Subtractive Engineering of Polymer Cladding Induces Tunable UV-C Irradiation from Flexible Side-Emitting Optical Fibers for Biofilm Control in Curved Piping
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Bibliographic record
Abstract
Germicidal ultraviolet (UV-C) light effectively inhibits the growth of pathogenic biofilms in water systems. However, delivering UV-C light from point sources (e.g., lamps) to complex wetted surfaces poses challenges. This study introduces a technology using flexible side-emitting optical fibers (SEOFs) that are <550 μm in diameter and emit UV-C light along their entire length. The SEOFs are designed to fit into tubing with narrow diameters and inhibit biofilm growth from forming on the tubing walls. First, we demonstrate factors influencing the tunability of the irradiance from the SEOFs. To enable flexibility and strength, UV-C-transparent polymers were coated onto the surfaces of glass optical fibers, allowing for the side emission of UV-C light. By modulating the surface roughness of the polymers using a scalable subtractive engineering approach, the extent of side-emitted UV-C light could be controlled, ranging from <5 to over 50 μW/cm 2 perpendicular to the flexible fibers. This novel subtractive engineering approach surpasses our previous work in terms of its robustness and tunability, as it no longer relies on nanoparticles and polymers to coat the optical fibers. Second, the effectiveness of the UV-C SEOFs in controlling biofilms produced by Pseudomonas aeruginosa was evaluated inside flexible tubing with a recirculating nutrient-rich and planktonic bacteria solution. Without SEOFs, biofilms grew inside on the tubing surfaces. SEOFs with an intermediate polymer surface roughness achieved a >2 log reduction in the biofilm (<100 CFU/cm 2 ) on the interior surface of the tubing. This subtractive engineering approach provides tunability in side-emitted UV-C light and offers a scalable manufacturing strategy as a promising solution for inhibiting biofilms on complex wetted surfaces.
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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 it