Evaluation of Different Single-Walled Carbon Nanotube Surface Coatings for Single-Particle Tracking Applications in Biological Environments
Bibliographic record
Abstract
Fluorescence imaging of biological systems down to the single-molecule level has generated many advances in cellular biology. For applications within intact tissue, single-walled carbon nanotubes (SWCNTs) are emerging as distinctive single-molecule nanoprobes, due to their near-infrared photoluminescence properties. For this, SWCNT surfaces must be coated using adequate molecular moieties. Yet, the choice of the suspension agent is critical since it influences both the chemical and emission properties of the SWCNTs within their environment. Here, we compare the most commonly used surface coatings for encapsulating photoluminescent SWCNTs in the context of bio-imaging applications. To be applied as single-molecule nanoprobes, encapsulated nanotubes should display low cytotoxicity, and minimal unspecific interactions with cells while still being highly luminescent so as to be imaged and tracked down to the single nanotube level for long periods of time. We tested the cell proliferation and cellular viability of each surface coating and evaluated the impact of the biocompatible surface coatings on nanotube photoluminescence brightness. Our study establishes that phospholipid-polyethylene glycol-coated carbon nanotube is the best current choice for single nanotube tracking experiments in live biological samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".