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Record W2109668376 · doi:10.1038/ncomms7940

Programmable definition of nanogap electronic devices using self-inhibited reagent depletion

2015· article· en· W2109668376 on OpenAlexaff
Brian Lam, Wendi Zhou, Shana O. Kelley, Edward H. Sargent

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrodeLithographyNanolithographyMaterials scienceReagentNanoscopic scaleNanotechnologyElectronicsPhotodetectorLimitingSemiconductorOptoelectronicsChemistryFabrication

Abstract

fetched live from OpenAlex

Electrodes exhibiting controlled nanoscale separations are required in devices for light detection, semiconductor electronics and medical diagnostics. Here we use low-cost lithography to define micron-separated electrodes, which we downscale to create three-dimensional electrodes separated by nanoscale gaps. Only by devising a new strategy, which we term electrochemical self-inhibited reagent depletion, were we able to produce a robust self-limiting nanogap manufacturing technology. We investigate the method using experiment and simulation and find that, when electrodeposition is carried out using micron-spaced electrodes simultaneously poised at the same potential, these exhibit self-inhibited reagent depletion, leading to defined and robust nanogaps. Particularly remarkable is the formation of fractal electrodes that exhibit interpenetrating jagged elements that consistently avoid electrical contact. We showcase the new technology by fabricating photodetectors with responsivities (A/W) that are one hundred times higher than previously reported photodetectors operating at the same low (1-3 V) voltages. The new strategy adds to the nanofabrication toolkit method that unites top-down template definition with bottom-up three-dimensional nanoscale features.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.429

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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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 designNot applicable
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

Citations21
Published2015
Admission routes1
Has abstractyes

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