Recent Progresses in Preparation and Characterization of RO Membranes
Bibliographic record
Abstract
Reverse osmosis (RO) is a water purifcation technology that uses a semipermeable membrane to remove ions, molecules, and larger particles for the production of drinking water. The frst RO membrane for seawater desalination, wastewater treatment and other applications were made of cellulose acetate. But, the polyamide thin-flm composite membrane that can tolerate wide pH ranges, higher temperatures, and harsh chemical environments is the most popular, currently. To further improve the membranes’ performances, the recent trend in polymer-based membrane research has been focused to investigate various types of nanocomposite membranes, in which nanosized fllers such as SMCNT, MWCNT, graphene, graphene oxide, silica, or zeolite are incorporated. However, there are many challenges to commercialize the application of these membranes. Nowadays, it is a norm to characterize membranes by the advanced characterization techniques such as Fourier transform infrared spectroscopy (ATR-FTIR), scanning electron microscope (SEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Raman spectroscopy and others for studying the physical and chemical properties of membranes and to co-relate those properties to the performances of the membranes. In this work, different aspects of RO membranes and proposed characterization methods, as well as recent progresses have been reviewed, comprehensively.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".