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Record W4386854161 · doi:10.1149/ma2023-01151444mtgabs

Silicon Phthalocyanines as Emerging n-Type Semiconductors in Thin Film Transistors

2023· article· en· W4386854161 on OpenAlexaff
Benoît H. Lessard

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThin-film transistorMaterials scienceOrganic semiconductorOptoelectronicsSemiconductorSiliconPentaceneTransistorThin filmSurface modificationElectron mobilityNanotechnologyLayer (electronics)Chemical engineeringVoltageElectrical engineering

Abstract

fetched live from OpenAlex

Silicon phthalocyanines are emerging n-type semiconductors for use in organic photovoltaics (OPVs) and organic thin-film transistors (OTFTs).[1] Their low synthetic complexity paired with their versatile axial group facilitates the finetuning of their chemical properties, solution properties and processing characteristics without significantly affecting their frontier orbital levels or their absorption properties. The crystal engineering and film forming characteristics of silicon phthalocyanine semiconductors can be tuned through appropriate axial group functionalization, therefore facilitating their integration into OTFTs by solution processing or vapor deposition. We were the first to integrate SiPcs into OTFTs, and unlike the majority of phthalocyanines, SiPcs are inherently better electron transporting materials then hole transport materials. We demonstrated n-type mobilities on the order of 0.5 cm2/Vs which is among the best of all phthalocyanines.[3] Through axial functionalization we reported that changes in the electron withdrawing character of the axial group lead to predictable drops in device threshold voltage from 45 V to 5 V.[4] The axial group can also be used to improve the SiPc solubility leading to solution processable OTFTs where the choice of axial group also dictates the thin film morphology an ultimately the resulting device performance.[5] GIWAXS studies show that processing conditions and choice of axial groups leads to changes in molecular alignment at the interface providing critical insight into device optimization. This presentation will cover our recent advances in the development of structure property relationships between SiPc structure, thin film processing and resulting charge transport properties. REFERENCES 1. ACS Applied Materials & Interfaces. 2021, 13, 31321-31330 2. Organic Electronics. 2020, 87, 105976 3. Advanced Electronic Materials. 2019, 5, 1900087 4. ACS Appl. Electron. Mater. 2021, 3, 5, 2212–2223 5. ACS Applied Materials & Interfaces. 2020, 13, 1008-1020 Figure 1

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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