Application and refinement of fluorescence spectroscopy for organic matter characterization in drinking water
Bibliographic record
Abstract
This research examined the use and advancement of fluorescence spectroscopy as an organic characterization method in drinking water treatment, providing novel insight into the performance of and fundamental mechanisms of water treatment processes. Using fluorescence spectroscopy coupled with analysis techniques including parallel factors analysis (PARAFAC) and peak shifts, biofiltration was found to have variable impact on individual fluorophores. The fluorescence method identified production of humic-like matter by the microbial communities, ultimately resulting in a unique treated organic character of the treated water. Through correlations with formation potentials of halogenated furanones, polysaccharides were identified as possible precursors. Pre-oxidation, was suggested to result in increased proportionality of carbonyl-containing functional groups and greater carbon oxidative state. A continuous fluorescence system was developed as part of this research and implemented in two studies focused on fouling mitigation of ultrafiltration (UF) membranes. A full-scale study was conducted that continuously monitored membrane feed water organic character. Utilizing the continuous fluorescence, improved prediction accuracy of membrane fouling was found using a neural network approach. A second study, conducted at bench-scale focused on understanding the role of organic surface changes and irreversible fouling potential. Low coagulant doses (
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".