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Record W3114241847 · doi:10.1002/slct.202004147

A Kinetic Study on the Degradation of Acetaminophen and Amoxicillin in Water by Ultrasound

2020· article· en· W3114241847 on OpenAlexafffund
M. Stucchi, Marco G. Rigamonti, Davide Carnevali, Daria C. Boffito

Bibliographic record

VenueChemistrySelect · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsPolytechnique Montréal
FundersCanada Research Chairs
KeywordsSonicationHydroquinoneAcetaminophenUltrasoundDegradation (telecommunications)ChemistryAmoxicillinKineticsYield (engineering)MoleculeNuclear chemistryChromatographyOrganic chemistryMaterials scienceBiochemistryMetallurgyAntibiotics

Abstract

fetched live from OpenAlex

Abstract This paper presents a study of the conversion pathway of acetaminophen (APAP) and amoxicillin (AMO) in water by ultrasound as a single degradation method. Results showed that the sonication power output, continuous vs. pulsed ultrasound and starting concentration, result in different conversion pathways, as well as the simultaneous presence of the two molecules reduces the degradation yield. Hydroquinone and hydroxyl‐hydroquinone were detected as acetaminophen by‐products, while amoxicillin degraded into four main products. Accordingly, we proposed a conversion pathway mechanism and we finally regressed the amoxicillin conversion kinetics as a function of the ultrasonic power. APAP was more resistant to continuous sonication either at low (25 ppm) or high (100 ppm) starting concentration. On the contrary, ultrasound was able to convert AMO up to 56 % starting from a concentration of 25 ppm. We ascribed this behavior to the presence of a higher number of hydroxyl groups present in the molecule compared to APAP. Moreover, when ultrasound were applied in pulses, it resulted in an energy saving of more than 50 % for a 2 % lower AMO conversion.

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 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 score0.004
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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.

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

Citations11
Published2020
Admission routes2
Has abstractyes

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