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Record W2328272207 · doi:10.1021/ie402658x

Characterization of Magnetic Carbon Nanotube–Cyclodextrin Composite and Its Adsorption of Dye

2014· article· en· W2328272207 on OpenAlexaff
Jing Cheng, Peter R. Chang, Pengwu Zheng, Xiaofei Ma

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsThermogravimetric analysisAdsorptionEndothermic processCarbon nanotubeLangmuir adsorption modelEnthalpyRaman spectroscopyMaterials scienceChemical engineeringMethylene blueTransmission electron microscopyNuclear chemistryChemistryInorganic chemistryNanotechnologyPhysical chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

β-Cyclodextrin (CD) grafted carbon nanotube (CNT) composites were prepared by the reduction of oxidized CNT with hydrazine hydrate in the presence of CD. The obtained reduced CNT-CD (RCNT-CD) was attached to iron oxide particles in preparation of magnetic RCNT-CD (M-RCNT-CD). These composites were characterized by Raman spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), and thermogravimetric analysis (TG). About 17 wt % CD was grafted onto RCNT, and about 23.5 wt % iron oxide particles were attached to the RCNT-CD. The kinetic adsorption fit the pseudo-second-order model, and the isotherm data followed the Langmuir model. The maximum adsorption capacity reached 196.5 mg/g for M-RCNT-CD, indicating that it is a good adsorbent for methylene blue. The positive free energy change (Δ G °) and negative enthalpy change (Δ H °) illustrated that the adsorption process was spontaneous and endothermic. M-RCNT-CD can be easily separated from solution in the magnetic field.

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)0.001
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 score0.015
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.034
GPT teacher head0.277
Teacher spread0.242 · 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

Citations56
Published2014
Admission routes1
Has abstractyes

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