Reagentless Biomolecular Analysis Using a Nanoscale Molecular Pendulum
Bibliographic record
Abstract
Abstract The ability to sense biological inputs using self-contained devices unreliant on external reagents or reporters would open countless opportunities to collect information about our health and environment. Currently, a very limited set of molecular inputs can be detected using this type of sensor format. The development of versatile reagentless sensors that could track molecular analytes in biological fluids remains an unmet need. Here, we describe a new universal sensing mechanism that is compatible with the analysis of proteins that are important physiological markers of stress, allergy, cardiovascular health, inflammation and cancer. The sensing mechanism we developed is based on the measurement of field-induced directional diffusion of a nanoscale molecular pendulum tethered to an electrode surface and the sensitivity of electron-transfer reaction kinetics to molecular size. Using time-resolved electrochemical measurements of diffusional motion, the presence of an analyte bound to a sensor complex can be continuously tracked in real time. We show that this sensing approach is compatible with making measurements in blood, saliva, urine, tears and sweat and that the sensors can collect data in situ in living animals. The sensor platform described enables a broad range of applications in personalized health monitoring.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".