Spin‐coated epoxy resin embedding technique enables facile SEM/FIB thickness determination of porous metal oxide ultra‐thin films
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Bibliographic record
Abstract
A facile nonsubjective method was designed to measure porous nonconductive iron oxide film thickness using a combination of a focused ion beam (FIB) and scanning electron microscopy. Iron oxide films are inherently nonconductive and porous, therefore the objective of this investigation was to optimize a methodology that would increase the conductivity of the film to facilitate high resolution imaging with a scanning electron microscopy and to preserve the porous nature of the film that could potentially be damaged by the energy of the FIB. Sputter coating the sample with a thin layer of iridium before creating the cross section with the FIB decreased sample charging and drifting, but differentiating the iron layer from the iridium coating with backscattered electron imaging was not definitive, making accurate assumptions of the delineation between the two metals difficult. Moreover, the porous nature of the film was lost due to beam damage following the FIB process. A thin layer plastication technique was therefore used to embed the porous film in epoxy resin that would provide support for the film during the FIB process. However, the thickness of the resin created using conventional thin layer plastication processing varied across the sample, making the measuring process only possible in areas where the resin layer was at its thinnest. Such variation required navigating the area for ideal milling areas, which increased the subjectivity of the process. We present a method to create uniform thin resin layers, of controlled thickness, that are ideal for quantifying the thickness of porous nonconductive films with FIB/scanning electron microscopy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it