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Record W2037534848 · doi:10.2166/wst.2013.730

Microsensor determination of multiple microbial processes in an oxygen-based membrane aerated biofilm

2013· article· en· W2037534848 on OpenAlexaff
Shuying Tan, Tong Yu, Hanchang Shi

Bibliographic record

VenueWater Science & Technology · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnoxic watersBiofilmAerationSulfateNitrificationDenitrificationChemistryOxygenReduction potentialNitrogenRedoxMembraneEnvironmental chemistryEnvironmental engineeringInorganic chemistryEnvironmental scienceBiochemistryBacteriaOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Microsensor techniques were used to investigate in situ the simultaneous occurrence of sulfate reduction and nitrogen removal in a membrane aerated biofilm reactor. H2S, O2, pH, ORP, NH4(+) and NO3(-) microsensors were fabricated and used to measure the profiles inside the membrane aerated biofilm. Production and consumption rates of H2S, O2, NH4(+) and NO3(-) were estimated using corresponding concentration profiles. The results showed that in anoxic zone, located from the interface between biofilm and bulk liquid to about 550 μm below the interface, both sulfate reduction and denitrification occurred. Highest H2S production rates (around 0.27 mg L(-1)s(-1)) were found about 400 to 450 μm below the interface. Below the anoxic zone, an aerobic zone was present. High H2S oxidation activity occurred at around 550-700 μm below the interface. High oxygen consumption rates (0.34 mg L(-1)s(-1)) occurred at around 750-900 μm below the interface. Nitrification activity occurred at about 500-650 μm below the interface. Along the entire biofilm depth, pH changed slightly (within 0.2 unit). Near the interface of the aerobic and anoxic zone, there was a drastic redox potential change. These results demonstrated simultaneous sulfate reduction and nitrogen removal in a piece of membrane aerated biofilm.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.085
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.226
Teacher spread0.216 · 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 teacher head, 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

Citations12
Published2013
Admission routes1
Has abstractyes

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