Nuclear Magnetic Resonance Spectroscopy Analysis of Anaerobic Microbial Metabolic Response to Benzalkonium Chloride Disinfectant
Bibliographic record
Abstract
Quaternary ammonium compounds (QACs) are disinfection agents used in industrial cleaning processes that are known to interfere with the proper functioning of anaerobic waste digestion directly impacting the quality and quantity of the biogas produced (i.e., CO2 and CH4). While the impact of these contaminants on waste digestors are well known, the impact these compounds have on the metabolic profile of an anaerobic digestor is less understood. This paper describes the use nuclear magnetic resonance (NMR) spectroscopy as a non-targeted tool to monitor variations in the metabolic profile of anaerobic bioreactor microcosms simulating the treatment of food production wastewater exposed to benzalkonium chloride (BAC), a key QAC. Using NMR, the variation in the metabolic profile of these wastewater microcosms is compared to variations in the quality and quantity of the biogas produced. A clear development of propionic, isobutyric, isovaleric, and other volatile fatty acids (VFAs) is observed indicating a disruption to the overall ability of the system to convert fatty acids to methane. The ability of NMR to successfully identify the overall metabolic profile, the occurrence of the individual VFAs, and the occurrence of BAC itself in one analysis helps to provide valuable information on the metabolic pathways involved in the disruption of these anaerobic processes.
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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) | 0.000 | 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 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".