Plasmonic gold nanoparticles for ZnO-nanotube photoanodes in dye-sensitized solar cell application
Bibliographic record
Abstract
Surface modification of nanostructured metal oxides with metal nanoparticles has been extensively used to enhance their nanoscale properties. The unique properties of metal nanoparticles associated with their controllable dimensions allow these metal nanoparticles to be precisely engineered for many applications, particularly for renewable energy. Here, a simple electrodeposition method to synthesize gold nanoparticles (GNPs) on electrochemically grown ZnO nanotubes (NTs) is reported. The size distribution and areal density of the GNPs can be easily controlled by manipulating the concentration of AuCl3 electrolyte solution, and the deposition time, respectively. An excellent enhancement in the optical properties of ZnO NTs surface-decorated with GNPs (GNP/ZnO-NT), especially in the visible region, is attributed to their surface plasmon resonance. The plasmonic effects of GNPs, together with the large specific surface area of ZnO NTs, can be used to significantly enhance the dye-sensitized solar cell (DSSC) properties. Furthermore, the Schottky barrier at the Au/ZnO interface could prevent electron back transfer from the conduction band of ZnO to the redox electrolyte and thus could substantially increase electron injection in the ZnO conduction band, which would further improve the overall performance of the constructed DSSCs. The GNP/ZnO-NT photoanode has been found to increase the efficiency of the DSSC significantly to 6.0% from 4.7% of the pristine ZnO-NT photoanode, together with corresponding enhancements in short-circuit current density from 10.4 to 13.1 mA cm(-2) and in fill factor from 0.60 to 0.75, while the open-circuit voltage remain effectively unchanged (from 0.60 to 0.61 V). Surface decoration with GNPs therefore provides an effective approach to creating not only a high specific surface area for superior loading of dye molecules, but also higher absorbance capability due to their plasmonic effect, all of which lead to excellent performance enhancement for DSSC application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".