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Record W3176229721 · doi:10.1002/adfm.202104341

Ionic Diode Based on an Asymmetric‐Shaped Carbon Black Nanoparticle Membrane

2021· article· en· W3176229721 on OpenAlexaff
Mengqi Li, Chengfa Wang, Zhijian Liu, Yongxin Song, Dongqing Li

Bibliographic record

VenueAdvanced Functional Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central Universities
KeywordsIonic bondingMaterials scienceRectificationDiodeNanotechnologyIonic liquidIonOptoelectronicsVoltageChemistryElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Bioinspired ionic diodes are widely explored to mimic the controllable ion transport of biological ion channels. However, due to their vertical structures, the integration of conventional ionic diodes into complex ionic circuits is still a challenge. Here, a horizontal ionic diode is developed based on an asymmetric nanochannel network membrane (NCNM) constructed from carbon black nanoparticles. The rectification of ionic current is achieved through the asymmetric concentration polarization of ions at two ends of the asymmetric NCNM. The rectification ratio of the NCNM ionic diode can be modified flexibly by changing the working fluid and the geometry of the NCNM. It is found that with the presence of cationic surfactant in the working fluid, the rectification ratio increases more than 30 times from 3.03 to 109.77. Advanced functions of the developed ionic component, including working as an ionic transistor for current switching and integrating into an ionic diode bridge on a single nanofluidic chip for rectifying alternating current signals, are also demonstrated in this paper. The horizontally arranged NCNM ionic diode possesses the advantages of easy fabrication and integration that can be practically applied in the development of ionic electronics and biocomputing.

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 score0.099
Threshold uncertainty score0.929

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)0.0000.000

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 head0.212
Teacher spread0.199 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

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