Reversible Absorption of Volatile Organic Compounds by Switchable-Hydrophilicity Solvents: A Case Study of Toluene with <i>N</i>,<i>N</i>-Dimethylcyclohexylamine
Bibliographic record
Abstract
Abstract Absorption is one of the most important treatment technologies for the removal of volatile organic compounds (VOCs) from tail gases, yet the separation of the absorbents and VOCs remains challenging because of concerns related to environmental impact and large energy requirements. Herein, we explored an absorption and desorption process using N,N-dimethylcyclohexylamine (CyNMe2) as a representative switchable-hydrophilicity solvent (SHS) and toluene as a representative VOC. The results showed that in comparison to common absorbents, CyNMe2 exhibits excellent toluene absorption performance. Desorption efficiencies of toluene from CyNMe2 of up to 94% were achieved by bubbling CO2 at 25 °C, and separation efficiencies of CyNMe2 from water up to 90% were achieved by bubbling N2 at 60 °C. Even after five absorption–desorption cycles, the toluene absorption capacity of CyNMe2 was comparable with that of the fresh absorbent, suggesting that CyNMe2 retains its absorption capacity. We demonstrate an innovative and reversible remediation strategy of VOCs based on SHSs, and the results indicate that SHSs can be used as an alternative to common absorbents for the removal of VOCs to reduce environmental pollution and energy consumption.
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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 | 0.000 | 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".