A Paper Sensor Printed with Multifunctional Bio/Nano Materials
Bibliographic record
Abstract
We report a paper-based aptasensor platform that uses two reaction zones and a connecting bridge along with printed multifunctional bio/nano materials to achieve molecular recognition and signal amplification. Upon addition of analyte to the first zone, a fluorescently labelled DNA or RNA aptamer is desorbed from printed graphene oxide, rapidly producing an initial fluorescence signal. The released aptamer then flows to the second zone where it reacts with printed reagents to initiate rolling circle amplification, generating DNA amplicons containing a peroxidase-mimicking DNAzyme, which produces a colorimetric readout that can be read in an equipment-free manner or with a smartphone. The sensor was demonstrated using an RNA aptamer for adenosine triphosphate (a bacterial marker) and a DNA aptamer for glutamate dehydrogenase (Clostridium difficile marker) with excellent sensitivity and specificity. These targets could be detected in spiked serum or feacal samples, demonstrating the potential for testing clinical samples.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".