Metal phthalocyanines: thin-film formation, microstructure, and physical properties
Bibliographic record
Abstract
Metal phthalocyanines (MPcs) are an abundant class of small molecules comprising of a highly conjugated cyclic structure with a central chelated metal ion. Due to their remarkable chemical, mechanical, and thermal stability MPcs have become popular for a multitude of applications since their discovery in 1907. The potential for peripheral and axial functionalization affords structural tailoring to create bespoke MPc complexes for various next generation applications. Specifically, thin-films of MPcs have found promising utility in medical and electronic applications where the need to understand the relationship between chemical structure and the resulting thin-film properties is an important ongoing field. This review aims to compile the fundamental principles of small molecule thin-film formation by physical vapour deposition and solution processing focusing on the nucleation and growth of crystallites, thermodynamic and kinetic considerations, and effects of deposition parameters on MPc thin-films. Additionally, the structure-property relationship of MPc thin-films is examined by film microstructure, morphology and physical properties. The topics discussed in this work will elucidate the foundations of MPc thin-films and emphasize the critical need for not only molecular design of new MPcs but the role of their processing in the formation of thin-films and how this ultimately governs the performance of the resulting application.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".