Hydrogen peroxide decomposition in bicarbonate solution catalyzed by divalent manganese species<sup>*</sup>This article has a companion paper in this issue (doi: 10.1139/v11-078).
Bibliographic record
Abstract
The peroxymonocarbonate mono- and di-anions (HCO 4 – and CO 4 2– ) are known to be generated from H 2 O 2 /HCO 3 – . They are promising oxidants for wood pulp bleaching, but peroxide decomposition catalyzed by Mn(II) species may be significant for pulp samples with unusually high Mn contents. This investigation aimed to see if HCO 3 – addition caused destabilization of the peroxygen system owing to its partial conversion to HCO 4 – . This anionic peracid is a much stronger oxidant than H 2 O 2 and could lead to a higher rate of Mn(II) oxidation to Mn(III) and (or) Mn(IV). For most free radical chain mechanisms, an increase in Mn(II) oxidation results in a higher rate of peroxide decomposition. Peroxide decomposition catalyzed by Mn(II) was investigated in H 2 O 2 /HCO 3 in the pH ranges 8.5–8.7 and 7.4–7.9. The rate equation for peroxide decomposition was first order in [H 2 O 2 ] and [Mn(II)] in both pH ranges, but close to second order in [HCO 3 – ] in the higher pH range and close to third order in the lower pH range. Free radical chain mechanisms were proposed for both pH ranges and with all the correct reaction orders. Contrary to mechanisms previously proposed, it was concluded that HCO 4 – is the principal oxidizer of Mn(II) in the pH 7.4–7.9 range.
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 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 | 0.000 | 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) | 0.024 | 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".