High flow rate nanofluidics for <i>in-liquid</i> electron microscopy and diffraction
Bibliographic record
Abstract
Abstract We introduce a nanofluidic platform that can be used to carry out femtosecond electron diffraction (FED) and transmission electron microscopy (TEM) measurements in liquid samples or in-liquid specimens, respectively. The nanofluidic cell (NFC) system presented herein has been designed to withstand high sample refreshing rates (over one kilohertz), a prerequisite to succeed with FED experiments in our lab. Short beam paths, below 1 μ m, in combination with ultrathin membranes (less than 100 nm thick) are necessary conditions for in-liquid FED and TEM studies due to the strongly interacting nature of electrons. Depending on the application, the beam path in our NFC can be tuned between 50 nm and 10 μ m with ultrathin stoichiometric silicon nitride (Si 3 N 4 ) windows as thin as 20 nm. Stoichiometric Si 3 N 4 has been selected to reduce membrane bulging owing to its higher tensile stress and transparency in the UV–vis–NIR region to allow for laser excitation in FED experiments. Key design parameters and improvements made over previous NFC systems are discussed, and some preliminary electron images obtained by 200 kV scanning TEM are presented.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".