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Record W2806120682 · doi:10.5004/dwt.2018.22301

Elimination of pharmaceutical contaminants fluoxetine and propranolol by an advanced plasma water treatment

2018· article· en· W2806120682 on OpenAlexaff
Amirreza Sohrabi, Ghazaleh Haghighat, Parmiss Mojir Shaibani, Charles William Van Neste, Selvaraj Naicker, Mohtada Sadrzadeh, Thomas Thundat

Bibliographic record

VenueDesalination and Water Treatment · 2018
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluoxetineContaminationPropranololEnvironmental chemistryChemistryEnvironmental scienceMedicineInternal medicineBiology

Abstract

fetched live from OpenAlex

ABSTRACT Human activities have contaminated water sources with pharmaceutical compounds by the improper disposal of unwanted medicine or through sewage waste. The search for the most effective water treatment processes has been ongoing for decades. In the current paper, by exposing water to non-thermal plasma in a floating electrode streamer corona discharge (FESCD) system, both the antidepressant compound fluoxetine and the antihypertensive compound propranolol are eliminated. After 3 h of plasma treatment, more than 99% of each contaminant was degraded. The energy yield, which is the amount of contaminants degraded using 1 kWh of energy, was in the range of 0.12–0.13 g/kWh. The degree of mineralization calculated from total organic carbon (TOC) measurements was 60% and 17% for fluoxetine and propranolol, respectively. Reaction with hydroxyl radicals was the only degradation pathway for fluoxetine and its byproducts. For propranolol, hydroxyl radicals primarily caused the degradation butoxidation of secondary alcohols to ketones suggested the possible role of ozone 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 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 score0.036
Threshold uncertainty score0.490

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 head0.019
GPT teacher head0.293
Teacher spread0.273 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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