Comparative Evaluation of Polyester and Polyether Polyurethane Foams for <i>Escherichia coli</i> Adsorption in Water Disinfection
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In 2022, an estimated 1.7 billion people globally lacked access to safe drinking water, with more than 100 million still relying on untreated surface water. Contamination of water with fecal bacteria can cause a variety of diseases. Many methods have been proposed and used to remove bacteria from drinking water. However, many of these methods still are not broadly used in remote areas, as they can be inefficient, require chemicals, or use complicated operational procedures. This study conducts an initial investigation of the potential of polyurethane foams for water disinfection. Specifically, we compared polyester polyurethane (PESPU) and polyether polyurethane (PU) foams with similar morphologies for Escherichia coli ( E. coli ) adsorption. The relationship between foam properties (surface charge, surface energy, and surface chemistry) and bacterial removal efficiency was investigated to identify the critical aspects for future adsorbent development. It was found that the PESPU foam achieved a bacterial log reduction of approximately 2.9 for E. coli when immersed in a solution with an initial concentration of 10 4 –10 5 CFU/mL for 1 h, whereas the PU foam showed negligible effectiveness under the same conditions. It was discovered that the PESPU foam exhibited a larger surface charge and surface energy than the PU foam, which likely contributed positively to its superior performance. This study is a step toward improving new foams employing surface charge, surface energy, and surface chemistry to improve bacterial adsorption effectiveness and provide accessible water treatment for underserved locations.
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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 | 0.001 | 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".