The effect of secondary electrons on radiolysis as observed by in liquid TEM: The role of window material and electrical bias
Bibliographic record
Abstract
The effect of window material on electron beam induced phenomena in liquid phase electron microscopy (LPEM) is an interesting yet under-explored subject. We have studied the differences of electron beam induced gold nanoparticle (AuNP) growth subject to three encapsulation materials: Silicon Nitride (Si3N4), carbon and formvar. We find Si3N4 liquid cells (LCs) to result in significantly higher AuNP growth yield as compared to LCs employing the other two materials. In all cases, an electrical bias of the entire LC structures significantly affected particle growth. We demonstrate an inverse correlation of the AuNP growth rate with secondary electron (SE) emission from the windows. We attribute these differences at least in part to variations in SE emission dynamics, which is seen as a combination of material and bias dependent SE escape flux (SEEF) and SE return flux (SERF). Furthermore, our model predictions qualitatively match electrochemistry expectations.
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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 | 0.001 |
| 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".