Can we detect Li K X‐ray in lithium compounds using energy dispersive spectroscopy?
Bibliographic record
Abstract
Summary Lithium is the key element for the development of battery and new technology and the development of an analytical technique to spatially and quantitatively resolve this element is of key importance. Detection of Li K in pure metallic lithium is now possible in the Scanning Electron Microscope (SEM) with newly designed Energy Dispersive Spectroscopy (EDS). However, this work is clearly showing, for the first time using EDS, the detection of Li K in several binary lithium compounds (LiH, Li 3 N, Li 2 S, LiF and LiCl). Experimental Li K X‐rays intensity is compared with a specially modified Monte Carlo simulation program showing discrepancies between theoretical and experimental Li K measurements. The effect of chemical bounding on the X‐rays emission using density functional theory with the all‐electron linearized augmented plane wave is showing that the emission of Li K from the binary compounds studied should be, at least, 12 times lower than in metallic Li. SCANNING 38:571–578, 2016. © 2016 Wiley Periodicals, Inc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".