Charge Contrast Imaging of Gibbsite Using the Variable Pressure SEM
Bibliographic record
Abstract
The variable pressure scanning electron microscope (VP-SEM) allows imaging of insulators without the need for a conductive coating, due to charge neutralization at the surface from recombination of positive ions and surface electrons. Varying certain parameters such as pressure, bias, and working distance creates incomplete neutralization, and localized charging develops called charge contrast. Although the exact mechanism creating charge contrast imaging (CCI) is unknown, it is agreed that it is related to an optimum charge compensation. The behavior of the CCI is still vague, which presents a problem for determining the mechanisms. This article provides user-friendly methods of finding the optimum levels of charge contrast in the VP-SEM. We show that the CCI is obtained at optimum operating conditions where the specimen current is between 2.5 nA and 3.5 nA. The specimen current is a function of secondary electrons (SE) emission and ionization potential, producing an ion flux. Therefore an optimum specimen current represents the balanced conditions of SE emission and ion flux. Controlling the pressure, working distance, bias, scan rate, and beam current allows the microscopist to set the specimen current at this optimum level for charge contrast imaging. All the work was performed on gibbsite using the S3000N VP-SEM from Hitachi.
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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 | 0.001 | 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".