Research Update: Liquid gated membrane filtration performance with inorganic particle suspensions
Bibliographic record
Abstract
Membrane filtration technology is widely used across several industries. But its efficiency is plagued by fouling, which ultimately deteriorates the membrane’s performance. This paper provides a research update on the biologically inspired liquid-enabled gating mechanism that acts as a novel filtration and separation approach offering reduction in transmembrane pressure (TMP), improved throughput, and reduced fouling. We study the performance of such Liquid Gated Membranes (LGMs) and present their benefits for filtration in the presence of model inorganic (nanoclay particles) fouling. We show over twofold higher throughput, nearly threefold longer time to foul, more than 60% reduction in irreversible fouling, ability to return to baseline pressures after backwashing along with reduction in use of backwash water, and 10%-15% reduction in TMP for filtration of nanoclay particles. Fouling models exhibit not only delayed onset of fouling for LGMs compared to the control but also different fouling characteristics. These results demonstrate the potential of the liquid gating mechanism, which can lead to breakthroughs in membrane technology applications in particle filtration, microfiltration, and ultrafiltration.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".