Charge Contrast Imaging of Gibbsite Using the Variable Pressure SEM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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