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Record W2944069199 · doi:10.1002/celc.201900732

A Generalized Kinetic Framework Applied to Whole‐Cell Bioelectrocatalysis in Bioflow Reactors Clarifies Performance Enhancements for <i>Geobacter Sulfurreducens</i> Biofilms

2019· article· en· W2944069199 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemElectroChem · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
Fundersnot available
KeywordsGeobacter sulfurreducensBiofilmGeobacterCatalysisChemistryChemical engineeringBiochemistryBiologyBacteriaEngineering

Abstract

fetched live from OpenAlex

Abstract A common kinetic framework for studies of whole‐cell catalysis is vital for understanding and optimizing bioflow reactors. In this work, we demonstrate the applicability of a flow‐adapted version of Michaelis‐Menten kinetics to an electrocatalytic bacterial biofilm. A three‐electrode microfluidic biofilm flow reactor measured increased turnover rates by as much as 50 % from a Geobacter sulfurreducens biofilm as flow rate was varied. Based on parameters from the applied kinetic framework, flow‐induced increases to turnover rate, catalytic efficiency and device reaction capacity could be linked to an increase in catalytic biomass. This study demonstrates that a standardized kinetic framework is critical for quantitative measurements of new living catalytic systems in flow reactors and for benchmarking against well‐studied catalytic systems such as enzymes.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.021
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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