Electronic Origin of the Oxidizing Properties of Birnessite Minerals
Bibliographic record
Abstract
Birnessite is a layered manganese oxide with strong oxidizing ability, but the electronic reason for this property is still unclear. In this work, eight birnessite samples with different interlayer cations and Mn(III)/Mn(IV) ratios were studied to explore how electron affinity (EA) affects oxidation behavior. The samples were examined by XRD, XPS, SEM and UPS, and oxidation tests were carried out with Fe(II) and phenol under controlled pH and temperature. The electron affinity values ranged from 5.52 to 5.93 eV, increasing from Na⁺- to K⁺-, Ca²⁺- and Mg²⁺-birnessite. Samples with higher EA showed faster oxidation, and the Fe(II) removal rate was about 2.1 times higher than that of low-EA samples. The relationship between EA and rate constant followed an exponential trend (R² = 0.96). Surface and spectral data showed that samples with higher EA had slower Mn(IV) loss and later surface passivation. These results show that electron affinity controls the oxidizing ability of birnessite and can be used to predict and improve the performance of manganese oxides in pollutant removal and catalytic oxidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".