Evaluation of protein separation mechanism and pore size distribution in colloidal self‐assembled nanoparticle sieves for on‐chip protein sizing
Bibliographic record
Abstract
The separation behavior of 6.5-66 kDa proteins in silica particle array-based sieves formed by colloidal self-assembly in microchips is reported across a pore size range of 22-103 nm. The protein separation and resolution improves markedly with decreasing pore size. The variation of electrophoretic mobility with molecular weight of SDS-protein complexes and with particle size was evaluated using the Ogston sieving equation for a random pore gel structure, and using the modified Giddings equation developed by Wirth for uniform pore structures. The Wirth/Giddings equation provides the best fit for estimation of molecular weight of proteins, and demonstrates that even though experimental evidence shows there is some dispersion in measured pore sizes, these structures can best be described as having a uniform pore size.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".