Acid–Base Polymeric Foams for the Adsorption of Micro-oil Droplets from Industrial Effluents
Bibliographic record
Abstract
Separation of toxic organic pollutants from industrial effluents is a great environmental challenge. Herein, an acid–base engineered foam is employed for separation of micro-oil droplets from an aqueous solution. In acidic or basic environments, acid–base polymers acquire surface charge due to protonation or dissociation of surface active functional groups. This property is invoked to adsorb crude oil microdroplets from water using polyester polyurethane (PESPU) foam. The physicochemical surface properties of the foam were characterized using X-ray photoelectron spectroscopy, inverse gas chromatography, electrokinetic analysis, and micro-computed tomography. Using the surface charge of the foam and oil droplets, the solution pH (5.6) for maximum separation efficacy was predicted. This optimal pH was verified through underwater wetting behavior and adsorption experiments. The droplet adsorption onto the foam was governed by physisorption, and the driving forces were attributed to electrostatic attraction and Lifshitz–van der Waals forces. The foam was regenerated and reused multiple times by simple compression. The lowest trace oil content in the retentate was 3.6 mg L –1, and all oil droplets larger than 140 nm were removed. This work lays the foundation for the development of a new class of engineered foam adsorbents with the potential to revolutionize water treatment technologies.
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 | 0.000 | 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".