Progress in Boron Subnaphthalocyanines (BsubNcs) –Targeting Bay Position Halogenation and Avoiding It and Its Electrochemical Impact
Bibliographic record
Abstract
For some time, our group has been focused on the molecular design, synthesis and application of derivatives of boron subphthalocyanines (BsubPcs) and subnaphthalocyanines (BsubNcs), which are compounds with a chelated central boron atom and an extensive p-conjugated macrocyclic ligand. Our focal point continues to be balanced 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 in organic light emitting diodes (OLEDs) and organic photovoltaics (OPVs)/organic solar cells (OSCs) respectively; 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 allowed composition based on bay-position halogenation that was formed randomly during the reaction of BCl 3 with 2,3-dicyanonaphthalene at temperature on forming 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. I will outline how the use of BBr 3 for the formation of the BsubNcs impacts the outcome, also enables random bay-position halogenation and does enable the first example of the bay-position halogenated BsubNcs to be separable. From a basic chemistry perspective, I will then highlight that we have progressed on blocking the random bay-position halogenation by developing a method to entirely avoid the bay-position halogenation. I will outline the approach and show the first basic characterization of non-bay-position halogenated BsubNc and the relative characteristics of the associated BsubNcs. Electrochemical comparison of the BsubNcs will also be outlined and also spectroelectrochemistry characterization. We have also applied computational modelling to look at the relative impact of the random bay-position halogenation. 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 and will be discussed. I will also outline our approach to accelerated development of BsubNcs whereby their molecular design and synthesis is first justified through a re-adopted computational model. I will show how we calibrate several levels of computational modelling relative to and against a firm set of experimental data. I will then move onto several examples of how we have developed BsubNcs for their application in organic electronic devices utilizing this method. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".