Comparative Study of Nanoscale Surface Structures of Calcite Microcrystals Using FE-SEM, AFM, and TEM
Bibliographic record
Abstract
The bulk morphology and surface features that developed upon precipitation on micrometer-size calcite powders and millimeter-size cleavage fragments were imaged by three different microscopic techniques: field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM) of Pt-C replicas, and atomic force microscopy (AFM). Each technique can resolve some nanoscale surface features, but they offer different ranges of magnification and dimensional resolutions. Because sample preparation and imaging is not constrained by crystal orientation, FE-SEM and TEM of Pt-C replicas are best suited to image the overall morphology of microcrystals. However, owing to the decoration effect of Pt-C on the crystal faces, TEM of Pt-C replicas is superior at resolving nanoscale surface structures, including the development of new faces and the different microtopography among nonequivalent faces in microcrystals, which cannot be revealed by FE-SEM. In conjunction with SEM, Pt-C replica provides the evidence that crystals grow in diverse and face-specific modes. The TEM imaging of Pt-C replicas has nanoscale resolution comparable to AFM. AFM yielded quantitative information (e.g., crystallographic orientation and height of steps) of microtopographic features. In contrast to Pt-C replicas and SEM providing three-dimensional images of the crystals, AFM can only image one individual cleavage or flat surface at a time.
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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".