Thermal characterization of morphologically diverse copper phthalocyanine thin layers by scanning thermal microscopy
Bibliographic record
Abstract
Morphologically diverse copper phthalocyanine (CuPc) thin layers were thermally characterized by scanning thermal microscopy (SThM). The organic layers with thicknesses below 1 µm were deposited by physical vapor deposition in a high vacuum on the N-BK 7 glass substrates. Four set of samples were fabricated and studied. Atomic Force Microscopy imaging revealed strong differences in the surface roughness, mean grain size/height, as well as distances between grains for the CuPc layers. For quantitative thermal investigations, three active SThM operating modes were applied using either a Wollaston thermal probe (ThP) or KNT ThP as thermal probe heated with a DC, an AC (3ω-SThM) current or their combination (DC/AC SThM). Meanwhile, qualitative analysis was performed by thermal surface imaging. The results of this study revealed a correlation between the morphology and the local thermophysical properties of the examined CuPc thin layers. It was found that the heat transport properties in such layers will deteriorate with the increase of the surface roughness and porosity. Those results can be a valuable contribution to the further development of phthalocyanine-based devices.
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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".