Benchtop <sup>19</sup>F NMR spectroscopy as a practical tool for testing of remedial technologies for the degradation of perfluorooctanoic acid, a persistent organic pollutant
Bibliographic record
Abstract
Abstract The development of effective remedial technologies for the destruction of environmental pollutants requires the ability to clearly monitor degradation processes. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for understanding reaction progress; however, practical considerations often restrict the application of NMR spectroscopy as a tool to better understand the degradation of environmental pollutants. Chief among these restrictions is the limited access smaller environmental research labs and remediation companies have to suitable NMR facilities. Benchtop NMR spectroscopy is a low‐cost and user‐friendly approach to acquire much of the same information as conventional nuclear magnetic resonance (NMR) spectroscopy, albeit with reduced sensitivity and resolution. This paper explores the practical application of benchtop NMR spectroscopy to understand the degradation of perfluorooctanoic acid using sodium persulfate, a common reagent for the destruction of groundwater contaminants. It is found that Benchtop 19F NMR spectroscopy is able to monitor the complete degradation of perfluorooctanoic acid into fluoride; however, the observation of intermediate degradation products formed, which can be observed using a conventional NMR spectrometer, cannot be readily distinguished from the parent compound when measurements are performed using the benchtop instrument. Under certain reaction conditions, the formation of fluorinated structures that are resistant to further degradation is readily observed. Overall, it is shown that benchtop 19F NMR spectroscopy has potential as a quick and reliable tool to assist in the development of remedial technologies for the degradation of fluorinated contaminants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".