Redox dye-mediated fluorescence energy transfer of carbon nanotube based nanosensors
Bibliographic record
Abstract
Single-walled carbon nanotubes (SWCNTs) exhibit non-photobleaching, near-infrared (NIR) fluorescence suitable for bioimaging applications. In particular, SWCNT fluorescence quenching induced by biopolymer dispersants facilitates flexible design of molecular nanosensors, yet challenges remain in analyte selectivity and lack of rational design strategies. A sought-after alternative to haphazard molecular modulation of SWCNT-based fluorescence is to couple the movement of a quencher to the SWCNT surface, enabling fluorescence energy transfer to modulate molecular recognition with high selectivity. This study presents the rational design of SWCNT-based nanosensors that leverages the unique properties of methylene blue (MB)-proximity mediated fluorescence quenching. MB-SWCNT based nanosensors exhibit 1- stability in redox environments and 2- analyte-specific displacement-driven fluorescence modulation. By designing hybridization-induced displacement of MB-conjugated ssDNA from the SWCNT surface, we calculate that SWCNT fluorescence modulation can occur within a 6.8 nm fluorescence resonance energy transfer distance from the SWCNT surface, and develop a robust and versatile platform to synthesize oligonucleotide nanosensors with tunable ΔF/F0 of up to 150%. Building upon this strategy, we developed four distinct nanosensors capable of selectively detecting tobacco mosaic virus (TMV) viral RNA fragments, which successfully differentiated TMV-infected plants from mock controls. Finally, we demonstrate the potential expansion of our design to target a wider scope of biomolecules using the biotin-avidin system as a model. By attaching biotin to MB-conjugated ssDNA, we enabled selective and robust responses to biotin-binding proteins through the same MB displacement mechanism as our oligonucleotide nanosensors. Therefore, we herein present a generalizable platform that enables rational engineering of SWCNT NIR fluorescence intensity through MB distance-dependent fluorescence energy transfer, overcoming the intrinsic selectivity challenges of current SWCNT nanosensors.
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) | 0.000 | 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".