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The Adhesive Strength of Epoxy/Sol-Gel Materials Modified by Various Ratio of γ-Al2O3 Nanoparticles

2018· article· en· W2888909374 on OpenAlexvenueno aff
Balhassn S. M. Ali, Mousa May, Heming Wang, R. Akid

Bibliographic record

VenueJournal of Coating Science and Technology · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsEpoxyAdhesiveMaterials scienceComposite materialNanoparticleNanotechnology

Abstract

fetched live from OpenAlex

In this study, the use of sol-gel/epoxy adhesive based on the combination of organic and inorganic components within the adhesive matrix have been studied. The combination of different amounts of ᵞ-Al2O3 nano-particles to the adhesive matrix was evaluated. Mild steel specimens were prepared for lap joints, which were cured in an oven at 200°C for 16 hours. The bond strength of the sol-gel/epoxy matrix was investigated using a universal tensile test machine. The presence of Al-OH and/or Si-OH bonds increases causing an increase in the strength of the bulk material. This process is seen through the appearance of an absorption peak shoulder which appears in the range of ~ 1088 to1100 cm-1 which corresponds to Al-O-Si or Si-O-Si. The maximum adhesive strength of composite sol-gel/epoxy adhesive recorded was 23±0.4 MPa. This was obtained when small amounts of γ-Al2O3 nano-particles (4.0 wt%), were incorporated within the matrix. However, as the level of these inorganic materials in the adhesive matrix increased further, the adhesive shear strength gradually decreased. At a high ratio of γ-Al2O3 particles, poor interfacial bonding or adhesion between the filler and the adhesive matrix is recorded. Scanning Electron Microscopy (SEM) is used to investigate the fracture surface with 4.0 wt% γ-Al2O3, the scanning shows a very small distance of cracks, suggesting the material may act as a barrier to crack propagation and thus increases the energy required for fracture.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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