A Novel Buffering Technique for Aqueous Processing of Zinc Oxide Nanostructures and Interfaces, and Corresponding Improvement of Electrodeposited ZnO‐Cu<sub>2</sub>O Photovoltaics
Bibliographic record
Abstract
Abstract A novel buffering method is presented to improve the stability of zinc oxide processed in aqueous solutions. By buffering the aqueous solution with a suitable quantity of sacrificial zinc species, the dissolution of functional zinc oxide structures and the formation of unwanted impurities can be prevented. The method is demonstrated for ZnO films and nanowires processed in aqueous solutions used for the selective etching of mesoporous anodic alumina templates and the electrochemical deposition of Cu2O. In both cases, improved ZnO stability is observed with the buffering method. ZnO‐Cu2O heterojunction solar cells (bilayer and nanowire cells) synthesized using both traditional and buffered deposition methods are characterized by impedance spectroscopy and solar simulation measurements. Buffering the Cu2O deposition solution is found to reduce unwanted recombination at the heterojunction and improve the photovoltaic performance.
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) | 0.001 | 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 | 0.001 | 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".