Micro-opto mechanical biosensors for enzymatic detection
Bibliographic record
Abstract
Miniaturization and highly accurate detection technologies are key factors to advancing sensor performance and utility and the search for such technologies promises accomplishments with the advent of Micro Electro Mechanical Systems (MEMS) structures. MEMS is an enabling technology that can create integrated devices with mechanical, optical and electronic components. One of the fields where these micro-devices are successfully widespread is that of medical care where micro-machined cantilever sensors are found to be the ideal candidates for bio-sensing applications. These micro-machined cantilevers have been proposed as mechanical transducers for different sensing applications. These sensing surfaces are of interest in the development of novel cantilever-based biosensors. The fascinating aspect about these transducers is that they bend due to modifications in nano-mechanical interactions between neighboring molecules which curve the beam and that curvature can be optically detected. In this paper, PVDF-cantilevers are coated with 2 sets of antibody and antigen on one side, which respond with specific deflection signatures to each other and their intermolecular nano-mechanics bend the cantilever. The first set of antibody and antigen used here are rabbit skeletal muscle Troponin C (TnC) and Honey Bee Venom Melittin (ME) prepared in 50mM KCl and 50mM Tris-HCl buffer at a pH of 7.5 in a 1:1 ratio. The next set used were Horse Raddish Peroxide (HRP) and Hydrogen Peroxide (H2O2) prepared to get 10mg per 1ml of 0.1M Potassium Phosphate dibasic (K2HPO4) and diluted hydrochloric acid to get pH of 6.0. The optical system includes a laser source and a Position Sensitive Detector (PSD) which is used to readout deflections of the PVDF-cantilevers. The behavior of the cantilevers was also monitored with the enzymes under the influence of voltage. The classical problem of evaluating the tip deflection of the cantilever beam is analyzed involving the relation between movement recorded by the PSD, the tip deflection and angle of incidence. The combined results provide valuable information on the development of an optimizing sensing element that would enable life saving treatments of patients suffering from Acute Myocardial Infarction (AMI).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".