Fluorescent sp3 Defect-Tailored Carbon Nanotubes Enable NIR-II Single Particle Imaging in Live Brain Slices at Ultra-Low Excitation Doses
Bibliographic record
Abstract
Abstract Cellular and tissue imaging in the second near-infrared window (NIR-II, ~1000–1350 nm) is advantageous for in vivo studies because of low light extinction by biological constituents at these wavelengths. However, deep tissue imaging at the single molecule sensitivity has not been achieved in the NIR-II window due to lack of suitable bio-probes. Single-walled carbon nanotubes have emerged as promising near-infrared luminescent molecular bio-probes; yet, their inefficient photoluminescence (quantum yield ~1%) drives requirements for sizeable excitation doses (~1–10 kW/cm 2 ) that are significantly blue-shifted from the NIR-II region (<850 nm) and may thus ultimately compromise live tissue. Here, we show that single nanotube imaging can be achieved in live brain tissue using ultralow excitation doses (~0.1 kW/cm 2 ), an order of magnitude lower than those currently used. To accomplish this, we synthesized fluorescent sp 3 -defect tailored (6,5) carbon nanotubes which, when excited at their first order excitonic transition (~985 nm) fluoresce brightly at ~1160 nm. The biocompatibility of these functionalized nanotubes, which are wrapped by encapsulation agent (phospholipid-polyethylene glycol), is demonstrated using standard cytotoxicity assays. Single molecule photophysical studies of these biocompatible nanotubes allowed us to identify the optimal luminescence properties in the context of biological imaging.
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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) | 0.000 | 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 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".