A New Approach to Quantify Spatial Distribution of Biofilm Kinetic Parameters by In Situ Determination of Oxygen Uptake Rate (OUR)
Bibliographic record
Abstract
In order to guide the rational optimization of biofilm reactor design and operation in wastewater treatment, it is essential to comprehend the biokinetic behavior of microorganisms, especially with its depth variation, in biofilms. However, no fast and accurate methods have been established for measuring the stratification of biokinetic parameters in biofilm systems up to now. In this paper, a new approach was presented to quantify the spatial distribution of biokinetic parameters in biofilms with a result demonstration. A multispecies biofilm at endogenous respiration was subjected to a pulse of substrates at various locations. An oxygen microelectrode structurally combined with an additional micropipette for substrate injection was constructed to monitor the in situ OUR (oxygen uptake rate) by measuring the dissolved oxygen concentration with time. Based on the oxygen concentration profiles versus times at a series of substrate concentrations, the spatial distribution of biokinetic parameters, biomassyield coefficient (YH), Monod half-saturation coefficient for substrate (KH,S), and maximum specific growth rate (muH,max) were quantified. The demonstration of its utility showed that YH was within the 0.437-0.449 g (COD in cell)/g COD range, with only a slight difference and no regular change in the spatial distribution. Meanwhile, the results showed that the distribution of muH,max varied from 9.18 day(-1) at the surface layer of the biofilm to 1.69 day(-1) at the substratum layer of the biofilm with a decrease of 80% due to the reduction of biomass activity orthe diverse distribution of microbial species. Additionally, an opposite change tendency of KH,S was found, which increased from 37 mg COD/L at the surface layer of the biofilm to 45 mg COD/L at the substratum layer of the biofilm, indicating larger mass transport limitations with penetration into the biofilm. Compared with high heterogeneity in the biofilm depth, the distribution of biokinetic parameters was less heterogeneous in the horizontal direction at the same depth. In summary,the results provide experimental evidence necessary for biofilm modeling, which could not be accomplished in the past, and enable us to obtain a clearer biokinetic description of the biofilm.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".