Characterization of a Spherical Packing Medium for Biological Nitrogen Removal by Microelectrodes
Bibliographic record
Abstract
A novel packing medium comprised of a spherical net-like shell, plastic stuffing, and a perforated hollow duct for biological reactors was investigated. In this study, we examined a biofilm process that employed the packing medium for combined aerobic carbonaceous substances removal, nitrification, and denitrification. As a result, we were able to obtain the average of nitrification, denitrification, as well as the chemical oxygen demand (COD) removal capacities, which correspond to 0.2 kg N/m3 · day, 0.3 kg N/m3 · day, and 5.56 kg/m3 · day, respectively. Moreover, the spatial concentration distribution of chemical species inside the packing medium was studied, using microelectrodes for dissolved oxygen, NH4+, NO3−, and redox potential. Clear evidence was found that aerobic/anoxic spatial stratification was formed inside the packing medium, and that due to microbial oxygen utilization, the aerobic zone was limited to a surface layer of 12 mm, which had a corresponding decrease in redox potentials from 157.4 mV to −99.5 mV. It was also found that nitrification dominated in the top 4 mm of the surface layer, whereas denitrification occurred in the deeper part of the packing medium. Furthermore, a gap zone only for aerobic heterotrophic oxidation was found to be present between nitrification and denitrification. This implied that a high C/N ratio could contribute to a decrease in the effective size of the packing medium. Based on the measured DO and NH4+ depth profiles under steady-state conditions, the effective diffusion coefficients of DO and NH4+ in the packing medium were calculated to be 0.0172 m2/day and 9.7 × 10−5m2/day, respectively. The turbulent diffusion of oxygen ensured the sufficient oxygen supply not only for aerobic carbon oxidation but for nitrification inside the packing medium as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".