Scalable, Chromatography-Free Synthesis of Alkyl-Tethered Pyrene-Based Materials. Application to First-Generation “Archipelago Model” Asphaltene Compounds
Bibliographic record
Abstract
In this paper, we report a highly efficient, scalable approach to the total synthesis of conformationally unrestricted, electronically isolated arrays of alkyl-tethered polycyclic aromatic chromophores. This new class of modular molecules consists of polycyclic aromatic "islands" comprising significant structural fragments present in unrefined heavy petroleum, tethered together by short saturated alkyl chains, as represented in the "archipelago model" of asphaltene structure. The most highly branched archipelago compounds reported here share an architecture with first-generation dendrimeric constructs, making the convergent, chromatography-free synthesis described herein particularly attractive for further extensions in scope and applications to materials chemistry. The syntheses are efficient, selective, and readily adaptable to a multigram scale, requiring only inexpensive, "earth-abundant" transition-metal catalysts for cross-coupling reactions and extraction and fractional crystallization for purification. This approach avoids typical limitations in cost, scale, and operational practicality. All of the archipelago compounds and synthetic intermediates have been fully characterized spectroscopically and analytically. The solid-state structure of one archipelago model compound has been determined by X-ray crystallography.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".