Surface Roughness Characterization of <font>ZnO</font>: <font>TiO</font><sub>2</sub>-Organic Blended Solar Cells Layers by Atomic Force Microscopy and Fractal Analysis
Bibliographic record
Abstract
The objective of this work is to quantitatively characterize the 3D complexity of ZnO : TiO 2-organic blended solar cells layers by atomic force microscopy and fractal analysis. ZnO : TiO 2-organic blended solar cells layers were investigated by AFM in tapping-mode in air, on square areas of 25 μm2. A detailed methodology for ZnO : TiO 2-organic blended solar cells layers surface fractal characterization, which may be applied for AFM data, is presented. Detailed surface characterization of the surface topography was obtained using statistical parameters, according with ISO 25178-2: 2012. The fractal dimensions Df values (all with average ± standard deviation), obtained with morphological envelopes method, for: blend D1 ( P 3 HT : PCBM : ZnO : TiO 2 blend with ratio 1:0.35:0.175:0.175 mg in 1 ml of Chlorobenzene) is Df = 2.55 ± 0.01; and for blend D2 ( P 3 HT : PCBM : ZnO : TiO 2 blend with ratio 1:0.55:0.075:0.075 mg in 1 ml of Chlorobenzene) is Df = 2.45 ± 0.01. Denoting the ratios in 1 ml of Chlorobenzene with D1 and D2 articles. The 3D surface roughness of samples revealed a fractal structure at nanometer scale. Fractal and AFM analysis may assist manufacturers in developing ZnO : TiO 2-organic blended solar cells layers with better surface characteristics and provides different yet complementary information to that offered by traditional surface statistical parameters.
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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 | 0.001 | 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".