Phthalocyanine-Based Organic Thin-Film Transistors: A Review of Recent Advances
Bibliographic record
Abstract
Metal phthalocyanines (MPcs) are versatile conjugated macrocycles that have attracted a great deal of interest as active components in modern organic electronic devices. In particular, the charge transport properties of MPcs, their chemical stability, and their synthetic versatility make them ideal candidate materials for use in organic thin-film transistors (OTFTs). This article reviews recent progress in both the material design and device engineering of MPc-based OTFTs, including the introduction of solubilizing groups on the MPcs and the surface modification of substrates to induce favorable MPc self-assembly. Finally, a discussion on emerging niche applications based on MPc OTFTs will be explored, in addition to a perspective and outlook on these promising materials in OTFTs. The scope of this review is focused primarily on the advances made in the field of MPc-based OTFTs since 2008.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Materials-science review of phthalocyanine organic thin-film transistors; a domain review.
It reviews organic-electronics materials and devices, not research practice.
Materials review of phthalocyanine organic transistors; domain electronics science.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".