Formation of Hierarchical Zinc Oxide Nanostructures for Solar Energy Converters and Photovoltaics
Bibliographic record
Abstract
Zinc oxide is a promising material for the fabrication of thin film layers for a new generation of photovoltaic devices and solar thermal collectors, due to the unique optical and electrical properties as well as the propensity to form filamentous one-dimensional submicron structures, namely nanowires, nanorods, nanotubes, nanobelts, and hierarchical nanostructures with developed surface and projected superhydrophobicity. We identified the possibility for a development of planar single-layer antireflection coatings and/or arrays of nanorods of this material as having the shape of hexagonal prisms, and demonstrating the moth eye effect on the substrates of transparent conductive tin dioxide and on silicon wafers with embedded homojunctions. The optimization of the pulsed electrodeposition of zinc oxide arrays adjust the size of parabolic nanonipples for the implementation of antireflection coatings with the moth eye effect on various substrates, including flexible. The antireflection coatings will be developed for thin-film photovoltaic devices of substrate configuration based on kesterite, tin sulphide, and fullerene layers, for the cadmium telluride based photovoltaic cells with bilateral sensitivity of superstrate configuration on the flexible substrates and for optoelectronic devices based on zinc selenide for the ultraviolet spectra.
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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".