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Record W4242908252 · doi:10.26434/chemrxiv.7730654

Singlet Oxygen Generation in Classical Fenton Chemistry

2019· preprint· en· W4242908252 on OpenAlexaff
Andrew Carrier, Saher Hamid, David A. Oakley, Ken D. Oakes, Xu Zhang

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsCape Breton University
Fundersnot available
KeywordsHydrogen peroxideReactive oxygen speciesSinglet oxygenChemistryReagentCatalysisOxygenPhotochemistryHydroxyl radicalRadicalReactive intermediateBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The Fenton reaction, the Fe-catalyzed conversion of hydrogen peroxide to reactive oxygen species (ROS) was discovered more than a century ago. It occurs widely in nature because of the ubiquity of Fenton reagents, i.e., Fe and H2O2, and ROS in environmental and biological systems; however, its mechanisms and the identity of the ROS generated under varying conditions have remained controversial. The widely accepted mechanism is that of successive oxidation and reduction of Fe2+ and Fe3+ by hydrogen peroxide to form ·OH and O2-·, respectively, where ·OH is implicated as the primary oxidant. However, the formation of high-valent Fe4+=O species has also been implicated. Herein, by systematically dissecting the contributions of various ROS species generated in the classical Fenton reaction by using specific ROS traps and scavengers, we identified that singlet oxygen (1O2) is the main ROS from pH 4–7. In contrast, although ·OH is produced in measurable quantities, it was not a major contributor to the oxidation of organic molecules.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.250
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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