Materials Design Strategies for Solvent-Resistant Organic Electronics
Bibliographic record
Abstract
Design of organic π-conjugated semiconducting materials is an exciting avenue of research that has already found promising applications in a wide variety of fields, ranging from stretchable electronics to bioimaging and theranostics. With favorable optoelectronic and thermomechanical properties, these materials and related devices can provide a complementary alternative to commercial silicon-based electronics. One of the most important features of organic semiconductors is their ability to be solution processed, allowing access to a wide variety of printing and solution deposition techniques inaccessible to silicon. However, the solution processability of these materials also poses challenges for the development of multilayer electronics due to potential problems such as swelling, film deformation and interfacial mixing that can occur upon successive solution deposition. Use of orthogonal (noncompatible) solvents and solvent-free deposition methods have been extensively investigated as solutions to this challenge, although the applicability of these approaches is limited by the chemical properties of the materials used. Another approach to address this problem is to focus on the materials rather than deposition methods. Through rational design, functional groups can be used to create triggered solvent resistance through covalent or dynamic intermolecular bonds. Design strategies include the incorporation of photo- and thermally cleavable functional groups in the materials, or the use of chemical additives/reagents to significantly alter the solubility of π-conjugated materials and afford solvent-resistant thin films. This spotlight article presents recent progress toward solvent-resistant organic materials with an emphasis on their use in electronic applications. Recent and key developments will be discussed from a personal perspective, providing an overview of the different approaches used to achieve solvent-resistant semiconducting materials toward the fabrication of advanced, multilayer organic electronics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".