Electron Affinity as a Design Principle for Oxidative Catalysts Based on Birnessite
Bibliographic record
Abstract
Birnessite is a layered manganese oxide that acts as a strong oxidant in water treatment, but the controlling electronic factor is not clear. We tested twelve natural and synthetic birnessites with different interlayer cations and Mn(III)/Mn(IV) ratios to evaluate whether electron affinity (EA) governs oxidation. EA measured by ultraviolet photoelectron spectroscopy and checked by density functional theory ranged from 5.56 to 6.08 eV. Oxidation of Fe(II) and As(III) at pH 7.0 and 25 °C followed a pseudo-second-order model, with k 2 increasing from 77.5×10 −4 to3.4×10 −3 g mg⁻¹ min⁻¹ for Fe(II) and from 4.1×10 −4 to2.1×10 −3 g mg⁻¹ min⁻¹ for As(III); an exponential k2k_2k2-EA fit gave R 2 =0.92. The apparent activation energy fell from 48±3to29±2 kJ mol⁻¹ as EA increased. After five cycles, high-EA samples retained 82-88% of their initial activity, while low-EA samples retained 52-60%, consistent with less MnOOH formation. These results show that electron affinity is a quantitative descriptor of oxidizing strength in birnessite and provide a basis for tuning interlayer chemistry and hydration to design durable manganese-oxide catalysts for pollutant removal.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".