Incorporation of Functionalized Halloysite Nanotubes (HNTs) into Thin-Film Nanocomposite (TFN) Nanofiltration Membranes for Water Softening
Bibliographic record
Abstract
Incorporating nanoparticles (NPs) into the selective layer of thin-film composite (TFC) membranes is a common approach to improve the performance of the resulting thin-film nanocomposite (TFN) membranes. The main challenge in this approach is the leaching out of NPs during membrane operation. Halloysite nanotubes (HNTs) modified with the first generation of poly(amidoamine) (PAMAM) dendrimers (G1) have shown excellent stability in the PA layer of TFN reverse-osmosis (RO) membranes. This study explores, for the first time, using these NPs to improve the properties of TFN nanofiltration (NF) membranes. Membrane performance was evaluated in a cross-flow nanofiltration (NF) system using 3000 ppm aqueous solutions of MgCl2, Na2SO4 and NaCl, respectively, as feed at 10 bar and ambient temperature. All membranes showed high rejection of Na2SO4 (around 97–98%) and low NaCl rejection, with the corresponding water fluxes greater than 100 L m−2 h−1. The rejection of MgCl2 (ranging from 82 to 90%) was less than that for Na2SO4. However, our values are much greater than those reported in the literature for other TFN membranes. The remarkable rejection of MgCl2 is attributed to positively charged HNT-G1 nanoparticles incorporated in the selective polyamide (PA) layer of the TFN membranes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".