Double-Side-Coated Nanomechanical Membrane-Type Surface Stress Sensor (MSS) for One-Chip–One-Channel Setup
Bibliographic record
Abstract
With their capability for real-time and label-free detection of targets ranging from gases to biological molecules, nanomechanical sensors are expected to contribute to various fields, such as medicine, security, and environmental science. For practical applications, one of the major issues of nanomechanical sensors is the difficulty of coating receptor layers on their surfaces to which target molecules adsorb or react. To have measurable deflection, a single-side coating is commonly applied to cantilever-type geometry, and it requires specific methods or protocols, such as inkjet spotting or gold-thiol chemistry. If we can apply a double-side coating to nanomechanical sensors, it allows almost any kind of coating technique including dip coating methods, making nanomechanical sensors more useful with better user experiences. Here we address the feasibility of the double-side coating on nanomechanical sensors demonstrated by a membrane-type surface stress sensor (MSS) and verify its working principle by both finite element analysis (FEA) and experiments. In addition, simple hand-operated dip coating is demonstrated as a proof of concept, achieving practical receptor layers without any complex instrumentation. Because the double-side coating is compatible with batch protocols such as dip coating, double-side-coated MSS represents a new paradigm of one-chip-one-channel (channels on a chip are all coated with the same receptor layers) shifting from the conventional one-chip-multiple-channel (channels on a chip are coated with different receptor layers) paradigm.
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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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".