Cellulose nanocrystal addition in thin film nanocomposite membranes: Which monomer solution is preferred?
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are biodegradable nanoparticles with a high aspect ratio and abundant surface hydroxyl groups resulting in negatively charged hydrophilic surfaces that make them an ideal candidate to be incorporated in thin‐film nanocomposite (TFN) membranes. In this study, we modified the CNCs via acetylation (ACNCs) to reduce their hydrophilicity and via reaction with L‐cysteine (CysCNCs) to impart them with functionality that promoted their interaction with the trimesoyl chloride organic monomer used in the preparation of the poly(amide) layer of the TFN membranes. These modifications allowed us to question in which monomer solution the nanoparticles should be dispersed. Addition of the unmodified CNCs in either the aqueous or organic monomer solution showed little difference in membrane performance. However, the addition of either the ACNCs or the CysCNCs to the organic monomer solution led to a significant increase in membrane performance in reverse osmosis (RO) and nanofiltration (NF) systems compared to their addition to the aqueous monomer solution. In addition, the CysCNCs exhibited performance very near the upper‐bound line for RO and NF.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".