Fluorescent Photopatterns from Thin Films and Aqueous Polymer Dots Composed of Aminated Polydimethylsiloxane and a Conjugated Polymer
Bibliographic record
Abstract
The microscale patterning of luminescent and other functional materials is important for optoelectronics, sensor arrays, anticounterfeiting, and many other technologies and applications. Photopatterning is a well-established and advantageous approach, for which multicomponent resins are the norm. Here, we demonstrate photopatterning that is greatly simplified in its method and in its formulation. The only two components are an aminated polydimethylsiloxane and a semiconducting polymer, where the latter has the dual role of imparting fluorescence to the photopatterns and sensitizing singlet oxygen to drive imine bond formation and cross-linking between chains of aminated polydimethylsiloxane. Poly(9,9-dioctylfluorene- alt -benzothiadiazole) (F8BT) was used in tandem with aminopropylmethylsiloxane-dimethylsiloxane copolymer (PDMS-NH 2 ) for most experiments; however, multiple conjugated semiconducting polymers showed potential to be utilized with this method. Fluorescent patterns with fidelity and resolution on the order of tens of microns were prepared from spin-coated thin films of the two polymers, as well as from aqueous solutions of nanoscale polymer dots (Pdots) prepared from the two polymers. The aqueous Pdot method had superior pattern fidelity to the thin-film method and more homogeneous fluorescence at the microscale. Characterization of the mechanism of on-substrate photopatterning via aqueous Pdots revealed cross-linking between individual Pdots to grow larger discrete particles and to form networks between these enlarged particles. This simple, resin-free approach stands to reduce cost and waste, facilitate waste management, and eliminate potential fluorescence quenching effects associated with multicomponent photopatterning resin formulations.
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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 | 0.000 |
| 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".