MétaCan
Menu
Back to cohort

Improved Performance of Pentacene Organic Field-Effect Transistors by Inserting a V <sub>2</sub> O <sub>5</sub> Metal Oxide Layer

2011· article· en· W2097797658 on OpenAlexfundno aff
Geng Zhao, Xiaoman Cheng, Bo-Qun Du, Xiaoyu Liang

Bibliographic record

VenueChinese Physics Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsPentaceneMaterials scienceLayer (electronics)MetalOxideOptoelectronicsField-effect transistorTransistorField (mathematics)Thin-film transistorNanotechnologyElectrical engineeringMetallurgyVoltage

Abstract

fetched live from OpenAlex

We fabricate pentacene-based organic field effect transistors (OFETs), inserting a transition metal oxide (V 2 O 5 ) layer between the pentacene and Al source-drain (S/D) electrodes. The performance of the devices with V 2 O 5 /Al S/D electrodes is considerably improved compared to the pentacene-based OFET with only Al S/D electrodes. After the 10-nm V 2 O 5 layer modification, the effective field-effect mobility of the devices increases from 2.7 × 10 −3 cm 2 /V·s to 8.93 × 10 −1 cm 2 /V·s. Owing to the change of the injection property, the effective threshold voltage (V th ) is changed from −7.5 V to −5 V and the on/off ratio shifts from 10 2 to 10 4 . Moreover, the dispersion of sub-threshold current in the devices disappears. These performance improvements are ascribed to the low carrier injection barrier and the reduction of contact resistance. It is indicated that V 2 O 5 layer modification is an effective approach to improve pentacene-based OFET performance.

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)0.000
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
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueChinese Physics LettersSame topicOrganic Electronics and PhotovoltaicsFrench-language works237,207