Effect of Surface Modification on Protein Deposition in Desiccated Droplets
Bibliographic record
Abstract
Detection of fluorescent signals from desiccated droplets is a useful tool for the analysis of microarray devices. This investigation presents an optical method for the observation of physical structures and protein deposition in desiccated droplets. Greyscale images of droplets containing red fluorescent protein solutions in water were recorded after desiccation. Images were obtained under both white and fluorescent light for droplets desiccated on glass and Teflon. Spots desiccated on glass were twice the size of those desiccated on Teflon. As such, average physical deposition and protein concentration was higher for Teflon spots. The average fluorescence intensity for spots desiccated on Teflon were four times greater than those desiccated on glass. For spots desiccated on glass, physical deposition and protein concentration increased with radial position, consistent with a coffee-ring pattern. The local maximum fluorescence intensity was highest at the center of the droplet. Protein deposition then decreased with increasing radius before increasing again toward the edge of the spot. These results suggest that desiccation of protein laden droplets on hydrophobic coatings, such as Teflon, may increase sensitivity of fluorescent protein detection while improving the uniformity of the fluorescent signal measured from the droplet.
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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 | 0.000 | 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) | 0.001 | 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".