Trapping, unfolding, identifying, and binding single proteins using the double-nanohole optical trap
Bibliographic record
Abstract
In this paper we describe the double nanohole laser tweezer system used to trap single nanoparticles. We cover the basic theory behind the DNH and what makes it more powerful than traditional laser tweezers commonly used for larger particles. We outline the basic setup used to reliably trap several different types of particles ranging in size from 1 nm to 40 nm. Data from several experiments is shown which displays exactly how a particle is confirmed to be trapped. We will discuss the use of autocorrelation as well as other information that can be extracted from the optical transmission in our setup and how it has been applied to the identification of protein small molecule interactions and protein binding. Other uses of the data collected from our setup will be discussed including the observation of protein folding. Finally we discuss the current developments of the process and its possible uses as a drug discovery tool, a new type of single particle nanopipette and new bio-sensors.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".