Positive bias and vacuum chamber wall effect on total electron yield measurement: A re-consideration of the sample current method
Bibliographic record
Abstract
The measurement of the total secondary electron yield (TEY, δ) is of fundamental importance in areas such as accelerator, spacecraft, detector, and plasma system. Most of the running TEY facilities in the world are based on the kind of bias strategy. The applied bias can assist in the collection of the secondary/primary electrons. In the prevailing sample current method, the TEY is obtained by the measurement of the current from the sample to ground with a negative/positive bias applied to the sample. One of the basic assumptions in this method is that the positive bias can retain most of the electrons emitted by the sample. This assumption is generally recognized based on the seeming fact that the low energy secondary electrons dominate the emitted electrons. In this work, by considering the full electron energy spectrum including both the true secondary and backscattered electrons, we give a new insight in this TEY measurement method. Through the analytical derivation as well as the Particle-in-Cell numerical simulation, we show that it is due to the following two factors, other than the assumption mentioned above, which make the sample current method works satisfactorily: (a) the TEY relative error is related to the TEY itself in the form of |1−δ|/δ, which indicates a smallest error when measuring samples with TEY closest to 1; and (b) the compensation effect of the vacuum chamber wall. Analytical results agree well with numerical simulations and furthermore, we present a correction method for reducing the TEY relative error when measuring samples with TEY below 1. By sweeping the positive bias from 50 to 500 V, a flat silver sample in the as-received state with maximum TEY larger than 2 and a laser etched sample with maximum TEY close to 1 were measured for further verification. The obtained experimental results agree well with the theoretical analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".