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Record W2531686149 · doi:10.1021/acs.chemmater.6b03527

Controlling C<sub>60</sub> Organization through Dipole-Induced Band Alignment at Self-Assembled Monolayer Interfaces

2016· article· en· W2531686149 on OpenAlexafffund
Mohamed A. Mezour, Oleksandr Voznyy, Edward H. Sargent, R. Bruce Lennox, Dmitrii F. Perepichka

Bibliographic record

VenueChemistry of Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of TorontoMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMonolayerDipoleScanning tunneling microscopeSelf-assembled monolayerDensity functional theorySelf-assemblyMoleculeChemical physicsMaterials scienceNanotechnologyAdsorptionChemistryCrystallographyComputational chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Understanding the structural organization and growth of organic molecules on self-assembled monolayers (SAMs) is crucial for creating high-performance SAM-based electronic devices. We report herein C 60 adsorption onto benzenethiol (BT), pentafluorobenzenethiol (PFBT), and octanethiol (C8SH) SAM-modified Au(111) studied using scanning tunneling microscopy at the liquid–solid interface. A continuous film of C 60 molecules forms at a much faster rate ( k = 3.3 × 10 –7 s –1 ) on PFBT compared to that on BT ( k = 7.2 × 10 –9 s –1 ) and C8SH SAMs ( k = 9.5 × 10 –9 s –1 ). On the basis of density functional theory calculations, we propose that the difference in C 60 growth behavior originates from the dipole-induced dipole interactions between the SAM and C 60 . This may be further augmented by an inverse charge transfer from C 60 to SAM. This work provides new insights into the self-assembly behavior of next-generation electronic materials.

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 score0.002
Threshold uncertainty score0.558

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 head0.006
GPT teacher head0.190
Teacher spread0.185 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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