Design and evaluation of a novel Faraday cup for easy and accurate beam current measurement in a transmission electron microscope
Bibliographic record
Abstract
Beam current measurement is a crucial step in estimating the electron dose when studying beam-sensitive samples in electron microscopy. A Faraday cup is a standard tool for measuring beam current; however, commercially available Faraday cups for transmission electron microscopes (TEM) are limited, expensive, and often difficult to use as the cup itself is invisible in the TEM. We herein present a new Faraday cup design that fits into an insulated TEM holder of Hitachi HT7700 and HT7800 series and can be easily located using four symmetrical through holes around the cup. This design also accommodates the 3 mm TEM mesh grid in the holder and allows both sample imaging and access to the Faraday cup within the TEM stage movement range. We evaluated the effectiveness of our Faraday cup in capturing the electron beam by varying the diameter to depth ratio and material of the Faraday cup through experimental measurements and Monte Carlo simulations, demonstrating an accuracy better than 1-2 %. The preferred configuration is an aluminum cup with a diameter of 0.2 mm and a depth of 0.8 mm. Monte Carlo simulations also suggest that this Faraday cup provides accurate beam current measurement at different electron energies. Our novel Faraday cup design provides a practical, simple, and cost-effective solution for beam current measurement in a TEM.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 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".