Capillary-Assisted Molecular Pendulum Bioanalysis
Bibliographic record
Abstract
The development of robust biosensing strategies that can be easily implemented in everyday life remains a challenge for the future of modern biosensor research. While several reagentless approaches have attempted to address this challenge, they often achieve user-friendliness through sacrificing sensitivity or universality. While acceptable for certain applications, these trade-offs hinder the widespread adoption of reagentless biosensing technologies. Here, we report a novel approach to reagentless biosensing that achieves high sensitivity, rapid detection, and universality using the SARS-CoV-2 virus as a model target. Universality is achieved by using nanoscale molecular pendulums, which enables reagentless electrochemical biosensing through a variable antibody recognition element. Enhanced sensitivity and rapid detection are accomplished by incorporating the coffee-ring phenomenon into the sensing scheme, allowing for target preconcentration on a ring-shaped electrode. Using this approach, we obtained limits of detection of 1 fg/mL and 20 copies/mL for the SARS-CoV-2 nucleoproteins and viral particles, respectively. In addition, clinical sample analysis showed excellent agreement with Ct values from PCR-positive SARS-CoV-2 patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".