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Record W2972689883 · doi:10.15244/pjoes/99099

Potential of Biochar-Anode ina Ceramic-Separator Microbial Fuel Cell (CMFC)with a Laccase-Based Air Cathode

2019· article· en· W2972689883 on OpenAlexfundno aff
Pimprapa Chaijak, Chikashi Sato, Monthon Lertworapreecha, Chontisa Sukkasem, Piyarat Boonsawang, N. Evelin Paucar

Bibliographic record

VenuePolish Journal of Environmental Studies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
FundersCollege of Family Physicians of CanadaCollege of Science and Engineering, University of MinnesotaIdaho State University
KeywordsBiocharSeparator (oil production)Microbial fuel cellAnodeLaccaseCathodeMaterials scienceCeramicWaste managementPulp and paper industryEnvironmental scienceEnvironmental chemistryChemical engineeringChemistryElectrodeMetallurgyPyrolysisOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

A cost-effective biochar derived from rubber tree sawdust was prepared by low-temperature pyrolysis at 500ºC for 2 h.The biochar was placed as an anode electrode in the anode chamber of the novel model ceramic-separator microbial fuel cell (CMFC) with a laccase-based air cathode.The rubber wastewater (with 500 mg/L sulfate and 1000 mg/L COD) was used as an anolyte.Maximal volumetric power density (PD) of 3.26±0.08µW/m 3 , maximal volumetric current density of 3.20±0.07mA/m 3 , and system internal resistance of 1002 Ω were obtained.The post-treatment results showed sulfate removal and COD removal efficiencies of 88.26±1.29%and 89.77±0.45%,respectively.Our work provided a novel model of a low-cost and economically friendly MFC system.Moreover, this work demonstrated a potential route based on sustainable and economical biochar as a bio-anode for wastewater treatment in an MFC.

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 score0.002

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 communication0.0000.000
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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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

Citations44
Published2019
Admission routes1
Has abstractyes

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Same venuePolish Journal of Environmental StudiesSame topicMicrobial Fuel Cells and BioremediationFrench-language works237,207