Hydrogen peroxide decomposition in bicarbonate solution catalyzed by ferric citrate<sup>*</sup>This article has a companion paper in this issue (doi: 10.1139/v11-080).
Bibliographic record
Abstract
The peroxymonocarbonate mono- and di-anions (HCO4– and CO42–) are known to be generated from H2O2/HCO3–. They are promising oxidants for wood pulp bleaching, but peroxide decomposition catalyzed by ferric complexes can be significant for pulps whose lignin is highly reactive. Dicarboxylates from lignin peroxidation are believed to be the ferric chelators in the pH 8.5 range that is optimum for H2O2/HCO3–. This investigation aimed to see if HCO3– addition caused destabilization of the peroxygen system owing to its partial conversion to HCO4–. This anionic peracid is a much stronger oxidant than H2O2 and could lead to a higher rate of Fe(II) oxidation to Fe(III) and (or) Fe(IV). For most free radical chain mechanisms, an increase in Fe(II) oxidation results in a higher rate of peroxide decomposition. Based on the kinetic data that were obtained and theoretical analyses, it was concluded that HCO4– did not significantly destabilize the peroxygen system when citrate was used as a model chelator for Fe(III). Increasing the [HCO3–] fourfold from 0.025 to 0.10 mol/L caused the decomposition rate to increase by only 20%.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".