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Record W2150549729 · doi:10.1039/c0lc00494d

A μL-scale micromachined microbial fuel cell having high power density

2011· article· en· W2150549729 on OpenAlexaff
Seokheun Choi, Hyung‐Sool Lee, Yongmo Yang, Prathap Parameswaran, César I. Torres, Bruce E. Rittmann, Junseok Chae

Bibliographic record

VenueLab on a Chip · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrobial fuel cellAnodePolydimethylsiloxaneMicroelectromechanical systemsPower densityCathodeMaterials scienceFaraday efficiencyGeobacterInternal resistanceCurrent densityChemical engineeringNanotechnologyChemistryElectrodePower (physics)BiofilmBacteriaBattery (electricity)Physics

Abstract

fetched live from OpenAlex

We report a MEMS (Micro-Electro-Mechanical Systems)-based microbial fuel cell (MFC) that produces a high power density. The MFC features 4.5-μL anode/cathode chambers defined by 20-μm-thick photo-definable polydimethylsiloxane (PDMS) films. The MFC uses a Geobacter-enriched mixed bacterial culture, anode-respiring bacteria (ARB) that produces a conductive biofilm matrix. The MEMS MFC generated a maximum current density of 16,000 μA cm(-3) (33 μA cm(-2)) and power density of 2300 μW cm(-3) (4.7 μW cm(-2)), both of which are substantially greater than achieved by previous MEMS MFCs. The coulombic efficiency of the MEMS MFC was at least 31%, by far the highest value among reported MEMS MFCs. The performance improvements came from using highly efficient ARB, minimizing the impact of oxygen intrusion to the anode chamber, having a large specific surface area that led to low internal resistance.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.172
Teacher spread0.165 · 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

Citations145
Published2011
Admission routes1
Has abstractyes

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