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Record W3095452836 · doi:10.1021/acs.est.0c05000

In Vivo Polymerization (“Hard-Wiring”) of Bioanodes Enables Rapid Start-Up and Order-of-Magnitude Higher Power Density in a Microbial Battery

2020· article· en· W3095452836 on OpenAlexfundno aff
Kristian L. Dubrawski, Sung-Geun Woo, Wei Chen, Xing Xie, Yi Cui, Craig S. Criddle

Bibliographic record

VenueEnvironmental Science & Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaStanford Woods Institute for the Environment
KeywordsMicrobial fuel cellPower densityBattery (electricity)PolymerizationCathodeChemistryChemical engineeringElectrochemistryMaterials scienceAnodeElectrodeOrganic chemistryPolymerPower (physics)

Abstract

fetched live from OpenAlex

For microbial electrochemical technologies to be successful in the decentralized treatment of wastewater, steady-state power density must be improved and cost must be decreased. Here, we demonstrate in vivo polymerization (“hard-wiring”) of a microbial community to a growing layer of conductive polypyrrole on a sponge bioanode of a microbial battery, showing rapid biocatalytic current development (∼10 times higher than a sponge control after 4 h). Moreover, bioanodes with the polymerized inoculant maintain higher steady-state power density (∼2 times greater than the control after 28 days). We then evaluate the same hard-wired bioanodes in both a two-chamber microbial fuel cell and microbial battery with a solid-state NaFeIIFeIII(CN)6 (Prussian Blue) cathode, showing approximately an order-of-magnitude greater volumetric power density with the microbial battery. The result is a rapid start-up, low-cost (no membrane or platinum catalyst), and high volumetric power density system (independent of atmospheric oxygen) for harvesting energy and carbon from dilute organics in wastewater.

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 categoriesInsufficient 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 score0.037
Threshold uncertainty score0.999

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.183
Teacher spread0.176 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

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