Examination of Extracellular Polymer (EPS) Extraction Methods for Anaerobic Membrane Bioreactor (AnMBR) Biomass
Bibliographic record
Abstract
Membrane bioreactor fouling is a complex process, which is typically driven by extracellular polymeric substances (EPS), a complex mixture of polysaccharides, proteins, lipids, humic substances, and other intercellular polymers. While much is known about fouling in aerobic membrane reactors, far less is known about fouling in anaerobic membrane bioreactors (AnMBR). Much of this knowledge, including EPS extraction methods, has been extrapolated from aerobic processes and is commonly assumed to be comparable. Therefore, several extraction methods commonly used for aerobic EPS quantification, including ultrasonication, ethylenediaminetetraacetic acid (EDTA), and formaldehyde plus sodium hydroxide (CH2O+NaOH), were evaluated to determine the most suitable extraction method for EPS of anaerobic microorganisms in an AnMBR. To maximize EPS yields, each extraction was performed four times. Experimental results showed that the EDTA method was best for EPS quantification, based on chemical oxygen demand (COD), dissolved organic carbon (DOC), and protein yields: 1.43 mg COD/mg volatile suspended solids (VSS), 0.14 mg DOC/mg VSS, and 0.11 mg proteins/mg VSS. In comparison, the CH2O+NaOH method maximized the extraction of carbohydrates (0.12 mg carbohydrates/mg VSS). However, multiple extraction cycles with EDTA and ultrasonication exhibited lower extracellular adenosine triphosphate (ATP) concentrations compared to CH2O+NaOH extractions, indicating lower levels of released intracellular substances. Successive EPS extractions over four cycles are better able to quantify EPS from anaerobic microorganisms, since a single extraction may not accurately reflect the true levels of EPS contents in AnMBRs, and possibly in other 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".