(Invited) Anisotropic Contributions in the Chromatographic Elution Behavior of Fullerenes and Fullertubes
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".