(Invited) Photoswitchable Near-Infrared Emitters Based on Single-Walled Carbon Nanotube Hybrids
Bibliographic record
Abstract
Super-resolution microscopy (SRM) has set a new paradigm in the field of optical imaging by delivering images with resolution much better than the diffraction limit. We demonstrated over the last years that such approaches can be designed to understand basic excitonic processes in carbon nanotubes [1-2]. In the field of bioimaging, SRM is currently limited to the visible range, missing the near-infrared region where biological tissues are however the most transparent. One reason for this is that single-molecule photoswitchable emitters, which are the basic ingredients to achieve single-molecule SRM, have not yet been developed in the near-infrared. To fill this gap, we have recently introduced a novel type of hybrid nanomaterials consisting of single-wall carbon nanotubes covalently functionalized with photo-switching molecules that are used to control the intrinsic luminescence of the single nanotubes in the near-infrared (beyond 1 µm) [3]. Through the control of photoswitching, we demonstrate super-localization imaging of nanotubes unresolved by diffraction limited microscopy opening the route toward SRM in the near-infrared for biological applications. Photocontrol of individual near-infrared emitters will also be highly desirable for elementary optical molecular switches or information storage elements since most communication data transfer protocols are established in this spectral range. References [1] Cognet et al Nanoletters , 8 (2008) 749 - 753. [2] Danné et al. ACS Nano , 12 (2018) 6059 [3] Godin et al Science Advances, 5, (2019) eaax1166
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".