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Record W4210580887 · doi:10.1002/er.7705

Surface activated natural wood biomass electrode for efficient microbial electrocatalysis: Performance and mechanism

2022· article· en· W4210580887 on OpenAlexaff
Zi‐Ai Xu, Xiaomeng Ma, Yutong Shi, Jamile Mohammadi Moradian, Yan‐Zhai Wang, Guofeng Sun, Jing Du, Xiaomei Ye, Yang‐Chun Yong

Bibliographic record

VenueInternational Journal of Energy Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsMinistry of Agriculture
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsElectrocatalystElectrodeMaterials scienceMicrobial fuel cellCarbon fibersChemical engineeringNanotechnologyChemistryElectrochemistryComposite materialComposite numberAnode

Abstract

fetched live from OpenAlex

Microbial electrocatalysis showed great potential for waste energy harvesting and CO2 upgrading. The conventional electrodes with three-dimensional (3D) architectures hold promise for efficient microbial electrocatalysis, but they were designed as macroporous structure with microfibers in all three dimensions, which could not concurrently improve the mass transfer and microbes penetration. In this study, a high-performance 3D electrode assembled from bulk two-dimensional (2D) structures derived from natural wood was fabricated by hydrothermal treatment for surface activation and followed by pyrolysis. This 2D/3D hybrid structure guaranteed high surface area and multi-transportation-pathways, which endowed the wood electrode attracted more bacterial cells, facilitated the interfacial electron transfer between cells and electrode. As a result, the wood electrode delivered 8.3 times higher power output (483 vs 52 mW/m2) and 3.1 times higher formic acid production (3.3 vs 0.8 mM) than conventional carbon cloth electrode in microbial electrocatalysis system. This work provided new strategy for high-performance wood electrode fabrication and unveiled the mechanism of microbial electrocatalysis with natural biomass electrode.

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)0.001
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 score0.022
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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