Fabrication of Nanostructured Cadmium Selenide Thin Films for Optoelectronics Applications
Bibliographic record
Abstract
There is lot of research work at enhancing the performance of energy conversion and energy storage devices such as solar cells, supercapacitors, and batteries. In this regard, the low bandgap and a high absorption coefficient of CdSe thin films in the visible region, as well as, the low electrical resistivity make them ideal for the next generation of chalcogenide-based photovoltaic and electrochemical energy storage devices. Here, we present the properties of CdSe thin films synthesized at temperatures (below 100°C using readily available precursors) that are reproducible, efficient and economical. The samples were characterized using XRD, FTIR, RBS, UV-vis spectroscopy. Annealed samples showed crystalline cubic structure along (111) preferential direction with the grain size of the nanostructures increasing from 2.23 to 4.13 nm with increasing annealing temperatures. The optical properties of the samples indicate a small shift in the bandgap energy, from 2.20 to 2.12 eV with a decreasing deposition temperature. The band gap is suitably located in the visible solar energy region, which make these CdSe thin films ideal for solar energy harvesting. It also has potential to be used in electrochemical energy storage 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 | 0.001 |
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".