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Record W2767003379 · doi:10.1021/jacs.7b08642

Control of Fullerene Crystallization from 2D to 3D through Combined Solvent and Template Effects

2017· article· en· W2767003379 on OpenAlexafffund
Daling Cui, Maryam Ebrahimi, Federico Rosei, Jennifer MacLeod

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAustralian Research CouncilMinistry of Education of the People's Republic of ChinaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistrySolventMoleculeCrystallizationFullereneNanoscopic scaleCovalent bondScanning tunneling microscopeChemical physicsDensity functional theoryAdsorptionSelf-assemblyNanotechnologyComputational chemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Achieving precise control of molecular self-assembly to form designed three-dimensional (3D) structures is a major goal in nanoscale science and technology. Using scanning tunnelling microscopy and density functional theory calculations, we show that a 2D covalent organic framework (COF-1) can template solution-processed C 60 guest molecules to form several solvent-dependent structural arrangements and morphologies via a 2D to 3D growth process. When 1,2,4-tricholorobenzene is used as solvent, C 60 molecules form a template-defined close-packed structure. When heptanoic acid is used as solvent, a range of lower density architectures that deviate from the template-defined close packing are observed. We attribute this difference to the co-adsorption of the heptanoic acid solvent molecules, which is only achieved in the presence of the template. This work demonstrates the possibility to precisely control 3D molecular self-assembly through the synergistic combination of template and solvent effects.

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 score0.004

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 communication0.0010.000
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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

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Same venueJournal of the American Chemical SocietySame topicSurface Chemistry and CatalysisFrench-language works237,207