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Record W2523273149 · doi:10.1021/acsami.6b07515

Improved Performance by SiO<sub>2</sub> Hollow Nanospheres for Silver Nanowire-Based Flexible Transparent Conductive Films

2016· article· en· W2523273149 on OpenAlexaff
Liwen Zhang, Longjiang Zhang, Yejun Qiu, Ji Yang, Ya Liu, Liu Hong, Guangji Li, Qiuquan Guo

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceElectrical conductorNanowireNanotechnologyTransparent conducting filmComposite materialOptoelectronicsChemical engineering

Abstract

fetched live from OpenAlex

Flexible transparent conductive films (TCFs) have attracted tremendous interest thanks to the rapid development of portable/flexible/wearable electronics. TCFs on the basis of silver nanowires (AgNWs) with excellent performance are becoming an efficient alternative to replace the brittle transparent metal oxide. In this study, a promising method was developed by introducing SiO 2 hollow nanospheres (SiO 2 –HNSs) into the film to significantly improve the performance of AgNW-based TCFs. Since SiO 2 –HNSs have opposite charges to AgNWs, the strong attraction had promoted a uniform distribution of AgNWs and made the distance between AgNWs closer, which could decrease the contact resistance greatly. The introduction of SiO 2 layer remarkably enhanced the transmission of visible light and the conductivity. In addition, the TCFs constructed by AgNWs and SiO 2 –HNSs showed much higher thermal stability and adhesive force than those by only AgNWs. As an example, the transmission of AgNW/SiO 2 –HNS-coated poly(ethylene terephthalate) (PET) could increase about 14.3% in comparison to AgNW-coated PET. Typically, a AgNW/SiO 2 –HNS-based TCF with a sheet resistance of about 33 Ω/sq and transmittance of about 98.0% (excluding substrate) could be obtained with excellent flexibility, adhesion, and thermal stability. At last some devices were fabricated.

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 categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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.

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

Citations33
Published2016
Admission routes1
Has abstractyes

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