Electro‐Conductive Ti<sub>3</sub>C<sub>2</sub> MXene Multilayered Membranes: Dye Removal and Antifouling Performance
Bibliographic record
Abstract
Abstract This work describes the fabrication of a novel electroconductive membrane made of Ti 3 C 2 T x (MXene) nanosheet coating through a one‐step pressure‐assisted technique. Ti 3 C 2 ‐MXene is firmly attached over a polyamide–imide (PAI) microfilter by employing a binder composed of carboxymethyl cellulose (CMC)/glutaraldehyde (GA). Through coating a proper amount of multilayer Ti 3 C 2 ‐MXene, the electrical conductivity of 174 ± 0.16 S m −1 is achieved. The rejection rates of reactive red 120 (RR120), reactive black (RB), and methyl orange (MO) by the pristine PAI membrane are 45.2%, 40.81%, and 33.65%, respectively. However, rejection rates significantly improve with the Ti 3 C 2 MXene coating to over 99.71%, 97.95%, and 68.91% for RR120, RB, and MO. Applying a 4 V cathodic potential resulted in a flux recovery ratio (FRR) of 99.83% and a flux decline rate (FDR) of less than 1% during humic acid (HA) filtration. Without applying voltage, the MXene‐coated membrane shows an FRR and FDR of 92.51% and 45.56%, respectively. Surface energy analysis reveals strong repulsive interactions between foulants and the membrane surface. Moreover, the surface free energy indicates that foulants such as sodium alginate (SA) and bovine serum albumin (BSA) exhibit stronger adhesion to the membrane than HA, consistent with the fouling experiment results.
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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".