Citric acid facilitates diisopropylamine separation from water: A potential solution for groundwater remediation
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
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
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.
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 it