Evaluating the Efficacy of Hollow Fiber Pre-Concentrator for Water Quality Monitoring
Bibliographic record
Abstract
Simple, efficient and compact concentrating systems are of prime importance to the development of portable biosensor based testing solutions for bacterial contamination in potable water. Bacteria are non-uniformly distributed in drinking water and hence testing with small sample volumes does not provide an accurate estimation. Hence bacteria have to be concentrated from large volumes of water of the order of 100 mL as recommended by United States Environmental Protection Agency (USEPA) to a few hundred microliters to accommodate within portable biosensor platforms like Lab on a Chip (LOC), paper microfluidics and micro-cantilever systems. In the present work, we have developed a simplified, rapid, handheld and field deployable concentrating module which involves filtration of contaminated water through a hollow fiber filter using tangential flow filtration and a subsequent elution step to facilitate the transfer of the concentrated mixture on to a portable biosensor platform. The process involves the collection of water sample in a 5 mL syringe. With the aid of two other syringes, the sample volume is concentrated by passing it through the hollow fiber a couple of times. For improved efficiency, bacteria recovery is performed using 1 mL of a non-ionic surfactant (Tween 20) solution as an elution fluid which is administered by another syringe. The bacteria along with the elution fluid form the required concentrated mixture. This elution strategy was found to be very efficient and the product recovery was close to 85%. With further modification to the current configuration, the system can be developed into a highly efficient pre-concentrating module compatible with any microfluidics based platform.
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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 | 0.000 |
| 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 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".