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Record W2752395902 · doi:10.3390/ma10091006

Characterization and Applications of Kaolinite Robustly Grafted by an Ionic Liquid with Naphthyl Functionality

2017· article· en· W2752395902 on OpenAlexaff
Gustave Kenne Dedzo, Christian Detellier

Bibliographic record

VenueMaterials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCharacterization (materials science)KaoliniteIonic liquidMaterials scienceChemical engineeringPolymer chemistryNanotechnologyChemistryOrganic chemistryEngineeringCatalysisMetallurgy

Abstract

fetched live from OpenAlex

Functionalization of the kaolinite (K) interlayer space is challenging. In this work, a new kaolinite-based nanohybridmaterial (K-NI) was successfully synthesized by grafting on the interlayer aluminol surfaces the ionic liquid, 1-(1-methylnaphthyl)-3-(2-hydroxyethyl) imidazolium chloride (NI), using a guest displacement strategy. A substantial increase of the basal spacing (10.8 Å) was obtained. This is a grafted derivative of kaolinite possessing one of the largest d-values. Washing in water for several days and other vigorous treatments such as sonication showed a minor effect on the integrity of the material. FTIR and 13C NMR confirmed the conservation of the structure of the ionic liquid after the grafting. Thermal analysis confirmed the presence of grafted material and was used to estimate the abundance of the grafted ionic liquid (0.44 mole per mole of kaolinite structural formula, (Al2Si2O5(OH)4)). By using cyclic voltammetry, the permeability of a film of K-NI for the bulky ferricyanide ions was demonstrated. The accumulation of nitrophenolate anions was effective (maximum capacity of 190 μmol/g), but was less important than what was expected due to the steric hindrance of the bulky grafted NI. Although the presence of chloride anions reduced the adsorption capacity, the affinity of the modified kaolinite interlayer space for the nitrophenolate anions was demonstrated.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.958

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.275
Teacher spread0.254 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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