Progress on Boron Subnaphthalocyanines (BsubNcs) and Associated Hybrids Towards Organic Electronic Applications and Their Electrochemical Properties
Bibliographic record
Abstract
For some time, our group has been focused on the molecular design, synthesis and application of boron subphthalocyanines (BsubPcs) and subnaphthalocyanines (BsubNcs), which are macrocycles with a chelated central boron atom via nitrogen and a p-conjugated ligand. Our focal point balances between the basic and applied chemistry of the BsubPcs and BsubNcs, their physical properties (electrochemistry included) and their application as light emitting, light absorbing and electronic conducting materials for application in organic light emitting diodes (OLEDs) and organic photovoltaics (OPVs)/organic solar cells (OSCs), the basic electrochemical and photophysical properties being critical to these applications. For this presentation, I will focus on our progress on the development of BsubNcs. In the past we have shown that BsubNcs end up being a mixed alloyed composition based on bay-position halogenation that was formed randomly during the reaction of BCl3 with 2,3-dicyanonaphthalene at temperature to form the BsubNcs. The random bay-position halogenation has been shown to be impactful in a positive way within OPV devices, negative within OLED devices and also has electrochemical variations. However given it is random halogenation, it is desirable to truly understand its impact systematically. We have recently been able to develop a separation method and therefore separate the mixed alloyed BsubNc compositions and acquire data to show the impact of the percentage/number of bay-position halogens, chlorine and bromine included, on the electrochemical potentials and the photoluminescence. I will also present a new synthetic methodology to avoid the random bay-position halogenation of the associated BsubNcs. We have also applied a computational model to look at the relative impact of the random bay-position halogenation on the electronics. We have found that the frequency of halogenation has a larger impact on the predicted HOMO/LUMO energy levels than does the random halogen positioning around the bay-positions of the BsubNcs. As this was in parallel with the separations method that was developed, this computational data is therefore comparable to the acquired electrochemical data. We have also developed BsubNc + BsubPc hybrid materials. For the hybrids, there is a way to avoid bay-position halogenation and once this was avoided, we have the first example of electrochemical and photoluminescence data for the associated BsubNc + BsubPc hybrids. I will also outline our approach to accelerated development of BsubNcs, BsubPcs and the hybrids whereby their molecular design and synthesis was first been justified through a re-adopted computational model. Figure 1
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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 | 0.001 |
| 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".