Sub-4 nm Mapping of Donor-Acceptor Organic Semiconductor Nanoparticle Composition
Bibliographic record
Abstract
Sub-4 nm Mapping of Donor-Acceptor Organic Semiconductor Nanoparticle CompositionNatalie Holmes a, Ingemar Persson a, Yue-Sheng Chen aa University of Sydney, AustraliaMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#Nano-Eco-PV - Nanoengineered Materials and Associated Advanced Characterisation Tools for Printable & Eco-Friendly Processed PhotovoltaicsBarcelona, Spain, 2024 March 4th - 8thOrganizers: Antoine Bousquet, Sylvain Chambon and Natalie HolmesOral, Yue-Sheng Chen, presentation 097DOI: https://doi.org/10.29363/nanoge.matsus.2024.097Publication date: 18th December 2023Organic photovoltaic (OPV) devices require active layers comprised of molecular heterojunctions to split excitons into free charges. These molecular heterojunctions are comprised of a binary blend of electron donor and electron acceptor material, where the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) offsets are sufficient for exciton dissociation. While optimising the morphology of organic photovoltaic active layers is increasingly important, measuring the morphology accurately has for some time been a challenge for researchers in the discipline. Here we report, for the first time, sub-4 nm mapping of donor : acceptor nanoparticle composition in eco-friendly colloidal dispersions for organic photovoltaics.1 Low energy scanning transmission electron microscopy (STEM) energy dispersive X-ray spectroscopy (EDX) mapping has revealed the internal morphology of organic semiconductor donor : acceptor blend nanoparticles at the sub-4 nm level. A unique element was available for utilisation as a fingerprint element to differentiate donor from acceptor material in each blend system. Si was used to map the location of donor polymer PTzBI-Si in PTzBI-Si:N2200 nanoparticles, and S (in addition to N) was used to map donor polymer TQ1 in TQ1:PC71BM nanoparticles. For select material blends, synchrotron-based scanning transmission X-ray microscopy (STXM), was demonstrated to remain as the superior chemical contrast technique for mapping organic donor : acceptor morphology, including for material combinations lacking a unique fingerprint element, or systems where the unique element is in a terminal functional group and hence can be easily damaged under the electron beam, e.g. F on PTQ10 donor polymer in the PTQ10:IDIC donor : acceptor blend. We provide both qualitative and quantitative compositional mapping of organic semiconductor nanoparticles with STEM EDX, with sub-domains resolved in nanoparticles as small as 30 nm in diameter. The sub-4 nm mapping technology presented shows great promise for the optimisation of organic semiconductor blends for applications in organic electronics (solar cells and bioelectronics) and photocatalysis, and has further applications in organic core–shell nanomedicines. References:[1] Persson, I., Laval, H., Chambon, S., Bonfante, G., Hirakawa, K., Wantz, G., Watts, B., Marcus, M., Xu, X., Lei, Y., Lakhwani, G., Andersson, M. R., Cairney, J., Holmes, N. P. (2023). Sub-4 nm mapping of donor-acceptor organic semiconductor nanoparticle composition. Nanoscale, 15, 6126–6142.© FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".