Effect of pH on fouling attachments and power consumption in ultrafiltration of latex solution
Bibliographic record
Abstract
The goal of the present study was to remediate ultrafiltration fouling through the altering of the latex paint solution pH, in order to reduce the total mass fouling and the specific power consumption. Polycarbonate flat membrane with a pore size of 0.05 µm was used under a constant feed flow rate and cross‐flow mode in ultrafiltration of a latex paint solution. It was observed that the ionic strength of the latex solution had a significant effect on the membrane hydrophilicy and the particle surface charge, which in turn influenced the particle–particle and particle–membrane attachment. At the transmembrane pressure of 15 psi, a feed flow rate of 1 LPM, and a feed concentration of 1.3 kg/m3, increasing solution pH from 7 to 12 resulted in a considerable 50% reduction in membrane fouling. This reduction of fouling led to a 53.9% decrease in the specific power consumption, while the permeate flux increased by 97.4%. On the other hand, the mass of fouling noticeably decreased by 35.8%, the permeate flux increased by 64.5% and the specific power consumption dramatically increased by 127.5%, when the feed flow rate was increased from 1 to 4 LPM at the solution pH of 7. Alternatively, when the feed flow rate and the solution pH were increased simultaneously, the mass of fouling was reduced by 56.8%, with an improvement in the specific power consumption augmented only by 54.1%, while the permeate flux increased by 136.84%.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".