Citric acid facilitates diisopropylamine separation from water: A potential solution for groundwater remediation
Bibliographic record
Abstract
Diisopropylamine (DIPA) is used in various industrial processes, such as the Sulfinol™ process to remove acidic components from oil and gas, and in the production of pesticides. It has relatively high solubility in water (≈100 g/L) and is found as a contaminant in groundwater. This study uses for the first time natural citric acid (CA) to purify water contaminated with DIPA with low energy costs. CA leads to the bulk separation of DIPA from concentrated aqueous mixtures, as demonstrated using attenuated total reflectance–Fourier transform infrared spectroscopy. Therefore, it offers a potential emergency response in the case of large spills. CA also enhances the volatilization of DIPA from aqueous solutions, as demonstrated using nuclear magnetic resonance. Therefore, it also offers a potential approach to facilitate stripping of DIPA from water in pump and treat, where groundwater is extracted, treated at the surface and reinjected. These findings suggest that CA can serve as a sustainable and effective tool to treat DIPA contamination. • Citric acid decreases the miscibility of diisopropylamine in water • Other carboxylic acids also decrease the miscibility of diisopropylamine in water • Citric acid enhances diisopropylamine volatilization from water • Citric acid can be used for the remediation of water contaminated by diisopropylamine
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".