Aptamer-Functionalized Hydrogel Microparticles for Fast Visual Detection of Mercury(II) and Adenosine
Bibliographic record
Abstract
With a low optical background, high loading capacity, and good biocompatibility, hydrogels are ideal materials for immobilization of biopolymers to develop optical biosensors. We recently immobilized mercury and lead binding DNAs within a monolithic gel and demonstrated ultrasensitive visual detection of these heavy metals. The high sensitivity was attributed to the enrichment of the analytes into the gels. The signaling kinetics was slow, however, taking about 1 h to obtain a stable optical signal because of a long diffusion distance. In this work, we aim to understand the analyte enrichment process and improve the signaling kinetics by preparing hydrogel microparticles. DNA-functionalized gel beads were synthesized using an emulsion polymerization technique and most of the beads were between 10 and 50 μm. Acrydite-modified DNA was incorporated by copolymerization. Visual detection of 10 nM Hg(2+) was still achieved and a stable signal was obtained in just 2 min. The gel beads could be spotted to form a microarray and dried for storage. A new visual sensor for adenosine was designed and immobilized within the gel beads. The adenosine aptamer binds its target about 1000-fold less tightly compared to the mercury binding DNA, allowing a comparison to be made on analyte enrichment by aptamer-functionalized hydrogels.
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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 | 0.000 |
| 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".