Lab-simulated downhole leaching of formaldehyde from proppants by high performance liquid chromatography (HPLC), headspace gas chromatography-vacuum ultraviolet (HS-GC-VUV) spectroscopy, and headspace gas chromatography-mass spectrometry (HS-GC-MS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ability of different methods to analyze formaldehyde and other leachates from proppants was investigated under lab-simulated downhole conditions. These methods include high performance liquid chromatography (HPLC), headspace gas chromatography-vacuum ultraviolet spectroscopy (HS-GC-VUV), and headspace gas chromatography-mass spectrometry (HS-GC-MS). Two different types of resin-coated proppants, phenol-formaldehyde- and polyurethane-based, were examined. Each proppant was tested at different time intervals (1, 4, 15, 20, or 25 hours) to determine the timeframe for chemical dissolution. Analyses were performed at room temperature and heated (93 °C) to examine how temperature affected the concentration of leachates. Multiple matrices were examined to mimic conditions in subsurface environment including deionized water, a solution surrogate to mimic the ionic concentration of produced water, and recovered produced water. The complexity of these samples was further enhanced to simulate downhole conditions by the addition of shale core. The influence of matrix components on the analysis of formaldehyde was greatly correlated to the quantity of formaldehyde measured. Of the three techniques surveyed, HS-GC-MS was found to be better suited for the analysis of formaldehyde leachates in complex samples. It was found that phenol-formaldehyde resin coated proppants leached higher concentrations of formaldehyde than the polyurethane resin coated proppants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 it