Applications of auger electron spectroscopy in the chemical state analysis of copper and its oxides
Bibliographic record
Abstract
Electron-induced Auger electron spectroscopy was successfully applied to differentiate copper species, such as metallic Cu, Cu 2 O, CuO, and Cu(OH) 2 . To do so, high-quality standard spectra were collected and their key parameters, including peak positions, widths, intensity ratios, and peak shapes, were examined to evaluate their effectiveness in chemical state identification. Average values and standard deviations were reported for each parameter. The results reveal sufficient spectral differences among Cu metal, Cu 2 O, CuO, and Cu(OH) 2 to enable chemical fingerprinting, though no single parameter provided information sufficient for accurate speciation. Instead, a combination of spectral features must be used to enable reliable identification of these species. This approach was successfully applied to identify the chemical states of oxide layers formed on two different copper samples. The results from the Auger analyses were consistent with those from XPS measurements. This methodology was also applied to determine the presence of Cu 2 O oxide inclusions in cold sprayed copper, and the results were consistent with previously published results from TEM, EELS, and electron diffraction studies. Compared to these techniques, Auger electron spectroscopy requires minimal sample preparation and offers high spatial and surface sensitivity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".