Improving the Photostability of Red- and Green-Emissive Single-Molecule Fluorophores via Ni<sup>2+</sup> Mediated Excited Triplet-State Quenching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Methods to improve the photostability/photon output of fluorophores without compromising their signal stability are of paramount importance in single-molecule fluorescence (SMF) imaging applications. We show herein that Ni 2+ provides a suitable photostabilizing agent for three green-emissive (Cy3, ATTO532, Alexa532) and three red-emissive (Cy5, Alexa647, ATTO647N) fluorophores, four of which are regularly utilized in SMF studies. Ni 2+ works via photophysical quenching of the triplet excited state eliminating the potential for reactive intermediates being formed. Measurements of survival time, average intensity, and mean number of photons collected for the six fluorophores show that Ni 2+ increased their photostability 10- to 45-fold, comparable to photochemically based systems, without compromising the signal intensity or stability. Comparative studies with existing photostabilizing strategies enabled us to score different photochemical and photophysical stabilizing systems, based on their intended application. The realization that Ni 2+ allowed achieving a significant increase in photon output both for green- and red-emissive fluorophores positions Ni 2+ as a widely applicable tool to mitigate photobleaching, most suitable for multicolor single-molecule fluorescence studies.
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.
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 | 0.001 |
| 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 it