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Record W4387534058 · doi:10.1002/cjce.25114

Real‐time microbial growth monitoring by combining microbial fuel cell‐based device with modified Nernst equation

2023· article· en· W4387534058 on OpenAlexaffvenue
Siyang Shen, Yen‐Han Lin, Chen‐Guang Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNernst equationMicrobial fuel cellBiological systemExponential growthExponential functionBacterial growthComputer scienceControl theory (sociology)Process engineeringBiochemical engineeringChemistryMathematicsElectrodeEngineeringControl (management)Mathematical analysisArtificial intelligenceBiologyPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract In this work, we demonstrate a novel design that integrates a modified Nernst equation and readings from a microbial fuel cell (MFC)‐based device, facilitating real‐time monitoring of microbial growth. The MFC‐based device is comprised of an H‐shaped double‐chamber MFC, specifically designed to incorporate an oxidation–reduction potential (ORP) sensor, allowing for simultaneous measurements of both parameters. The Nernst equation was adjusted to assimilate readings from both the ORP sensor and the MFC device, ultimately deriving a unitless curve that represents the online dynamics of microbial growth. This curve exhibits two distinct peaks: the first peak indicates the initiation of the exponential phase, while the second peak signals its termination. The proposed design can be seamlessly integrated into fermentation processes to continually monitor progress, boost productivity, develop tailored control strategies that meet specific objectives, and so on.

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 score0.022
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.173
Teacher spread0.164 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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