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Record W2022260036 · doi:10.1021/es8020099

Photocatalytic Oxidation of Paracetamol: Dominant Reactants, Intermediates, and Reaction Mechanisms

2008· article· en· W2022260036 on OpenAlexaff
Liming Yang, Liya E. Yu, Madhumita B. Ray

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsWestern University
FundersNational University of Singapore
KeywordsChemistryHydroxylationMineralization (soil science)PhotocatalysisCatalysisRadicalNitrateReaction intermediateReaction rate constantAmmoniumReactive intermediateMedicinal chemistryInorganic chemistryPhotochemistryNitrogenOrganic chemistryKineticsEnzyme

Abstract

fetched live from OpenAlex

The role of primary active species (ecb(-), hvb(+), *OH, HO2*, O2*(-), and H2O2) during photocatalytic degradation of paracetamol (acetaminophen) using TiO2 catalyst was systematically investigated. Hydroxyl radicals (*OH) are responsible for the major degradation of paracetamol with a second-order rate constant (1.7 x 10(9) M(-1) s(-1)) for an *OH-paracetamol reaction. A total of 13 intermediates was identified and classified into four categories: (i) aromatic compounds, (ii) carboxylic acids, (iii) nitrogen-containing straight chain compounds, and (iv) inorganic species (ammonium and nitrate ions). Concentration profiles of identified intermediates indicate that paracetamol initially undergoes hydroxylation through *OH addition onto the aromatic ring at ortho (predominantly), meta, and para positions with respect to the -OH position of paracetamol. This initial *OH hydroxylation is followed by further oxidation generating carboxylic acids. Subsequent mineralization of smaller intermediates eventually increases ammonium and nitrate concentration in the system.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.134
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.239
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations238
Published2008
Admission routes1
Has abstractyes

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