Freezing To Preserve Groundwater Samples and Improve Headspace Quantification Limits of Water-Soluble Organic Contaminants for Carbon Isotope Analysis
Bibliographic record
Abstract
Few systematic investigations have addressed the use of freezing for applications in analytical chemistry. Here, we tested its potential to preserve groundwater samples and to improve headspace quantification limits for compound-specific isotope analysis. Analysis of compound concentrations, as well as stable carbon isotope ratios, confirmed that trichloroethene was preserved in frozen suspensions of nanoscale zerovalent iron. In contrast, storage at 7 degrees C was ineffective, and complete degradation of TCE occurred in 4 weeks. Hence, freezing may stop even abiotic chemical reactions that would not be prevented by cooling or traditional preservation agents. In the absence of iron, we found that headspace concentrations of 14 organic contaminants were considerably higher over frozen solutions than at 25 degrees C, likely reflecting a freezing-out effect governed by Raoult's law. The observed enhancement depended on the salinity of the samples and was strongest for water-soluble, volatile compounds (values in brackets indicate the minimum observed effect out of six replicates): tert-butyl alcohol (TBA, 35-fold), methyl tert-butyl ether (MTBE, 14-fold), 1,2-dichloroethane (10-fold), or benzene (7-fold). In contrast, little enhancement was observed for less water-soluble compounds, such as tetrachloroethene. Although standard deviations of the measurements were too high for the method to be used for quantitative analysis of total compound concentrations, since we found that freezing introduces no measurable carbon isotope effect for TBA, MTBE, 1,2-dichloroethane, and benzene, the method is an effective way of increasing the sensitivity of compound-specific isotope analysis, particularly of water-soluble organic contaminants.
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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".