Elemental quantification using electron energy-loss spectroscopy with a low voltage scanning transmission electron microscope (STEM-EELS)
Bibliographic record
Abstract
Electron beam damage in electron microscopes is becoming more and more problematic in material research with the increasing demand of characterization of new beam sensitive material such as Li based compounds used in lithium-ion batteries. To avoid radiolysis damage, it has become common practice to use Cryo-EM, however, knock-on damage can still occur in conventional TEM/STEM with a high-accelerating voltage (200-300 keV). In this work, electron energy loss spectroscopy with an accelerating voltage of 30,20 and 10 keV was explored with h-BN, TiB2 and TiN compounds. All Ti L2,3, N K and B K edges were successfully observed with an accelerating voltage as low as 10 keV. An accurate elemental quantification for all three samples was obtained using a multi-linear least square (MLLS) procedure which gives at most a 5% of standard deviation which is well within the error of the computation of the inelastic partial-cross section used for the quantification. These results show the great potential of using low-voltage EELS which is another step towards a knock-on damage free analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".