Nanodiamond-Enabled Thin-Film Nanocomposite Polyamide Membranes for High-Temperature Water Treatment
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Bibliographic record
Abstract
Despite growing demands for high-temperature wastewater treatment, most available polymeric membranes are limited to mild operating temperatures (<50 °C) and become less efficient at high temperatures. Herein we show how to make thermally stable reverse osmosis thin-film nanocomposite (TFN) membranes by embedding nanodiamond (ND) particles. Polyamide composite layers containing different loadings of surface-modified ND particles were synthesized through interfacial polymerization. The reactive functional groups and the hydrophilic surface of the NDs intensified the interactions of the nanoparticles with the polymer matrix and increased the surface wettability of the TFN membranes. Contact angle measurement showed a maximum decrease from 88.4° for the pristine membrane to 58.3° for the TFN membrane fabricated with 400 ppm ND particles. The addition of ND particles and ethyl acetate created larger surface features on the polyamide surface of TFN membranes. The average roughness of the membranes increased from 108.4 nm for the pristine membrane to 177.5 nm for the TFN membrane prepared with highest ND concentration. The ND-modified TFN membranes showed a higher pure water flux (up to 76.5 LMH) than the pristine membrane (17 LMH) at ambient temperature at 220 psi and room temperature. The TFN membrane with the highest loading of ND particles overcame the trade-off relation between the water flux and NaCl rejection with 76.5 LMH and 97.3% when 2000 ppm of NaCl solution was filtered at 220 psi. Furthermore, with increasing ND concentration, the TFN membrane showed a lower flux decline at high temperatures over time. The TFN400 prepared with 400 ppm of m-phenylene diamine functionalized ND particles had a 13% flux decline over a 9 h filtration test at 75 °C. This research provides a promising path to the development of high-performance TFN membranes with enhanced thermal stability for the treatment of wastewaters at high temperatures.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it