Annealing Effect on the Solid State and Optical Properties of <i>α</i>Fe<sub>2</sub>O<sub>3</sub> Thin Films Deposited Using the Aqueous Chemical Growth (ACG) Method
Bibliographic record
Abstract
Thin films of hematite (α-Fe2O3) were deposited by heteronucleation through the process of hydrolysis and condensation of an aqueous solution of 0.1 M Fe (NO3)3.9H2O, 1 M NaNO3, 50 ml H2O in addition with five drops of HCl at 90℃. One of the samples was kept as prepared while the others were annealed at different temperatures in order to determine the effect of annealing on their solid state and optical properties. The films were characterized using Rutherford Back Scattering (RBS), spectroscopy for chemical composition and thickness, X-Ray Diffraction (XRD) for structural analysis, UV-VIS Spectrophotometer for the analysis of other solid state and optical properties and a photomicroscope for photomicrographs. The results indicate that while the absorbance and absorption coefficient decreases with increasing annealing temperature, the direct band gap and refractive index increases with increasing annealing temperature in the direction of increasing photon energy in the visible range. Also, there is a high infrared transmittance which increases with increasing annealing temperature and a shift/decrease in peak value of all the optical properties except transmittance in the direction of increasing photon energy as annealing temperature increases. The results further indicate that ACG hematite thin film annealed at 632K is a suitable metal oxide semiconductor material for photocatalytic applications. It is also suitable for use in the construction of poultry houses for the rearing of chicks because of its high infrared transmittance including other opto-electronic applications.
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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".