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Record W4327884627 · doi:10.52547/jcc.4.4.7

Carbon-based composites for removal of pharmaceutical components from water

2022· article· en· W4327884627 on OpenAlex
Saeed Bahadorikhalili, Fariborz Sharifianjazi, Maryam Azimpour, Neda Min Bashi, Aliasghar Abuchenari, Mahsa Borzouyan Dastjerdi, Zahra Hashemian, Masoud Soroush Bathaei, Mahnoosh Fatemi, Mahsa Hojjati, Amirhossein Esmaeilkhanian, Elahe Ahmadi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Composites and Compounds · 2022
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsActivated carbonGrapheneCarbon fibersAdsorptionMaterials scienceCarbon nanotubeWater treatmentNanotechnologyComposite materialChemical engineeringComposite numberChemistryOrganic chemistryEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Carbon-based materials. including carbon nanotubes, graphene, and activated carbon, are among the most effective materials for pharmaceutical components removal from water. Despite the severe effect of pharmaceutical micropollutants in the aquatic environments and the effectiveness of carbon-based composites for water treatment, only a few studies has reviewed carbon-based materials for the removal of pharmaceutical components. Carbon-based materials with special properties like tunable surface functions, abundant pore structure, and high specific surface are used in different water treatment mechanisms such as adsorption and advanced oxidation processes. Graphene, activated carbon, and carbon nanotubes have been widely studied for pharmaceutical components removal. Herein, we have introduced carbon-based materials and reviewed recent studies on their properties, application in water treatment, and possible mechanism for removal of pharmaceutical components from aquatic environments.

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.

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 score0.038
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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