mn biogeochemical cycles, particularly in environments with dynamic redox gradients or in anthropogenically contaminated systems. Further, with research on microbial and abiotic Mn oxidation processes often using aminosulfonic acids to control pH, understanding the impact of such sulfonic acids on microbial Mn oxidation processes is critical. Several recent studies found that commonly used zwitterionic N substituted aminosulfonic acids, known as Good’s buffers, such as HEPES and MES, can alter abiotic birnessite sheet structures. Here we investigate the impact of two sulfonic acids with broad applications to natural and contaminated sites as well as laboratory settings (HEPES and MES) on fungal Mn oxidation relative to a carbonate buffer and a buffer-free control by three Ascomycete fungi known to oxidize Mn(II): Stagonospora sp. SRC1lsM3a, Paraphaeosphaeria sporulosa AP3s5–JAC2a, and Plectosphaerella cucumerina DS2psM2a2. Structural analyses of the products show that sulfonic acids promote Mn oxidation by P. cucumerina, producing hexagonally symmetric phyllomanganates analogous to hexagonal birnessite or c–disordered H + birnessite [(Ca,Na,K)(Mn 4+ ,Mn 3+ ,$\square$)O 2 nH 2 O], with solid–associated Mn(II) bound to vacancy sites and biomass, while in their absence almost all Mn remains as either aqueous Mn(II) or solid–associated Mn(II) bound to biomass. In contrast, sulfonic acids exert the opposite effect on Mn oxidation by P. sporulosa, with their presence suppressing Mn(II) oxidation to Mn(IV), likely leading to the formation of mycogenic bixbyite (Mn 3+ 2 O 3 ) while the buffer–free control forms a poorly crystalline phyllo- or tectomanganate. Meanwhile, all treatments exert a minimal effect on Mn(II) uptake from solution and Mn oxidation with Stagonospora sp., with all experimental systems and controls forming poorly crystalline, hexagonally symmetric phyllomanganates. The fact that the sulfonic acids here studied exert similar effects on Mn oxidation, but substantially different effects for each fungus, suggests they affect Mn oxidation via mechanistically similar pathways that are likely dependent on interactions with fungal exudates (which vary from species to species) or specific fungal Mn oxidation processes. Interestingly, for all fungi, MES increases Mn(III) in the resulting biominerals, while the carbonate buffer consistently decreases Mn(III). Further, these results clearly demonstrate that sulfonic acids not only alter Mn oxide structures, as has been previously noted in abiotic studies, but can interfere with Mn oxidation reactions themselves, highlighting the incredible sensitivity of both Mn oxide structures and the Mn oxidation process to the aqueous environment.

58 GEOSCIENCES↗

Data, figures, animations, and scripts associated with the manuscript "Integrated Effects of Site Hydrology and Vegetation on Exchange Fluxes and Carbon Cycling at the Coastal Terrestrial Aquatic Interface"

This package contains the data, figures, animations, and scripts used in "Integrated Effects of Site Hydrology and Vegetation on Exchange Fluxes and Carbon Cycling at the Coastal Terrestrial Aquatic Interface" (Li et al., 2023). This study examines the interactions between soil, vegetation and hydrologic conditions in coastal areas, with a focus on the Chesapeake Bay region. The researchers used a mechanistic model called ATS-PFLOTRAN to explore how these interactions affect exchange process in different regions and the carbon/nitrogen cycle across the terrestrial-aquatic interface (TAI). Simulation scenarios isolate the effects of control factors and reaction constants derived from laboratory experiments. The results show a carbon cycle "hot zone" in coastal wetlands and in the transition zone between wetlands and uplands. Transpiration enhances fluxes between the surface and subsurface domains and increases dissolved oxygen in TAI. The decomposition of leaf-derived organic carbon provides an additional source of carbon for aerobic respiration and denitrification in the TAI. Microbial activity plays a key role in controlling redox conditions and their variability. This modeling study improves the understanding of complex TAI interactions and facilitates the representation of coastal ecosystems in larger-scale Earth system models.Several files can be found from this data package.1. Readme,md: This file describe the Title, Target Journal, Target submission date, Co-author, Science Questions, Hypotheses, Key words, Key message, Model and Data, Repo Structure. The user can read this file first and then go to details.2. mesh.zip: This file contains the mesh file for simulation cases. 3. Figure.zip: This file contains figures used in the manuscript . 4. animation.zip: This file contains animations used in the manuscript . 5. Data.zip: This file contains all required input data, such as concentration and flow boundaries. Also, it contains the DEM and processes results on area fraction. 6. Simulation_setup_and_results.zip: This file contains setup of cases used in the manuscript . The result files are too large, if you need that data, please contact to the Author. 7. notebooks.zip: This file contains the Jupyter notebooks for performing sensitivity analysis and other pre-process and post-process analyses.

54 ENVIRONMENTAL SCIENCES↗

Bacterial nitrite production oxidizes Fe(II) bioremediating acidic abandoned coal mine drainage

Passive remediation systems (PRSs) treating either acidic or neutral abandoned coal mine drainage (AMD) are colonized by bacteria that can bioremediate iron (Fe) through chemical cycling. Due to the low pH in acidic AMD, iron oxidation from soluble Fe(II) to precipitated Fe(III) is mainly directed by microbial oxidation. Less well described are biotic reactions that lead to iron remediation through abiotic secondary reactions. We describe here iron oxidation in acidic AMD that is mediated by the bacterial reduction of nitrate to nitrite followed by the geochemical oxidation of Fe(II). Within an acidic PRS, 4,560 bacteria cultured from the microbial community were screened for their ability to oxidize iron and to perform nitrate-dependent iron oxidation (NDFO). Iron oxidation in the culturable community was observed in every pond of the system, ranging from 2.1% to 11.4%, and NDFO was observed in every pond, ranging from 1.4% to 6.0% of the culturable bacteria. Five NDFO isolates were purified and identified as Paraburkholderia spp. One of our isolates, Paraburkholderia sp. AV18 was shown to drive NDFO through the bacterial production of nitrite that in turn chemically oxidizes Fe(II) (nitrate reduction-iron oxidation; NRIO). AV18 expressed nitrate reductase, napA, concurrent to nitrite production. Burkholderiales are found by 16S rRNA gene sequencing in every pond of the PRS. The frequency of NDFO metabolism in the culturable microbial community and abundance of Burkholderiales in the PRS suggest nitrite producers contribute to the bioremediation of iron in acidic AMD and may be an unharnessed opportunity to increase iron bioremediation in acidic conditions.

(NDFO)↗

Bioproduction of cerium-bearing magnetite and application to improve carbon-black supported platinum catalysts

Biogeochemical processing of metals including the fabrication of novel nanomaterials from metal contaminated waste streams by microbial cells is an area of intense interest in the environmental sciences. Here we focus on the fate of Ce during the microbial reduction of a suite of Ce-bearing ferrihydrites with between 0.2 and 4.2 mol% Ce. Cerium K-edge X-ray absorption near edge structure (XANES) analyses showed that trivalent and tetravalent cerium co-existed, with a higher proportion of tetravalent cerium observed with increasing Ce-bearing of the ferrihydrite. The subsurface metal-reducing bacterium Geobacter sulfurreducens was used to bioreduce Ce-bearing ferrihydrite, and with 0.2 mol% and 0.5 mol% Ce, an Fe(II)-bearing mineral, magnetite (Fe(II)(III) 2 O 4 ), formed alongside a small amount of goethite (FeOOH). At higher Ce-doping (1.4 mol% and 4.2 mol%) Fe(III) bioreduction was inhibited and goethite dominated the final products. During microbial Fe(III) reduction Ce was not released to solution, suggesting Ce remained associated with the Fe minerals during redox cycling, even at high Ce loadings. In addition, Fe L 2,3 X-ray magnetic circular dichroism (XMCD) analyses suggested that Ce partially incorporated into the Fe(III) crystallographic sites in the magnetite. The use of Ce-bearing biomagnetite prepared in this study was tested for hydrogen fuel cell catalyst applications. Platinum/carbon black electrodes were fabricated, containing 10% biomagnetite with 0.2 mol% Ce in the catalyst. The addition of bioreduced Ce-magnetite improved the electrode durability when compared to a normal Pt/CB catalyst. Different concentrations of Ce can inhibit the bioreduction of Fe(III) minerals, resulting in the formation of different bioreduction products. Bioprocessing of Fe-minerals to form Ce-containing magnetite (potentially from waste sources) offers a sustainable route to the production of fuel cell catalysts with improved performance.

59 BASIC BIOLOGICAL SCIENCES↗

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results

This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

54 ENVIRONMENTAL SCIENCES↗