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Record W4416310014 · doi:10.1002/ep.70203

Otolith‐derived nanocomposite for the removal of tetrazine from water: thermodynamic, kinetic, and isotherm studies

2025· article· en· W4416310014 on OpenAlexaff
James F. Amaku, Innocent Kanayo Ugwuanyi, Okoche Kelvin Amadi, Fanyana M. Mtunzi, Jesse Greener

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAdsorptionSorbentSorptionAqueous solutionFreundlich equationLangmuirNanocompositeActivated carbon

Abstract

fetched live from OpenAlex

Abstract The performance of the multi‐walled carbon nanotubes (MWCNTs)–calcined otoliths (OLT) composite (MTO) in removing tetrazine (Tatz) from aqueous solution was evaluated and compared with that of calcined otoliths (OLT) alone. To find the ideal sorption conditions for Tatz removal, the effects of key variables, including pH, contact time, adsorbent amount, initial Tatz concentration, and adsorbate temperature, were investigated through batch adsorption trials. The uptake of Tatz by OLT and MTO was dependent on the aforementioned factors of adsorption. The pseudo‐second‐order model provided the best fit for the kinetic data of OLT and MTO, reflecting a chemisorptive mechanism involving two molecular interactions between Tatz and the active binding sites. The Langmuir and the Freundlich models best described the equilibrium data obtained for both OLT and MTO, respectively. Therefore, MTO exhibited a higher removal efficiency for Tatz, with an adsorption capacity of 58.75 mg g −1 , compared to 19.53 mg g −1 for OLT. Therefore, using MTO as a possible sorbent for wastewater and effluent treatment is doable and ought to be investigated further to reduce water pollution.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.645

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.225
Teacher spread0.220 · 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

Explore more

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