Fabrication of superhydrophilic membranes for oil-water separation: A life cycle assessment study
Bibliographic record
Abstract
Membrane-based technologies are widely used in oily wastewater treatment. This study selects two superhydrophilic ultrafiltration (UF) membranes (denoted M1 and M2) for oil-in-water emulsion separation and evaluates the environmental impact of membrane fabrication using life cycle assessment (LCA). Although the two membranes have similar separation performance, M1 exhibits ~40% lower environmental impacts than M2 in almost every category owing to its fewer modification steps, lower electricity use, and less solvent consumption. Electricity consumption, reactive-copolymer synthesis, and toxic-solvent use are identified as environmental hotspots in membrane fabrication. A sensitivity analysis of different energy sources reveals that coal-based electricity has the greatest environmental impact, while photovoltaic energy reduces the impact by up to 71%. Considering solvents, dimethylformamide (DMF) shows a slightly lower environmental impact than N-methyl-2-pyrrolidone (NMP).
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 teacher head, 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".