Iridescent paper-based polymerized colloidal crystal arrays for molecular sensing
Bibliographic record
Abstract
Polymerized colloidal crystal arrays (PCCAs) are colloidal crystal arrays that are embedded in a hydrogel matrix. Because they exhibit iridescent colour that can change in response to external stimuli, they have potential to be useful sensing materials. In this research, a new testing platform was synthesized by immobilizing PCCAs on filter paper. This is possible because paper is a porous, hydrophilic, and compliant support. Two methods were developed to optimize these so-called paper-based PCCAs (PB-PCCAs, M. Li, M.Eng. thesis, McGill University). A two-step method involves crystallizing colloidal spheres on paper and then applying a hydrogel to fix the array. Here, the temperature, cast size, dispersion concentration, contact angle of the colloidal dispersion with the mould, and filter paper type all influence the crystallization quality. In a simpler one-step method, PB-PCCAs can be assembled by directly depositing colloidal spheres in pre-gel solution onto paper or gel-filled paper. The one-step method produces much more intense and uniform iridescent colour, and uses significantly less gel and nanoparticles. Both techniques have been optimized to produce superior colour and surface texture than achieved previously, thereby paving the way toward our ultimate goal of developing aptamer functionalized colloidal crystal arrays for molecular- and bio-sensing.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".