Kinetics of hydrogen‐dependent denitrification under varying pH and temperature conditions
Bibliographic record
Abstract
It is important to determine the effect of changing environmental conditions on the microbial kinetics for design and modeling of biological treatment processes. In this research, the kinetics of nitrate and nitrite reduction by autotrophic hydrogen-dependent denitrifying bacteria and the possible role of acetogens were studied in two sequencing batch reactors (SBR) under varying pH and temperature conditions. A zero order kinetic model was proposed for nitrate and nitrite reduction and kinetic coefficients were obtained at two temperatures (25 +/- 1 and 12 +/- 1 degrees C), and pH ranging from 7 to 9.5. Nitrate and nitrite reduction was inhibited at pH of 7 at both temperatures of 12 +/- 1 and 25 +/- 1 degrees C. The optimum pH conditions for nitrate and nitrite reduction were 9.5 at 25 +/- 1 degrees C and 8.5 at 12 +/- 1 degrees C. Nitrate and nitrite reduction rates were compared, when they were used separately as the sole electron acceptor. It was shown that nitrite reduction rates consistently exceeded nitrate reduction rates, regardless of temperature and pH. The observed transitional accumulation of nitrite, when nitrate was used as an electron acceptor, indicated that nitrite reduction was slowed down by the presence of nitrate. No activity of acetogenic bacteria was observed in the hydrogenotrophic biomass and no residual acetate was detected, verifying that the kinetic parameters obtained were not influenced by heterotrophic denitrification and accurately represented autotrophic activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".