Microbial Purification in Well-Water Using UV-Vis LEDs and Monitoring Using Laser-Induced Fluorescence
Bibliographic record
Abstract
Well-water contaminated by microbial bacteria has been purified using LEDs radiating in the ultra-violet and visible spectral regions. The contaminated water sample was exposed to the LEDs in specially constructed chambers and the purification process carried on for 3 days. The efficiency of the process was determined using a 445 nm diode laser to induce fluorescence (LIF) and the monitoring of coliform bacteria count (Total coliform, Fecal coliform and Escherichia coli) as well as Total Heterotrophic Bacteria (THB). The LIF peak fluorescence intensities at 526 nm (Raman water peak) and dissolved organic matter fluorescence intensity at 550 nm were determined. Using the fluorescence intensity of purified drinking water as reference, a fluorescence intensity ratio was calculated. A decrease in this ratio with time, at the two wavelengths indicated a proof of purification. Using the values of the slopes, the red and green LEDs proved most efficient while the UV was the least. From the counts of coliform bacteria and THB, the light sources registered zero after the first day of purification, but counts were recorded thereafter for some of the bacterial for some light sources. This may be attributable to bio-films formation on internal surfaces of the purification chamber due to excessive temperatures. A reduction in fluorescence intensity observed in the sample stored in dark environment could be attributed to the stationary and logarithmic-decline phases of the growth curve of bacterial population. This purification technique is inexpensive and can easily be adapted for domestic water purification for reducing waterborne bacteria.
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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 | 0.000 | 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) | 0.001 | 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".