MétaCan
Menu
Back to cohort
Record W2885384084 · doi:10.1021/acs.jpcc.8b06724

Topochemical Polymerization of Phenylacetylene Macrocycles under Pressure

2018· article· en· W2885384084 on OpenAlexaff
Andrea Lapini, Samuele Fanetti, Margherita Citroni, Roberto Bini, Charles-Olivier Gilbert, Simon Rondeau‐Gagné, Jean‐François Morin

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2018
Typearticle
Languageen
FieldChemistry
TopicPolydiacetylene-based materials and applications
Canadian institutionsUniversity of WindsorUniversité Laval
FundersAlfred P. Sloan Foundation
KeywordsPhenylacetylenePolymerizationMaterials scienceCovalent bondCarbon nanotubeAbsorption (acoustics)PhotochemistryAmbient pressureAlkyneThermal decompositionNanostructureChemical engineeringChemistryNanotechnologyOrganic chemistryPolymerCatalysis

Abstract

fetched live from OpenAlex

Self-assembly of organic macrocycles has been exploited as a preliminary step in the synthesis of soluble and tailorable carbon-based nanostructures. Functionalized nanotubes have been prepared using, as core building blocks, nearly planar ring structures containing several alkyne units, exploiting the geometry achieved in the spontaneous preassembling step driven by π interaction. Covalent cross-linking between these units was achieved by thermal or photochemical activation with UV light. Here, we apply a moderate pressure in a sapphire anvil cell (1.0 GPa) to facilitate the preassembling and induce the cross-linking under pressure either with visible light, absorbed by two-photon absorption, or thermally. We observe a high yield of enhanced quality cross-linked nanotubes in a sample, showing, at ambient pressure, only side-chain decomposition. These results show that moderate pressures, easily achievable in large volume cells, are able to selectively favor topochemical reactions in such complex organic systems.

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 score0.003

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 communication0.0000.000
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicPolydiacetylene-based materials and applicationsFrench-language works237,207