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Record W2879961344 · doi:10.1039/c8tb01598h

Nanostructured interfaces for probing and facilitating extracellular electron transfer

2018· article· en· W2879961344 on OpenAlexfundno aff
Huan‐Hsuan Hsu, Pu Deng, Yixin Zhang, Han N. Nguyen, Xiaocheng Jiang

Bibliographic record

VenueJournal of Materials Chemistry B · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceElectron transferElectronNanotechnologyExtracellularChemical physicsChemistryPhotochemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Extracellular electron transfer (EET) is a process performed by electrochemically active bacteria (EAB) to transport metabolically-generated electrons to external solid-phase acceptors through specific molecular pathways. Naturally bridging biotic and abiotic charge transport systems, EET offers ample opportunities in a wide range of bio-interfacing applications, from renewable energy conversion, resource recovery, to bioelectronics. Full exploration of EET fundamentals and applications demands technologies that could seamlessly interface and interrogate with key components and processes at relevant length scales. In this review, we will discuss the recent development of nanoscale platforms that enabled EET investigation from single-cell to network levels. We will further overview research strategies for utilizing rationally designed and integrated nanomaterials for EET facilitation and efficiency enhancement. In the future, EET components such as c-cytochrome based outer membranes and bacterial nanowires along with their assembled structures will present themselves as a whole new category of biosynthetic electroactive materials with genetically encoded functionality and intrinsic biocompatibility, opening up possibilities to revolutionize the way electronic devices communicate with biological systems.

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 score0.003
Threshold uncertainty score0.982

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 science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.209
Teacher spread0.202 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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