Unconventional Nanofabrication for Supramolecular Electronics
Bibliographic record
Abstract
The scientific effort toward achieving a full control over the correlation between structure and function in organic and polymer electronics has prompted the use of supramolecular interactions to drive the formation of highly ordered functional assemblies, which have been integrated into real devices. In the resulting field of supramolecular electronics, self-assembly of organic semiconducting materials constitutes a powerful tool to generate low-dimensional and crystalline functional architectures. These include 1D nanostructures (nanoribbons, nanotubes, and nanowires) and 2D molecular crystals with tuneable and unique optical, electronic, and mechanical properties. Optimizing the (opto)electronic properties of organic semiconducting materials is imperative to harness such supramolecular structures as active components for supramolecular electronics. However, their integration in real devices currently represents a significant challenge to the advancement of (opto)electronics. Here, an overview of the unconventional nanofabrication techniques and device configurations to enable supramolecular electronics to become a real technology is provided. A particular focus is put on how single and multiple supramolecular fibers and gels as well as supramolecularly engineered 2D materials can be integrated into novel vertical or horizontal junctions to realize flexible and high-density multifunctional transistors, photodetectors, and memristors, exhibiting a set of new properties and excelling in their performances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".