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Record W3154618053 · doi:10.1021/acs.langmuir.1c00147

Tuning the Conductivity and Electron Work Function of a Spin-Coated PEDOT:PSS/PEO Nanofilm for Enhanced Interfacial Adhesion

2021· article· en· W3154618053 on OpenAlexafffund
Raymond Christopher Setiawan, Dongyang Li

Bibliographic record

VenueLangmuir · 2021
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsWork functionPEDOT:PSSAdhesionConductivityMaterials scienceWork (physics)Spin (aerodynamics)ElectronChemical engineeringFunction (biology)NanotechnologyChemical physicsPolymer chemistryComposite materialChemistryLayer (electronics)Physical chemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

We report a novel phenomenon of increasing the adherence of a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS/PEO) nanofilm for Si3N4 through cosolvent treatment by DMSO. By varying the w/w% ratio of DMSO, nanofilms with different conductivities were produced. Atomic force microscopy (AFM) analysis showed that the adhesive force between the AFM’s Si3N4 probe and the nanofilm increased by 35.8% as the conductivity of the nanofilm increased. The conductivity became saturated after the PEDOT:PSS-to-DMSO ratio reached a certain level. This study demonstrates that the variations in the adhesive force are determined by two factors: (1) the difference in EWF between the nanofilm and the counter-body Si3N4 and (2) the electrical conductivity of the materials involved. The former is used for establishing a dipole layer at the interface, while the latter determines the degree of ease to achieve the dipole layer. This study demonstrates an approach to tailor interfacial bonding for different types of materials without atomic diffusion, which is promising for applications in various fields such as control of biomedical films on implants and functional films for electronic devices.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.224

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 head0.027
GPT teacher head0.291
Teacher spread0.265 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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