Combined Effects of Surface Roughness, Solubility Parameters, and Hydrophilicity on Biofouling of Reverse Osmosis Membranes
Bibliographic record
Abstract
The fouling of protein on the surface of reverse osmosis (RO) membranes is a surface phenomenon strongly dependent on the physical and chemical characteristics of both the membrane surface and the foulant molecule. Much of the focus on fouling mitigation is on the synthesis of more hydrophilic membrane materials. However, hydrophilicity is only one of several factors affecting foulant attachment. A more systematic and rationalized methodology is needed to screen the membrane materials for the synthesis of fouling-resistant materials, which will ensure the prevention of the accumulation of foulants on the membrane surfaces, avoiding the trial and error methodology used in most membrane synthesis in the literature. If a clear correlation is found between various membrane surface properties, in combination or singly, and the amount of fouling, this will facilitate the establishment of a systematic strategy of screening materials and enhance the selection of membrane materials and therefore will reflect on the efficiency of the membrane process. In this work, eight commercial reverse osmosis membranes were tested for bovine serum albumin (BSA) protein fouling. The work here focused on three surface membrane properties: the surface roughness, the water contact angle (hydrophilicity), and finally the Hansen solubility parameter (HSP) distance between the foulant understudy (BSA protein) and the membrane surface. The HSP distance was investigated as it represented the affinities of materials to each other, and therefore, it was believed to have an important contribution to the tendency of foulant to stick to the surface of the membrane. The results showed that the surface roughness and the HSP distance contributed to membrane fouling more than the hydrophilicity. We recommend taking into account the HSP distance between the membrane material and foulants when selecting membrane materials.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".