Direct Measurement of Density for Evaporated Thin Films
Bibliographic record
Abstract
A simple and elegant method is reported for direct measurement of the density of vapor-deposited thin films using a combination of profilometry, microscopy, and high-vacuum thermogravimetric analysis (TGA). Density affects fabrication control, optical properties, charge transport, and mechanical properties of thin film devices. Accurate density determination is essential for optimizing device design to ensure film stability, charge balance, and light-matter interactions. Exact mass of the vapor-deposited thin films with suitable mass and aspect ratio is determined by high-vacuum TGA. Combined with precise measurements of thickness and area for each film, the true density of individual films is captured as deposited. This method avoids common universalizing assumptions about the degree of crystallinity in a film. Furthermore, this method does not depend on bulk optical or electrical parameters that may vary as a result of the unique nanoscale properties of thin films. Density values are reported for a range of small molecule semiconductors commonly found in organic electronics such as organic light emitting diodes (OLEDs), organic photovoltaics (OPVs), and organic thin film transistors (OTFTs), and compared to values calculated by other means. It is anticipated that this technique can be used to measure the density of a wide range of vacuum-stable thin film materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".