Preparation of highly smooth nanofiltration membranes based on lignin nanoparticle hydrogels and study of interfacial mechanism
Bibliographic record
Abstract
Limited by the synthetic materials, traditional nanofiltration membranes have always suffered from high surface roughness due to the fast interfacial polymerization rate and the interfacial perturbation during the preparation process. Herein, a method for developing lignin nanoparticle hydrogels to replace traditional aqueous phase materials is proposed, synchronizing interfacial polymerization rate modulation and interfacial stabilization enhancement for the preparation of smooth and anti-fouling nanofiltration membranes. The surface, structural and performance of the fabricated membrane were evaluated comprehensively and compared. The atomic force microscope analysis revealed that the surface roughness of prepared membrane decreased by 63 % (Ra = 34.3 nm), while SEM analysis confirmed its thinner filtering interface (46 nm) when lignin nanoparticle-based hydrogel was incorporated into NF membrane. The surface of the fabricated membrane presented –23.67 mV zeta potential and 36.85° contact angle, also implying better anti-fouling property. Thanks to these characteristics, the developed membranes exhibited a higher fouling resistance and flux recovery ratio (FRR, 95.4 %). In addition, the mechanical strength of the prepared membranes was improved by 45 %. The density functional theory (DFT) further confirmed that the binding energy of lignin and 1,3,5-benzenetricarbonyl trichloride (TMC) was −8.92 kcal · mol −1 , which was lower than that of conventional nanofiltration membranes, facilitating the mild and controllable reaction that benefited the thin and smooth surface on the NF membrane. This study provided a new technique for fabricating a more sustainable NF membrane with improved mechanical strength, surface smoothness.
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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 | 0.001 |
| 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 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".