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Record W4386855004 · doi:10.1149/ma2023-01121260mtgabs

(Invited) Anisotropic Contributions in the Chromatographic Elution Behavior of Fullerenes and Fullertubes

2023· article· en· W4386855004 on OpenAlexaff
Emmanuel Bourret, Michel Côté, Steven Stevenson

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldChemistry
TopicFullerene Chemistry and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFullerenePolarizabilityContext (archaeology)AnisotropyChemistryElutionMoleculeEndohedral fullereneComputational chemistryPhysicsOrganic chemistryChromatographyQuantum mechanics

Abstract

fetched live from OpenAlex

Since their discovery in the mid-1980s, the fullerenes have been confined to molecules of less than 100 atoms. With the recent development of a method allowing the isolation of pristine samples[1], the discovery of increasingly long fullertubes, hybrid between a fullerene and a nanotube, is stimulating renewed interest in the field of fullerene science. The increase in the number of atoms comes at a price, the explosion in the number of possible isomers, among which we must identify the experimentally isolated structure. Fuch’s model[2] is commonly used to explain the interaction between fullerenes and non-polar HPLC columns. Still, this approach cannot always differentiate between isomers as the results depend only on the average polarizability, which varies little for fullerenes with the same number of carbon atoms. In this presentation, we will discuss the use of polarizability anisotropy to differentiate isomers in the context of chromatographic elution. It allows us to generalize Fuchs’ model by adding anisotropic contributions leading to a finer description of fullerenes elution behavior and a better isomeric identification. [1] R.M. Koenig, H.-R. Tian, T.L. Seeler, K.R. Tepper, H.M. Franklin, Z.-C. Chen, S.-Y. Xie, and S. Stevenson, J. Am. Chem. Soc. 142 , 15614 (2020). [2] D. Fuchs, H. Rietschel, R.H. Michel, A. Fischer, P. Weis, and M.M. Kappes, J. Phys. Chem. 100, 725 (1996). Figure 1

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 score0.171
Threshold uncertainty score0.474

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 head0.016
GPT teacher head0.260
Teacher spread0.243 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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