Enrichment of H<sub>2</sub><sup>17</sup>O from Tap Water, Characterization of the Enriched Water, and Properties of Several<sup>17</sup>O-Labeled Compounds
Bibliographic record
Abstract
A low-abundance form of water, H(2)(17)O, was enriched from 0.04% to ∼90% by slow evaporation and fractional distillation of tap water. The density and refractive index for H(2)(17)O are reported. Gas chromatography-mass spectrometry (GC-MS) of (16)O- and (17)O-1-hexanols and their trimethyl silyl ethers and of (16)O- and (17)O-hexamethyl disiloxanes was used to determine the percentage of (17)O enrichment in the H(2)(17)O. Furthermore, the chemical shifts of labeled and nonlabeled water dissolved in CDCl(3) differed sufficiently that we could verify the enrichment of H(2)(17)O. (17)O hexanol was synthesized by the reaction of iodohexane with Na(17)OH. (17)O-Labeled trimethylsilanol and (17)O-labeled hexamethyldisiloxane were prepared by the reaction of H(2)(17)O with bis(trimethylsilyl)trifluoroacetamide (BSTFA). To generate standards for (17)O NMR, H(2)(17)O(2), and (17)O camphor were prepared. H(2)(17)O was electrolyzed to form (17)O-labeled hydrogen peroxide which was quantified using two colorimetric assays. (17)O-Labeled camphor was prepared by exchanging the ketone oxygen of camphor using H(2)(17)O. The (17)O-labeled compounds were characterized using (17)O, (1)H, and (13)C NMR and GC-MS. While we were characterizing the labeled camphor, we also detected an unexpected oxygen exchange reaction of primary alcohols, catalyzed by electrophilic ketones such as camphor. The reaction is a displacement of the alcohol OH group by water. This is an example of the usefulness of (17)O NMR in the study of a reaction mechanism that has not been noticed previously.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".