Chemical reactivity of microbe and mineral surfaces in hydrous ferric oxide depositing hydrothermal springs
Bibliographic record
Abstract
ABSTRACT The hot springs in Yellowstone National Park, USA, provide concentrated microbial biomass and associated mineral crusts from which surface functional group (FG) concentrations and pKa distributions can be determined. To evaluate the importance of substratum surface reactivity for solute adsorption in a natural setting, samples of iron‐rich sediment were collected from three different springs; two of the springs were acid‐sulfate‐chloride (ASC) in composition, while the third was neutral‐chloride (NC). At one of the ASC springs, mats of Sº‐rich Hydrogenobaculum‐like streamers and green Cyanidia algae were also collected for comparison to the sediment. All samples were then titrated over a pH range of 3–11, and comparisons were made between the overall FG availability and the concentration of solutes bound to the samples under natural conditions. Sediments from ASC springs were composed of hydrous ferric oxides (HFO) that displayed surface FGs typical of synthetic HFO, while sediments from the NC spring were characterized by a lower functional group density, reflected by decreased excess charge over the titration range (i.e., lower surface reactivity). The latter also showed a lower apparent point of zero charge (PZC), likely due the presence of silica (up to 78 wt. %) in association with HFO. Variations in the overall HFO surface charge are manifest in the quantities and types of solutes complexed; the NC sediments bound more cations, while the ASC sediments retained significantly more arsenic, presumably in the form of arsenate (H2AsO4−). When the microbial biomass samples were analyzed, FG concentrations summed over the titratable range were found to be an order of magnitude lower for the Sº‐rich mats, relative to the algal and HFO samples that displayed similar FG concentrations on a dry weight basis. A diffuse‐layer surface complexation model was employed to further illustrate the importance of surface chemical parameters on adsorption reactions in complex natural systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".