Organic buffers act as reductants of abiotic and biogenic manganese oxides
Bibliographic record
Abstract
Abstract Proton activity is the master variable in many biogeochemical reactions. To control pH, laboratory studies involving redox-sensitive minerals like manganese (Mn) oxides frequently use organic buffers (typically Good’s buffers); however, two Good’s buffers, HEPES and MES, have been shown to reduce Mn(IV) to Mn(III). Because Mn(III) strongly controls mineral reactivity, avoiding experimental artefacts that increase Mn(III) content is critical to avoid confounding results. Here, we quantified the extent of Mn reduction upon reaction between Mn oxides and several Good’s buffers (MES, pK a = 6.10; PIPES, pK a = 6.76; MOPS, pK a = 7.28; HEPES, pK a = 7.48) and TRIS (pK a = 8.1) buffer. For δ-MnO 2 , Mn reduction was rapid, with up to 35% solid-phase Mn(III) generated within 1 h of reaction with Good’s buffers; aqueous Mn was minimal in all Good’s buffers experiments except those where pH was one unit below the buffer pK a and the reaction proceeded for 24 h. Additionally, the extent of Mn reduction after 24 h increased in the order MES < MOPS < PIPES < HEPES << TRIS. Of the variables tested, the initial Mn(II,III) content had the greatest effect on susceptibility to reduction, such that Mn reduction scaled inversely with the initial average oxidation number (AMON) of the oxide. For biogenic Mn oxides, which consist of a mixture of Mn oxides, bacterial cells and extracelluar polymeric substances, the extent of Mn reduction was lower than predicted from experiments using abiotic analogs and may result from biotic re-oxidation of reduced Mn or a difference in the reducibility of abiotic versus biogenic oxides. The results from this study show that organic buffers, including morpholinic and piperazinic Good’s buffers and TRIS, should be avoided for pH control in Mn oxide systems due to their ability to transfer electrons to Mn, which modifies the composition and reactivity of these redox-active minerals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 teacher head, 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".