Dynamic imaging of Au-nanoparticles via scanning electron microscopy in a graphene wet cell
Bibliographic record
Abstract
High resolution nanoscale imaging in liquid environments is crucial for studying molecular interactions in biological and chemical systems. In particular, electron microscopy is the gold-standard tool for nanoscale imaging, but its high-vacuum requirements make application to in-liquid samples extremely challenging. Here we present a new graphene based wet cell device where high resolution scanning electron microscope (SEM) and energy dispersive x-rays (EDX) analysis can be performed directly inside a liquid environment. Graphene is an ideal membrane material as its high transparancy, conductivity and mechanical strength can support the high vacuum and grounding requirements of a SEM while enabling maximal resolution and signal. In particular, we obtain high resolution ([Formula: see text] nm) SEM video images of nanoparticles undergoing Brownian motion inside the graphene wet cell and EDX analysis of nanoparticle composition in the liquid enviornment. Our obtained resolution surpasses current conventional silicon nitride devices imaged in both a SEM and transmission electron microscope under much higher electron doses.
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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".