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Record W2014426147 · doi:10.1115/mn2008-47038

Some Aspects on Thermal Degradation of Organo-Layer Silicates

2008· article· en· W2014426147 on OpenAlexfundno aff
Mervat S. Hassan, H. El‐Shall, Chearly Beaty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerUniversity of Florida
KeywordsMontmorilloniteMaterials scienceThermogravimetryOrganoclayThermal stabilityExfoliation jointIntercalation (chemistry)Chemical engineeringPolymerDifferential scanning calorimetryBentonitePolymer chemistryComposite materialInorganic chemistryChemistryGrapheneNanotechnology

Abstract

fetched live from OpenAlex

Naturally occurring silicates, like montmorillonite (MMT) have received much attention as reinforcement materials for polymers because of their potentially high aspect ratio and unique intercalation (exfoliation) characteristic. Montmorillonite is of particular interest because it has a layered structure typically about 1nm in thickness and a high aspect ratio ranging from 100 to 1500 that, with proper exfoliation, can lead to platelets with high stiffness and strength dispersed in the polymer matrix. In this paper, we studied the delamination of Egyptian bentonite and Cloisite Na+ (USA) using different onium ions. The organo-clays were characterized by X-ray diffraction (XRD), Differential Scanning Calorimeter (DSC), Derivativethermogravimetry (TGA), Thermogravimetry (TG), and Infrared spectroscopy (IR). Understanding the relationship between molecular structure of the modifying surfactant and the thermal stability of the organically modified layered silicates is critical to its processing and subsequent applications. Therefore, special emphasis is given to the study of the thermal degradation of the modified clays. The results of the thermal degradation of montmorillonite (MMT) and alkyl quaternary ammonium montmorillonite (OMMT) are discussed in this paper.

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 score0.004
Threshold uncertainty score0.945

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)0.0010.000

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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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
Published2008
Admission routes1
Has abstractyes

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