Determination of Stable Hydrogen Isotopic Composition and Isotope Enrichment Factor at Low Hydrogen Concentration
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Determination of stable hydrogen isotopic compositions (δ 2 H) is currently challenged to achieve a high detection limit for reaching the linear range where δ 2 H values are independent of concentration. Therefore, it is difficult to assess precise δ 2 H values for calculating the hydrogen isotope enrichment factor (ε H ) and for field application where the concentrations of contaminants are relatively low. In this study, a data treatment approach was developed to obtain accurate δ 2 H values below the linear range. The core concept was to use a logarithmic function to fit the δ 2 H values below the linear range and then adjust the δ 2 H values below the linear range into the linear range by using the fitted logarithmic equation. Moreover, the adjusted δ 2 H values were calibrated by using laboratory reference materials, e.g., n -alkanes. Tris(2-chloroethyl) phosphate (TCEP) and hexachlorocyclohexane (HCH) isomers were selected as examples of complex heteroatom-bearing compounds to develop the data treatment approach. This data treatment approach was then tested using δ 2 H values from a TCEP transformation experiment with OH radicals. Comparable δ 2 H values and ε H between the low-concentration experiment and the reference experiment were obtained using the developed approach. Therefore, the developed data treatment approach enables a possibility of determining the hydrogen isotopic compositions of organic components in low concentrations. It is especially valuable for determining organic contaminants in environmental samples, which are usually present in low concentrations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".