Imaging layers in thin-film molecular devices by transmission electron microscopy, using milling by focused ion beams and deposition on NaCl and Si
Bibliographic record
Abstract
The performance of molecule-based thin-film devices such as organic light-emitting diodes, photovoltaic cells, and thin-film transistors depends on the electronic properties of the individual molecular components, as well as on their association to form complex morphologies. Transmission electron microscopy (TEM) can be used to image the morphologies and help reveal how the devices work and can be improved. We have examined the suitability of various ways to prepare samples of thin molecular films for imaging by TEM. Specifically, we have used focused ion beams to mill cross sections of complete devices that have been glued together with epoxy adhesives. In addition, thin films of the type used as active layers in molecule-based devices can be deposited on disks of NaCl, which can then be dissolved in water to release free-standing films that can be imaged by TEM, without loss of nanostructural details. Films of this type can also be deposited on Si wafers, which can then be fractured to expose sections of film that overhang edges of fragments and can be imaged conveniently by TEM. This allows TEM to be used as a quick method for screening samples and monitoring the purification of active 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".