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Record W4206590128 · doi:10.1021/acsnano.1c10122

Bidirectional Phase Transformation of Supramolecular Networks Using Two Molecular Signals

2022· article· en· W4206590128 on OpenAlexafffund
Daling Cui, Chenghao Liu, Federico Rosei, Dmitrii F. Perepichka

Bibliographic record

VenueACS Nano · 2022
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsSupramolecular chemistryScanning tunneling microscopeMaterials scienceNanotechnologyMoleculePhase (matter)Chemical physicsPhase transitionRealization (probability)NanostructureChemistryPhysics

Abstract

fetched live from OpenAlex

Reversible control of molecular self-assembly is omnipresent in adaptive biological systems, yet its realization in artificial systems remains a major challenge. Using scanning tunneling microscopy and density functional theory calculations, we show that a 2D supramolecular network formed by terthienobenzenetricarboxylic acid (TTBTA) can undergo a reversible structural transition between a porous and dense phase in response to different molecular signals (trimethyltripyrazolotriazine (TMTPT) and C60). TMTPT molecules can induce a phase transition from the TTBTA honeycomb to the dense phase, whereas a reverse transition can be triggered by introducing C60 molecules. This response stems from the selective association between signal molecules and TTBTA polymorphs. The successful realization of reversible molecular transformation represents important progress in controlling supramolecular surface nanostructures and could be potentially applicable in various areas of nanotechnology, including phase control, molecular sensing, and “smart” switchable surfaces.

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 score0.366
Threshold uncertainty score0.402

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 head0.010
GPT teacher head0.239
Teacher spread0.230 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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