Impacts of hydration and dehydration on microfiltration point-of-use filters: performance and cleaning impacts
Bibliographic record
Abstract
Abstract This research examines the performance of two commercially available point-of-use (POU) microfiltration membrane filters (MF) under hydrated (wet) versus a multi-day dry period (dehydrated). Filter performance is monitored in terms of water quality and flowrate, as well as flowrate recovery following different cleaning regimes. The cleaning methods tested were backwashing with filtrate at room temperature, filtrate heated to 45 °C, filtrate at 45 °C with gentle shaking, and a vinegar solution (5% acetic acid). The selected cleaning methods reflect easily accessible cleaning methods with a goal to assess their impacts on flowrate recovery under both wet and dry conditions. After initial testing, hydrated MF flowrate varied between 197 ± 22 mL/min and backwashing with filtrate at room temperature was sufficient to maintain the membrane flowrate, while any of the other methods initially improved the system flowrate. In experiments where the filters were subject to a 5-day dry condition MF flowrates dropped to 65 ± 35 mL/min and filtrate at room temperature did not recover the flowrate sufficiently, however heated filtrate (45 °C) with/without gentle shaking was effective at recovering the MF for use. Water quality remained similar throughout the study, and 0 CFU/mL of E. coli were found in filtrate samples.
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) | 0.001 | 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".