Evaluation on the Temperature and Calcination Time During Sol-gel Coating of TiO2 on Iron Foam substrate
Bibliographic record
Abstract
Iron foam is iron based material which is widely applied due to its unique properties. However since corrosion is also a problem for this material, coating with innert material is required in enhancing its applications. In the present research, TiO2 coating is performed on iron foam suface by sol-gel dipping method. Focus is given on the study of the effect of calcinations temperature and time on the coating characteristics. TiCl4 is used as the precursor with concentration of 0.3 M, added with 1M HCl solution and chitosan soluation with concentration of 1%. Calcination is performed at temperature of 400, 500 dan 600oC and calcinations time of 1 and 3 hours inside controlled gas furnace using nitrogen atmosphere. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) characterization data shows that TiO2 particles form on the iron foam substrate. SEM characterization on the sample heated at 400oC and heating time of 1 hour shows the formation of nano particle titania (0.06 μm) which is homogeneously distributed with less agglomeration than others and considered as the best sample in the present research. As the temperature and time of calcinations increase, more inhomogeneous particle distribution and bigger particles form.
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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".