A Graphene-Based Biosensing Platform Based on the Release of DNA Probes and Rolling Circle Amplification
Bibliographic record
Abstract
We report a versatile biosensing platform capable of achieving ultrasensitive detection of both small-molecule and macromolecular targets. The system features three components: reduced graphene oxide for its ability to adsorb single-stranded DNA molecules nonspecifically, DNA aptamers for their ability to bind reduced graphene oxide but undergo target-induced conformational changes that facilitate their release from the reduced graphene oxide surface, and rolling circle amplification (RCA) for its ability to amplify a primer-template recognition event into repetitive sequence units that can be easily detected. The key to the design is the tagging of a short primer to an aptamer sequence, which results in a small DNA probe that allows for both effective probe adsorption onto the reduced graphene oxide surface to mask the primer domain in the absence of the target, as well as efficient probe release in the presence of the target to make the primer available for template binding and RCA. We also made an observation that the circular template, which on its own does not cause a detectable level of probe release from the reduced graphene oxide, augments target-induced probe release. The synergistic release of DNA probes is interpreted to be a contributing factor for the high detection sensitivity. The broad utility of the platform is illustrated though engineering three different sensors that are capable of achieving ultrasensitive detection of a protein target, a DNA sequence and a small-molecule analyte. We envision that the approach described herein will find useful applications in the biological, medical, and environmental fields.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".