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Record W2911836134 · doi:10.1139/cjc-2018-0247

Facile synthesis of EDTA-functionalized halloysite nanotubes for the removal of methylene blue from aqueous phase

2019· article· en· W2911836134 on OpenAlexvenueno aff
Mengmeng Zhang, Hongchao Yu, Bibo Chen

Bibliographic record

VenueCanadian Journal of Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
FundersEast China Institute of Technology
KeywordsChemistryAdsorptionMethylene blueHalloysiteThermogravimetric analysisAqueous solutionLangmuirFreundlich equationLangmuir adsorption modelNuclear chemistryFourier transform infrared spectroscopyInorganic chemistryOrganic chemistryChemical engineeringCatalysisPhotocatalysis

Abstract

fetched live from OpenAlex

EDTA-functionalized halloysite nanotubes (EDTA-HNTs) were synthesized by a two-step method. The structures of the EDTA-HNTs were analyzed by Fourier-transform infrared spectra, X-ray diffraction, thermogravimetric analysis, and transmission electron spectroscopy. Then, the EDTA-HNTs were applied as adsorbents for the removal of methylene blue from aqueous phase. Adsorption of methylene blue onto EDTA-HNTs was investigated with respect to adsorbent dose, contact time, initial methylene blue concentration, and temperature. A maximum adsorption capacity of 115.8 mg/g for methylene blue onto EDTA-HNTs at room temperature was achieved. The adsorption kinetics could be described by the pseudo-second-order model. Both the Langmuir and Freundlich models were employed to describe the adsorption isotherms, and the results presented that the equilibrium data obeyed the Langmuir model. Thermodynamic parameters of ΔG 0 and ΔH 0 verified the spontaneous and exothermic nature of the methylene blue adsorption onto EDTA-HNTs. Moreover, the EDTA-HNTs could be facilely regenerated and efficiently reused, exhibiting a good prospect for the treatment of dye effluents.

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 categoriesInsufficient payload (model declined to judge)
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 score0.995

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)0.0060.000

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 head0.016
GPT teacher head0.254
Teacher spread0.237 · 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.

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

Citations8
Published2019
Admission routes1
Has abstractyes

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