Stoichiometry and Dispersity of DNA Nanostructures Using Photobleaching Pair-Correlation Analysis
Bibliographic record
Abstract
A wide variety of approaches have become available for the fabrication of nanomaterials with increasing degrees of complexity, precision, and speed while minimizing cost. Their quantitative characterization, however, remains a challenge. Analytical methods to better inspect and validate the structure and composition of large nanoscale objects are required to optimize their applications in diverse technologies. Here, we describe single-molecule fluorescence-based strategies relying on photobleaching and multiple-color co-localization features toward the characterization of supramolecular structures. By optimizing imaging conditions, including surface passivation, excitation power, frame capture rate, fluorophore choice, buffer media, and antifading agents, we have built a robust method by which to dissect the structure of synthetic nanoscale systems. We showcase the use of our methods by retrieving key structural parameters of four DNA nanotube systems differing in their preparation strategy. Our method rapidly and accurately assesses the outcome of synthetic work building nano- and mesoscale architectures, providing a key tool for product studies in nanomaterial synthesis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".