Investigation of the improved performance in a graphene/polycrystalline BiFeO3/Pt photovoltaic heterojunction: Experiment, modeling, and application
Bibliographic record
Abstract
We report on the enhancement of photovoltaic performance in a graphene/polycrystalline BiFeO3 (BFO)/Pt heterojunction for the first time. The unique properties of the graphene electrode lead to a short circuit current density of 61 μA/cm2 and an open circuit voltage of 0.52 V in the heterojunction. These values are much higher than the results reported in polycrystalline BFO with indium tin oxide as the top electrode. A theoretical band diagram model and an equivalent electrical model considering the ferroelectric polarization, interface states, and energy band bending effect are constructed to depict the carrier transport behavior. Important photovoltaic parameters, such as conversion efficiency, illumination intensity response, ON/OFF characteristics, minority carrier lifetime, and external quantum efficiency, are investigated experimentally and theoretically. To improve the photovoltaic performance of the graphene/polycrystalline BFO/Pt heterojunction, HNO3 treatment, and CdSe quantum dots (QDs) filling/sensitizing, as two independent chemical and physical routines, were processed and compared. It can be seen that the photocurrent density exhibits a significant improvement from 61 μA/cm2 to 8.67 mA/cm2 (∼150 fold) after HNO3 treatment, while a considerable enhancement of ∼5 fold is seen with QDs filling/sensitizing. We also present and investigate an optical application of our graphene/polycrystalline BFO/Pt heterojunction as a photosensitive detector.
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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".