Degradable cationic clarifiers based on ester bonds for treating wastewater produced from polymer flooding
Bibliographic record
Abstract
Abstract Polymer‐contained oily sludge is inevitably produced when the oily wastewater produced from polymer flooding (OWPF) is treated by a cationic polymer clarifier, and it severely affects oilfield production. This study designed a novel degradable cationic clarifier (DCC) based on the ester bond and prepared it in light of the poly(β‐amino ester) used in the area of generic material delivery. First, the copolymer of N ‐[3‐(dimethylamino) propyl] methacryamide (DMAPMA) and diallylamine (PDMAPMA‐NH) were synthesized by radical copolymerization. Then, the DCC was prepared by Michael addition between PDMAPMA‐NH and poly(ethylene glycol) diacrylate. The synthesis conditions of DCC were investigated, and a DCC with excellent flocculant performance and good degradability was successfully prepared. At 70°C, both the degradations of DCC and the water‐insoluble polymer complex formed by DCC and the polymer used for polymer flooding can be completed in 10 h, which is beneficial for the self‐disappearance of the polymer‐contained oily sludge. The flocs formed by DCC were observed at different times, and the results confirmed that the flocs can change to an oil film and that the polymer‐contained oily sludge cannot exist for a long time. This study provides inspiration for OWPF treatment and expands the new application for poly(β‐amino ester).
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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".