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Record W3158728152 · doi:10.3389/fmicb.2021.610389

Denitrification Biokinetics: Towards Optimization for Industrial Applications

2021· article· en· W3158728152 on OpenAlexafffund
Navreet Suri, Yuan Zhang, Lisa M. Gieg, M. Cathryn Ryan

Bibliographic record

VenueFrontiers in Microbiology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsDenitrificationNitrateMicrobial enhanced oil recoveryChemistryNitriteIncubationKineticsEnvironmental chemistryBacteriaMicroorganismBiologyBiochemistryNitrogenOrganic chemistry

Abstract

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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).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.220
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2021
Admission routes2
Has abstractyes

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