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Record W4404906548 · doi:10.1038/s41467-024-53945-4

Cationic covalent organic framework for the fluorescent sensing and cooperative adsorption of perfluorooctanoic acid

2024· article· en· W4404906548 on OpenAlexfundno aff
Asmaa Jrad, Gobinda Das, Nour Alkhatib, Thirumurugan Prakasam, Farah Benyettou, Sabu Varghese, Felipe Gándara, Mark A. Olson, Serdal Kırmızıaltın, Ali Trabolsi

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsnot available
FundersTamkeenYork UniversityNew York University Abu Dhabi
KeywordsPerfluorooctanoic acidAdsorptionCationic polymerizationCovalent organic frameworkCovalent bondChemistryContaminationEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The contamination of water by per- and polyfluorinated substances (PFAS) is a pressing global issue due to their harmful effects on health and the environment. This study explores a cationic covalent organic framework (COF), TG-PD COF, for the efficient detection and removal of perfluorooctanoic acid (PFOA) from water. Synthesized via a simple sonochemical method, TG-PD COF shows remarkable selectivity and sensitivity to PFOA, with a detection limit as low as 1.8 µg·L⁻¹. It achieves significant PFOA adsorption exceeding 2600 mg·g⁻¹ within seconds over several cycles in batch mode and complete removal at environmentally relevant concentrations in column adsorption. Results reveal unique adsorption behavior characterized by two phases, leveraging PFOA aggregation through hydrophobic interactions. Computer simulations elucidate the mechanisms underlying TG-PD COF’s sensing, adsorption, and charge transfer dynamics. Our findings position this COF design strategy as a promising solution for combating PFAS contamination in water bodies worldwide. The contamination of water by per- and polyfluorinated substances is a pressing global issue. Here, the authors explore a cationic covalent-organic framework for efficient detection and removal of perfluorooctanoic acid (PFOA) from water.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.293

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 science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.329
Teacher spread0.304 · 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 designNot applicable
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

Citations125
Published2024
Admission routes1
Has abstractyes

Explore more

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