DNA Detection on Plastic: Surface Activation Protocol To Convert Polycarbonate Substrates to Biochip Platforms
Bibliographic record
Abstract
A mild and efficient surface activation protocol to convert polycarbonate (PC) substrates, e.g., plastic bases of compact disks, to biochip platforms for DNA probe immobilization and target detection is described. The preparation procedure (activation, patterning, and coupling) is simple and effective; the on-chip hybridization is sensitive and selective. Particularly, UV/ozone treatment of PC sheets produces a hydrophilic surface with a high density of reactive carboxylic acid groups [(4.8 +/- 0.2) x 10-10 mol/cm2] in less than 10 min at ambient conditions, and no significant aging or physical damage to the substrate is observed. Covalent immobilization of DNA probes via both passive (reagent-less photopatterning and coupling in bulk solution phase) and flow-through (creation of microarrays with microfluidic channel plates) procedures has been demonstrated. Subsequent hybridization shows uniform and strong fluorescent signals for complementary target DNA and allows clear discrimination between fully complementary targets and strands with a single base-pair mismatch. The surface chemistry described herein will facilitate the development of disposable plastic biochips (not limited to DNA microarrays) and the fabrication of biomedical devices that are readable with conventional optical drives.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".