Reply to the comment by Malfait on “Spectroscopic studies of oxygen speciation in potassium silicate glasses and melts”
Bibliographic record
Abstract
Malfait challenges our XPS results by arguing that the XPS spectroscopic technique cannot be used to determine small amounts of O 2– in potassium silicate glasses. Instead, he claims that there is no free oxide (O 2– ) in potassium silicate glasses based primarily on his 29 Si MAS NMR spectroscopic results. In this rebuttal, we demonstrate that O1s XPS and well-resolved 2D 29 Si MAF NMR spectral results of potassium disilicate (K 2 Si 2 O 5 ) glass are consistent with each other and that both techniques indicate the presence of a few mol% of O 2– in the glass. Neither of these techniques, however, supports the interpretation of the 29 Si MAS NMR results presented in the comments of Malfait. The major difficulty relates to the low resolution of the 29 Si MAS NMR spectra, which does not reveal the Q 4 signal beneath a strong Q 3 peak in these spectra. The proof is provided by the 2D 29 Si MAF NMR spectrum of potassium disilicate glass in which both Q 3 and Q 4 peaks are revealed; the 2D 29 Si MAF NMR results for potassium disilicate glass are far more informative than 29 Si MAS NMR spectra. It demonstrates that the potassium disilicate glass (K 2 Si 2 O 5 ) contains greater Q 4 intensity, is more polymerized than previously considered, and that O 2– is present at ∼2 (±1) mol% in the potassic glass. This O 2– value confirms our O1s XPS results. Specific points raised by Malfait are rebutted in Appendix A .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".