Lewis acids and bases as molecular dopants for organic semiconductors
Bibliographic record
Abstract
Abstract Controlling the concentration of charge carriers (mobile electrons and holes) in organic semiconductors is vital to precisely controlling their electronic properties. Significant efforts have gone into understanding how molecular dopants induce charge carriers in organic semiconductors. The most widely used doping mechanisms occur via electron transfer (i.e., oxidation or reduction of the semiconductor) or via reaction with a strong Brønsted acid. Recently, strong Lewis acids have been observed to induce p‐type charge carriers in organic semiconductors with greater efficiency than classical dopants. The mechanism of Lewis‐acid doping could not easily be unified with either classical doping methods and has been under intense scrutiny over the past 5 years. Very recently, the Lewis‐acid doping effects have been shown to be due to water impurities in commercial Lewis acids forming strong Brønsted acids. This means that many studies on doping using Lewis acids may be occurring via a Brønsted‐acid doping mechanism. This recent revelation explains some observations in literature, but not all, and there are still unanswered questions. The nature of the Lewis acid and organic semiconductor can significantly impact the doping mechanism and the doping efficiency. Additionally, strong evidence for alternative doping mechanisms using Lewis acids not involving water has been shown. Lewis‐acid doping has mostly been studied as a p‐type dopant method on Lewis‐basic polymers. There is growing literature showing Lewis bases can also act as n‐type dopants, excluding Brønsted‐acid doping as a possible mechanism. In this tutorial review, we will present a brief overview on molecular doping of organic semiconductors, survey the literature on p‐type and n‐type Lewis doping, outline several proposed mechanisms, and speculate on some possible mechanisms using literature observations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".