David Joy’s Invaluable Contribution to Modern Scanning (Transmission) Electron Microscopy and Analysis
Bibliographic record
Abstract
Among the pioneers of modern electron microscopy (EM), David Joy will always have a special place since his research findings are still, and will be for many years, the foundation of several scanning electron microscopy (SEM) techniques. Over the years he prepared the ground for routinely used techniques like magnetic domain [1] and channeling contrast (ECCI) [2] imaging, electron energy-loss spectroscopy (EELS) [3], X-ray microanalysis [4], low voltage secondary electron microscopy [5] and charging mechanisms [6] among others. In parallel, his deep understanding of physical concepts involved in EM theory has led him to significantly improve the Monte Carlo modeling precision and productivity [7]. His contribution in these fields was invaluable and remains an important source of inspiration for his followers. Manufacturers now integrate low and ultra-low voltage modes into their state-of-the-art commercial SEMs and recently Hitachi has released a perfect combo low voltage SEM/STEM with a cold-field emission source, namely the SU-9000, an improved version of the immersion-lens S5500 FEG-SEM [8]. This machine combines 30 kV STEM capabilities with energy-dispersive (EDS) and EEL spectroscopies as well as low voltage SEM deceleration with landing voltages down to 100 V. The SU-9000, as well as the state-of-the-art SU-8230 CFE-SEM, among other candidates, celebrate David Joy’s vision of EM. In this presentation, modern SEM/STEM characterization will be demonstrated with an obvious linkage to David Joy’s invaluable contribution to EM. Figure 1 shows secondary (a) and backscattered (b) electron micrographs from a single scan obtained from a carbon nanotube (CNT) covered with Ni-Pt nanoparticles (NPs). Beam deceleration technology was used to produce a 100-eV landing energy beam. In Figure 1c, we exemplify how ECCI can now routinely provide detailed microstructure from a bulk complex martensitic steel alloy at E0 = 10 kV, showing nanometric carbides (C) and single dislocations (D). With the same SEM technology, low voltage STEM images and analysis can be performed at an energy of 30 keV and provides Mg lattice images (a), Li elemental maps (b) as well as energy-filtered images (c, d) using plasma resonance from a Ru covered aluminum NP. Finally, Figure 2e demonstrates the high-quality EDS maps obtainable in a CNT+TiO2 NPs system due to the net increase of scattering at 30 kV in the specimen versus 200 kV. (a) SE and (b) BSE images of a CNT + NiPt at 100 V landing voltage and (c) BSE image of a bulk martensitic steel at E0 = 10 kV showing dislocations (D) and nanometric carbides (C). Low voltage (30 kV) STEM: (a) Lattice image of a Mg NP, (b) Li jump ratio map of an Al-Li-Cu alloy and (c, d) SE/surface plasmon images from Al NP + Ru obtained using the three-window technique in EELS; (e) EDS elemental x-ray images of a CNT covered with TiO2 NPs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".