Cellulose acetate ultrafiltration membranes customized with copper oxide nanoparticles for efficient separation with antifouling behavior
Bibliographic record
Abstract
Abstract Cellulose acetate (CA) nanocomposite ultrafiltration membranes are fabricated with copper oxide (CuO) nanoparticles with the aim of improving efficient protein separation and antifouling performance. CuO nanoparticles are synthesized from cupric nitrate using a wet precipitation method and characterized by FTIR and XRD. CA/CuO nanocomposite membranes fabricated using 0.5, 1.0, and 1.5 wt% of CuO nanoparticles individually by simple phase inversion technique. The CA nanocomposite membrane with 0.5 wt% of hydrophilic CuO exhibited enhanced PWF of 118.6 Lm−2 h−1 due to the improvement in porosity and water uptake. This is in good agreement with the enhanced hydrophilicity of the CA/CuO nanocomposite membranes results observed in surface contact angle and morphological investigations. Further, 95.5% of BSA separation and 94.7% of flux recovery ratio (FRR) indicates its superior antifouling potential caused due to the presence of the hydration layer at the CA/CuO membrane surface. Among all the fabricated membranes, the CA‐0.5 nanocomposite membrane with 0.5 wt% of CuO exhibited superiorly improved hydrophilicity, water permeation, BSA separation, and antifouling performance indicates its potential use in water and wastewater treatment applications.
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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) | 0.000 | 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 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".