(Invited) Nanosurface Fluidic Devices for Electrochemical Sensing and Biosensing
Bibliographic record
Abstract
Diagnostics of pathogenic and genetic disease (such as cancer) at the point of need, in particular at early-stage, requires dynamic manipulation and concentration of a small number of target molecules at individual single-molecule level, which currently limit microfluidic technologies. In my lab, we focus on engineering of new approaches in lab-on-chip technology via synergistically combining nanostructured materials with fluidic sample delivery systems to enhance the sensitivity and selectivity of the detection. Nanostructured materials boost the sensor resolution and show higher biochemical sensitivity and selectivity by significant amplification of the detection sites. We investigate 1) fabrication of novel nanostructured platforms based on 3D materials such as gold and 2D materials such as graphene and molybdenum disulfide, 2) integration of nanostructures with fluid sample delivery and biological assays (based on DNA/antibody) and 3) implementation of the device for detection of small molecules, pathogenic disease, and cancer genomics. In this regard, we address fundamental questions including: optimal interface of nanostructures with fluidic devices; target isolation, preparation and concentration in fluidic devices. We have successfully implemented the nanosurface fluidic devices for rapid and quantitative detection of bacteria such as Escherichia coli (E.coli) and Methicillin-resistant Staphylococcus aureus (MRSA), electrochemical detection of small molecules such as Dopamine and optical detection of extracellular vesicles (EVs).
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".