A COMPARATIVE STUDY ON MECHANICAL AND ADHESION PROPERTIES OF CALCINATED AND NON CALCINATED NANOBIOGLASS-TITANIA NANO COMPOSITE COATINGS ON STAINLESS STEEL SUBSTRATES
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".