Denitrification Biokinetics: Towards Optimization for Industrial Applications
Bibliographic record
Abstract
Denitrification is a microbial process that converts nitrate (NO 3 – ) to N 2 and can play an important role in industrial applications such as souring control and microbially enhanced oil recovery (MEOR). The effectiveness of using NO 3 – in souring control depends on the partial reduction of NO 3 – to nitrite (NO 2 – ) and/or N 2 O while in MEOR complete reduction of NO 3 – to N 2 is desired. Thauera has been reported as a dominant taxon in such applications, but the impact of NO 3 – and NO 2 – concentrations, and pH on the kinetics of denitrification by this bacterium is not known. With the goal of better understanding the effects of such parameters on applications such as souring and MEOR, three strains of Thauera (K172, NS1 and TK001) were used to study denitrification kinetics when using acetate as an electron donor. At low initial NO 3 – concentrations (∼1 mmol L –1 ) and at pH 7.5, complete NO 3 – reduction by all strains was indicated by non-detectable NO 3 – concentrations and near-complete recovery (> 97%) of the initial NO 3 -N as N 2 after 14 days of incubation. The relative rate of denitrification by NS1 was low, 0.071 mmol L –1 d –1 , compared to that of K172 (0.431 mmol L –1 d –1 ) and TK001 (0.429 mmol L –1 d –1 ). Transient accumulation of up to 0.74 mmol L –1 NO 2 – was observed in cultures of NS1 only. Increased initial NO 3 – concentrations resulted in the accumulation of elevated concentrations of NO 2 – and N 2 O, particularly in incubations with K172 and NS1. Strain TK001 had the most extensive NO 3 – reduction under high initial NO 3 – concentrations, but still had only ∼78% of the initial NO 3 -N recovered as N 2 after 90 days of incubation. As denitrification proceeded, increased pH substantially reduced denitrification rates when values exceeded ∼ 9. The rate and extent of NO 3 – reduction were also affected by NO 2 – accumulation, particularly in incubations with K172, where up to more than a 2-fold rate decrease was observed. The decrease in rate was associated with decreased transcript abundances of denitrification genes ( nirS and nosZ ) required to produce enzymes for reduction of NO 2 – and N 2 O. Conversely, high pH also contributed to the delayed expression of these gene transcripts rather than their abundances in strains NS1 and TK001. Increased NO 2 – concentrations, N 2 O levels and high pH appeared to cause higher stress on NS1 than on K172 and TK001 for N 2 production. Collectively, these results indicate that increased pH can alter the kinetics of denitrification by Thauera strains used in this study, suggesting that liming could be a way to achieve partial denitrification to promote NO 2 – and N 2 O production (e.g., for souring control) while pH buffering would be desirable for achieving complete denitrification to N 2 (e.g., for gas-mediated MEOR).
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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 | 0.001 |
| 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 | 0.001 | 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".