Probing the morphology-device relation of Fe<sub>2</sub>O<sub>3</sub>nanostructures towards photovoltaic and sensing applications
Bibliographic record
Abstract
A lot of research on nanomaterials has been carried out in recent years. However, there is still a lack of nanostructures that have a combination of superior properties; both efficient electron transport and high surface area. Here, the authors have tried to develop hybrid α-Fe(2)O(3) flower-like morphology which exhibits both superior electron transport and high surface area. Intrigued by the unique properties of Fe(2)O(3) at the nanoscale and its abundance in nature, we have demonstrated a facile template-free solution based synthesis of hybrid α-Fe(2)O(3) comprising nanopetals nucleating radially from a 3D core. Due to its simplicity, the synthesis process can be easily reproduced and scaled up. We carried out in-depth studies on gas sensing and dye-sensitized solar cell (DSSC) device characterization so as to gain an understanding of how surface area and transport properties are affected by variation in morphology. The hybrid α-Fe(2)O(3) nanostructures are studied as potential candidates for gas sensors and for the first time as a working electrode for DSSC.
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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 | 0.001 | 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".