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Record W4388865943 · doi:10.26434/chemrxiv-2023-f7mnr

A microfluidic study of the kinetics of bio-electrochemical oxidation of acetate by a Geobacter sulfurreducens biofilm

2023· preprint· en· W4388865943 on OpenAlexaff
Nastaran Khodaparastasgarabad, Manon Couture, Jesse Greener

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGeobacter sulfurreducensBiofilmGeobacterKineticsElectrochemistryMicrofluidicsChemistryElectrodeAnalytical Chemistry (journal)NanotechnologyMaterials scienceChemical engineeringChromatographyPhysicsEngineeringBacteriaPhysical chemistryBiology

Abstract

fetched live from OpenAlex

This work addresses the need for kinetic studies to better understand the mechanisms that are responsible for the outputs from flow-based bioelectrochemical systems (BES). Unlike most kinetic studies, which focus on electron transport, here we consider reaction kinetics with a focus on chemical mass transport. To achieve this, we used a precision microfluidic reactor which accurately controlled acetate concentration ([Ac]), flow rate (Q), and flow direction (tangential and perpendicular), while output current (I) from a mature Geobacter sulfurreducens electroactive biofilm (EAB) was measured. A detailed analysis of the effects of the control variables on the current (I) were evaluated over a long timeframe, between 1 month to nearly 1 year. The results indicate that all experimental parameters have effects on outputs, but overall, age is the dominant factor. After nearly 1 year, current densities were as high as 29.5 A m-1, which is higher than any other 3-electrode experiment on G. sulfurreducens EAB. The main mechanisms for high outputs at early stages appeared to be related to EAB deacidification, whereas for the old EAB, increases were related to significantly higher acetate permeability due to structural changes in the EAB. Additionally, each flow mode exhibits complementary kinetic properties based on a quantitative analysis of apparent enzyme/substrate affinity (KM(app)) and maximum current (Imax) values. Therefore, in addition to providing valuable insights to the biosensors and bioelectronics community, these findings open the door to practical approaches for optimization of BES device design and operation.

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 score0.006
Threshold uncertainty score0.569

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 science0.0000.001
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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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