Enzymatically catalyzed CO <sub>2</sub> ‐H <sub>2</sub> O equilibration for oxygen isotope analyses of aqueous samples
Bibliographic record
Abstract
Rationale The classic CO 2 ‐H 2 O equilibration method is a very popular technique for the measurement of the oxygen isotope composition of aqueous samples in stable isotope geochemistry. This study examined whether enzymatically controlled CO 2 ‐H 2 O equilibration by carbonic anhydrase (CA) could reduce the time for oxygen isotope equilibrium between CO 2 and H 2 O at 25°C. Methods Four types of aqueous samples containing CA were equilibrated with CO 2 gases using a continuous flow isotope ratio mass spectrometer equipped with an automated gas sample collection device. We examined the effect of CA concentration in an aqueous sample, the influence of drying technique for the preparation of sample vials containing dried CA, the age of CA stock solution, and the ionic strength and the oxygen isotope composition of aqueous samples. Results CA rapidly catalyzed the oxygen isotope exchange between CO 2 and H 2 O and was unaffected by drying technique or stock solution age. Compared with aqueous samples with no CA or 0.2 μmolal CA, samples containing 4 μmolal CA significantly reduced the CO 2 ‐H 2 O equilibration time for deionized water and artificial seawater (ionic strength = ~0.6) from ~19 h and ~23 h to ~0.30 h and ~0.77 h, respectively at 25°C. Conclusions This enzymatically catalyzed CO 2 ‐H 2 O equilibration method is time‐efficient, cost‐effective, requires no additional data correction procedure, and can be used for most commercially available CO 2 ‐H 2 O equilibration devices without any modification.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".