TiO2-UV detection under different types of acids and rinsing process for films preparation
Bibliographic record
Abstract
Titanium dioxide (TiO2) films were prepared by the sol–gel method under different types of acids. Effect of hydrochloric acid (HCl), nitric acid (HNO3) and nature-based solutions (NBS) under ultraviolet (UV) detection of films were investigated. Films were coated by dip coating on glass substrate. Moreover, TiO2 films were compared properties on water rinsing (Ri) and without rinsing (WRi) processes. The phase structure of prepared samples was characterized by means of X-ray powder diffraction (XRD). The results confirm that films were highly crystalline anatase TiO2 and free from other phases of titanium dioxide. For the optical property, the transmittance (%T) observed sharp rise in the violet-ultraviolet transition region and a maximum transmittance of ~80%. Photocurrents were measured under UV intensity of 260 mWcm–2 and DC bias voltage of -20 – 20 volts (V). The results observed that currents increased as bias voltage increased. Current - Voltage (I-V) curves observed different slopes under the different of acids and rinsing process. The photocurrent of TiO2-NBS-Ri was greater than TiO2-HCl-WRi and TiO2-HNO3-WRi of 10 and 20 times; respectively. These findings suggest that the significant effect of the acid and rinsing process on crystalline, morphological, optical, and electrical properties of nanostructured TiO2 films would be useful for applying the device in UV photoelectric detection.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".