Surface State‐Induced Anomalous Negative Thermal Quenching of Multiferroic BiFeO<sub>3</sub> Nanowires (Phys. Status Solidi RRL 1/2018)
Bibliographic record
Abstract
The multiferroic wide-bandgap semiconductor material BiFeO3 (BFO) has attracted intense research interests due to its multifunctional properties and broad range of potential applications in optoelectronic devices. However, in onedimensional BFO nanostructures, because of the high surfaceto- volume ratio, surface states play a crucial role in their optical and electrical properties offering extraordinary functionalities. In their Letter (article no. 1700352), Kovur Prashanthi and coworkers report surface state induced anomalous negative thermal quenching of semiconductor BFO nanowires. The BFO nanowires show a rare phenomenon of negative thermal quenching (NTQ) of emission, where the emission intensity of certain peaks shows an increase as a function of temperature instead of the routinely observed decrease in luminescence intensity. A possible mechanism for the observed NTQ behaviour is proposed using electron trapping and de-trapping at higher temperatures. The authors' studies reveal that the defect emission behaviour of the nanowires is due to the presence of localized surface states in the band gap caused by the oxygen vacancies. This phenomenon has neither been observed in thin-film nor in bulk BiFeO3. Therefore, this effect is enhanced in the nanowires because of their high surface-to-volume ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".