Bibliographic record
Abstract
Wet cell electron microscopy is important for a wide range of studies from biological theory to polymer physics and chemistry. Wet cell microscopy is typically performed using silicon nitride (SiN) cells which have a fundamental limit to their resolution. This is due to limitations in how thin the SiN membranes can be etched. With the discovery of graphene in 2004, we are able to surpass these limitations by replacing SiN membranes with an atomic thick layer of graphene. This thesis will highlight the fabrication and results obtained in our novel graphene wet cell device. In particular, we are able to demonstrate live high resolution (< 5 nm) SEM (Scanning Electron Microscope) video images of nanoparticles undergoing brownian motion inside the graphene wet cell. We are also able to perform characterisation of the liquid environment through EDX (Energy Dispersive X-rays spectroscopy). This is possible due to the transparency and strength of the graphene window which can sustain the high vacuum and grounding requirements of a SEM. Looking ahead, our approach is a crucial proof-of-principle for electron-microscopy based DNA mapping using molecules tagged with nanoparticles of different chemical composition.
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.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".