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Record W2507781683 · doi:10.6000/1927-5129.2016.12.52

Synthesis and Characterization of Low-Cost Epoxy-Based Erosion Resistant Nanocomposite Coating

2016· article· en· W2507781683 on OpenAlex
Shujaat Ali, Sajid Ali, Mohamed Ahmed Naeem, Shadrukh Abdul Haq, Muhammad Mashhood, Ammad Ali, S. Adnan Hasan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2016
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNanocompositeEpoxyMaterials scienceCoatingComposite materialThermal stabilityStearic acidChemical engineering

Abstract

fetched live from OpenAlex

We report a simple route to synthesized erosion resistant epoxy-based nanocomposite coatings. The silica nanoparticles were surfaced modified using stearic acid and then incorporated into the epoxy coating. The resulting nanocomposite coating films were characterized for erosion resistance, mechanical and thermal stability. For the application on turbine blades, conventional techniques were used. It was found that for the incorporation of nano silica into the epoxy matrix, surface modification was essential. Besides, incorporation of silica resulted in considerable improvement in the resistance to erosive wear and a life span improvement of around 36 percent was achieved. Similar trend was observed for the Shore D hardness which increases from 60 for the virgin coating to 70 for the nanocomposite coating.

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.

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)0.000
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 score0.035
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.208
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