Reductive Aromatization/Dearomatization and Elimination Reactions to Access Conjugated Polycyclic Hydrocarbons, Heteroacenes, and Cumulenes
Bibliographic record
Abstract
Abstract Acenes, heteroacenes, conjugated polycyclic hydrocarbons, and polycyclic aromatic hydrocarbons (collectively referred to in this review as conjugated polycyclic molecules, CPMs) have fascinated chemists since they were first isolated and synthesized in the mid 19th century. Most recently, these compounds have shown significant promise as the active components in organic devices (e.g., solar cells, thin‐film transistors, light‐emitting diodes, etc.), and, since 2001, a plethora of publications detail synthetic strategies to produce CPMs. In this review, we discuss reductive aromatization, reductive dearomatization, and elimination/extrusion reactions used to form CPMs. After a brief discussion on early methods to synthesize CPMs, we detail the use of reagents used for the reductive (de)aromatization of precursors containing 1,4‐diols/diethers, including SnCl 2 and iodide (I − ). Extension of these methods to carbomers and cumulenes is briefly discussed. We then describe low‐valent metal species used to reduce endoxides to CPMs, and discuss the methods to directly reduce acenediones and acenones to the respective acene. In the final section, we describe methods used to affect aromatization to the desired CPM via extrusion of small, volatile molecules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".