PFO sorted s-SWNT networks for optoelectronics
Bibliographic record
Abstract
The rise of efficient extraction techniques triggered a renewal of interest in semiconducting carbon nanotubes (s-SWNT) research. It represents a great interest for optoelectronics, with outstanding properties in field-effect transistor, and s-SWNT ability to efficiently emit light in the near-IR range. In particular, polyfluorene (PFO) wrapped s‑SWNT (s-SWNT@PFO) display strong photoluminescence, and could be coupled with photonic devices such as microring resonators [1,2] to control photoluminescence linewidth and enhance photoluminescence intensity. The main challenge for using s-SWNT@PFO in optoelectronics lies in the difficulty to establish good electrical contact with a PFO embedded carbon nanotube. We propose to investigate these issues by tuning the amount of PFO wrapping around s-SWNT. A low pressure annealing process is used to selectively remove PFO around s‑SWNT without burning nanotubes themselves (Figure). The resulting s-SWNT@PFO networks are then probed by AFM, Raman spectroscopy, absorption, photoluminescence and electrical experiments.
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) | 0.000 | 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) | 0.003 | 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".