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Record W2340352245 · doi:10.1063/1.4947285

Electrical limit of silver nanowire electrodes: Direct measurement of the nanowire junction resistance

2016· article· en· W2340352245 on OpenAlexaff
Franz Selzer, Carlo Floresca, David Kneppe, Ludwig Bormann, Christoph Sachse, Nelli Weiß, Alexander Eychmüller, Aram Amassian, Lars Müller‐Meskamp, Karl Leo

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsCanadian Institute for Advanced Research
FundersSeventh Framework ProgrammeDeutsche Forschungsgemeinschaft
KeywordsNanowireMaterials scienceElectrodeSheet resistanceElectrical resistivity and conductivityOptoelectronicsElectrical resistance and conductanceNanotechnologyConductivityComposite materialElectrical engineeringChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

We measure basic network parameters of silver nanowire (AgNW) networks commonly used as transparent conducting electrodes in organic optoelectronic devices. By means of four point probing with nanoprobes, the wire-to-wire junction resistance and the resistance of single nanowires are measured. The resistance RNW of a single nanowire shows a value of RNW=(4.96±0.18) Ω/μm. The junction resistance RJ differs for annealed and non-annealed NW networks, exhibiting values of RJ=(25.2±1.9) Ω (annealed) and RJ=(529±239) Ω (non-annealed), respectively. Our simulation achieves a good agreement between the measured network parameters and the sheet resistance RS of the entire network. Extrapolating RJ to zero, our study show that we are close to the electrical limit of the conductivity of our AgNW system: We obtain a possible RS reduction by only ≈20% (common RS≈10 Ω/sq). Therefore, we expect further performance improvements in AgNW systems mainly by increasing NW length or by utilizing novel network geometries.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.169
Teacher spread0.160 · 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

Citations55
Published2016
Admission routes1
Has abstractyes

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