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Record W4409569509 · doi:10.1680/jenes.24.00157

Adsorption behaviour of modified fish scales as an emerging fluoride adsorbent

2025· article· en· W4409569509 on OpenAlexvenueno aff
Vishal Chaudhari, Manish Patkar

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionFluorideFish <Actinopterygii>Environmental chemistryChemical engineeringChemistryEnvironmental scienceInorganic chemistryFisheryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

This study explores the use of acid-activated, thermally treated Tilapia fish scales as an eco-friendly and cost-effective adsorbent to remove fluoride from water. The chemical structure, surface properties, and microstructure of the adsorbent were analysed using the Fourier transform infrared spectroscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy, X-ray diffraction, Brunauer–Emmett–Teller, and Barrett–Joyner–Halenda methods. Optimal fluoride adsorption was achieved at pH 6, 6 g/l adsorbent dose, 240 min contact time, and 10 mg/l fluoride concentration. The Freundlich model (R 2 = 0.9274) described multilayer adsorption, and the pseudo-second-order model (R 2 = 0.9921) provided a comprehensive insight into F − adsorption kinetics. The thermodynamic examination confirmed that the adsorption rate of F − on modified fish scale exhibited exothermic behaviour, driven mainly by the physical interaction mode. In the context of regeneration studies, it was discovered that the adsorbent exhibited the potential for reusability in fluoride sorption for up to five cycles.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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