Hierarchical Tin Oxide Nanostructures for Dye‐Sensitized Solar Cell Application
Bibliographic record
Abstract
Nanoscale material manipulation is the key to increasing solar light harvesting and photon‐to‐electron conversion efficiency (PCE) for an organic–inorganic photovoltaic system. Many SnO2 1D nanostructures, including nanowires and nanobelts, have been employed because of their potential of enhancing the charge collection properties of DSSCs by eliminating losses caused by grain boundary scattering of carriers in nanoparticle‐based DSSCs. Here, a new approach to growing hierarchical 1D SnO2 nanostructured layer by catalyst‐assisted pulsed laser deposition after introducing NiO into the SnO2 target is reported, and a plausible growth mechanism to describe the observed hierarchical nanostructures is presented. A remarkable improvement in the solar cell performance, including open circuit voltage, short circuit current density, fill factor, and PCE, by simple surface modification of the hierarchical SnO2 nanostructured photoanode is further demonstrated. Surface passivation is achieved on the as‐deposited hierarchical SnO2 nanostructures by dip coating with an MgO passivation layer of appropriately optimized thickness. Such an insulating layer is found to effectively reduce the recombination process caused by the higher electron mobility of SnO2 photoanode nanostructures. Compared with a pristine SnO2 nanobelt photoanode, a tenfold enhancement in their PCE (to 4.14%) has been observed for MgO‐passivated hierarchical SnO2 nanostructures.
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".