High performance BiFeO3 ferroelectric nanostructured photocathodes
Bibliographic record
Abstract
Ferroelectric materials may be used as effective photoelectrocatalysts for water splitting due to enhanced charge carrier separation driven by their spontaneous polarization induced internal electric field. Compared to other ferroelectric materials, BiFeO3 exhibits a high catalytic efficiency due to its comparatively smaller bandgap, which enables light absorption from a large part of the solar spectrum and its higher bulk ferroelectric polarization. Here, we compare the photoelectrochemical properties of three different BiFeO3 morphologies, namely, nanofibers, nanowebs, and thin films synthesized via electrospinning, directly on fluorine-doped tin oxide (FTO) coated glass substrates. A significant photocathodic current in the range from −86.2 to −56.5 μA cm−2 at −0.4 V bias (vs Ag/AgCl) has been recorded for all three morphologies in 0.1M Na2SO4 aqueous solution (pH = 6.8). Among these morphologies, BiFeO3 nanofibers exhibit higher efficiency because of their larger surface area and improved charge separation resulting from rapid diffusion of photoinduced charge carriers along the axis of the nanofiber. In the case of BiFeO3 nanofibers, we obtained the highest photocurrent density of −86.2 µA/cm2 at −0.4 V bias (vs Ag/AgCl electrode) and an onset potential of 0.22 V. We also observed that the onset potential of the photocathodic current can be increased by applying a positive polarization voltage, which leads to favorable bending of band edges at the electrode/electrolyte interface resulting in increased charge carrier separation.
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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".