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Record W2258562269

A COMPARATIVE STUDY ON MECHANICAL AND ADHESION PROPERTIES OF CALCINATED AND NON CALCINATED NANOBIOGLASS-TITANIA NANO COMPOSITE COATINGS ON STAINLESS STEEL SUBSTRATES

2010· article· en· W2258562269 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientia Iranica · 2010
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceComposite numberNanocompositeComposite materialSol-gelNanotechnology
DOInot available

Abstract

fetched live from OpenAlex

Thick lms of calcinated and non calcinated nanobioglass(NBG)-titania nanocomposite coatings were prepared on stainless steel substrates using an alkoxide sol-gel process. The prepared lms were characterized by TEM, SEM, EDS, XRD and other methods. The composite lms obtained from calcinated NBG particles were compared to the lms obtained from non calcinated NBG particles. Here, we present a comparative study on the mechanical and adhesion properties of two types of lm (TiO2- calcinated NBG and TiO2-non calcinated NBG). The prepared thick lms were smooth and free of macro cracking, fracture or aking. The grain size of these lms was uniform and its nano scale con rmed using a TEM microscope. Adhesion tests were carried out according to the ASTM-D-3359-97 standard. The results showed that both calcinated and non calcinated NBG-titania lms have very good adhesion properties. The hardness of the prepared lms (TiO2-calcinated NBG and TiO2-non calcinated NBG) was compared by using a micro hardness test method. The results veri ed that the presence of calcinated NBG particles in a NBG-titania composite gradually enhanced the mechanical data of the prepared lms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.242
Teacher spread0.223 · 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