Nanostructured Oxide (SnO2, FTO) Thin Films for Energy Harvesting: A Significant Increase in Thermoelectric Power at Low Temperature
Bibliographic record
Abstract
Previous studies have shown that undoped and doped SnO2 thin films have better optical and electrical properties. This study aims to investigate the thermoelectric properties of two distinct semiconducting oxide thin films, namely SnO2 and F-doped SnO2 (FTO), by the nebulizer spray pyrolysis technique. An X-ray diffraction study reveals that the synthesized films exhibit a tetragonal structure with the (200) preferred orientation. The film structural quality increases from SnO2 to FTO due to the substitution of F− ions into the host lattice. The film thickness increases from 530 nm for SnO2 to 650 nm for FTO films. Room-temperature electrical resistivity decreases from (8.96 ± 0.02) × 10−2 Ω·cm to (4.64 ± 0.01) × 10−3 Ω·cm for the SnO2 and FTO thin films, respectively. This is due to the increase in the carrier density of the films, (2.92 ± 0.02) × 1019 cm−3 (SnO2) and (1.63 ± 0.03) × 1020 cm−3 (FTO), caused by anionic substitution. It is confirmed that varying the temperature (K) enhances the electron transport properties. The obtained Seebeck coefficient (S) increases as the temperature is increased, up to 360 K. The synthesized films exhibit the S value of −234 ± 3 μV/K (SnO2) and −204 ± 3 μV/K (FTO) at 360 K. The estimated power factor (PF) drastically increases from ~70 (μW/m·K2) to ~900 (μW/m·K2) for the SnO2 and FTO film, respectively.
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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".