Size‐based proteins separation using polymer‐entrapped colloidal self‐assembled nanoparticles on‐chip
Bibliographic record
Abstract
We report on a facile method to stabilize colloidal self-assembled (CSA) nanoparticles packed in microchannels for high speed size-based separation of proteins. Silica nanoparticles, self-assembled in a network of microfluidic channels, were stabilized with a methacrylate polymer prepared in situ through photopolymerization. The entrapment conditions were investigated to minimize the effect of the polymer matrix on the structure of the packing and the separation properties of the CSA beds. SEM shows that the methacrylate matrix links the nanoparticles at specific sphere-sphere contact points, improving the stability of the CSA structure at high electric fields (up to at least 1800 V/cm), allowing fast and efficient separation. The %RSD of the protein migration times varied between 0.3 and 0.5% (n = 4, in 1 day) and <0.83% over a period of 7 days (n = 28 runs) in a single device, at high field strength. The overall %RSD of protein migration times from chip-to-chip across a single fabrication run was 4.3% (n = 3) and between fabrication runs was 11% (n = 35), with 87% fabrication yield, demonstrating reproducible packing and entrapment behavior. The optimized entrapped CSA beds demonstrated better separation performance (plate height, H ∼ 200 nm) than similarly prepared on-chip CSA beds without the polymer entrapment. Polymer-entrapped CSA beds also exhibited superior protein resolving power: the minimum resolvable molecular weight difference of proteins in the polymer-entrapped CSA bed is 0.6 kDa versus ∼9 kDa for the native silica CSA bed (i.e. without polymer entrapment).
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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) | 0.001 | 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 | 0.001 | 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".