Bibliographic record
Abstract
In the lines of a previous paper [1], this contribution will describe the strong influence that David C. Joy had in my research in electron microscopy. As a graduate student, in 1985, I developed my first Monte Carlo program following a paper by David Joy et al. [2] that describes the effect of Fast Secondary Electrons [FSE] on x-ray generation. Their simulations showed that FSE can generate a significant fraction of the x-rays for energy lines below 1 keV. My Monte Carlo simulations showed that FSE can make Cliff-Lorimer K factors composition dependant when one energy line is below 1 keV and the other above [3]. I met David Joy the first time at the 1988 EMSA conference of Milwaukee where I asked him several technical questions. He was very nice to answer to a young student. Later, he accepted to be the external evaluator of my Ph. D. thesis that I defended in July 1990. After graduation, I started to work as Assistant Professor at University de Sherbrooke and we started to collaborate. I visited him several times in Knoxville. At one of those visits, he gave me his computed values of the Mott elastic cross sections and this eventually led to the creation of the program CASINO that was released in 1996 [4]. The help of David Joy must be credited in the development of CASINO. Figure [1] shows plot of electron trajectories in bulk Silicon at 1, 5 and 10 keV simulated with CASINO. These simulations followed those made by David Joy [5] for C. As it can be seen, in bulk Si, the electron range goes from 1 μm at 10 keV to 10 nm at 1 keV. David Joy [5] promoted low voltage SEM to improve spatial resolution. Since getting TEM was nearly impossible at that time, I jumped to this idea and we predicted that 10 nm spherical MnS inclusions in steel can be imaged at 5 keV with a 10 nm probe size using CASINO [6]. Figure [2] shows a 3 keV BSE picture of an bulk Aluminum alloy AA2199 taken with the Hitachi SU-8230. T1 plates (AlLiCu) of 2 – 4 nm width, δ′ phases (Al3Li) and β phases (Al3Zr) of 20 nm of radius are clearly visible, demonstrating the validity of the predictions made by Monte Carlo simulations in 1995. Low voltage SEM competes with TEM without having to make thin foils. While I visited David Joy the first time in Knoxville, in March 1991, he told me that STEM in the SEM will work because with the new cold field emitters and no lens below the sample, the chromatic aberration will be minimal, and the spatial resolution of this imaging mode will be almost as good as conventional TEM. He made many STEM instrumental contributions with the Hitachi S-5500. I followed David Joy idea with the Hitachi SU-8000 and SU-8230 for STEM at 30 keV, with Nicolas Brodusch and Hendrix Demers [7]. In 2017, I acquired the SU-9000 from Hitachi that has a resolution of 1,6 Å in Bright Field STEM at 30 keV and with the first EELS detector out of the Naka factory. Figure [3] shows a Li EELS jump ratio map of the AA 2199 alloy acquired at 30 keV. T1 plates of 2 nm and δ′ phases of 20 nm are clearly visible. Each time I see the results of the SU-9000, the remember what David Joy told me in 1991. Monte Carlo simulations of electron trajectories in bulk Silicon at 1, 5 and 10 keV simulated with CASINO. CASINO is available for free at www.mermg.com. Aluminum alloy AA 2199 imaged at 3 keV from a bulk specimen with the Hitachi SU-8230. Picture taken by Nicolas Brodusch. Li jump ratio EELS map of an Aluminum alloy AA 2199 acquired at 30 keV with the Hitachi SU-9000. Image acquired by Nicolas Brodusch.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".