Nanobonding: A key technology for emerging applications in health and environmental sciences
Bibliographic record
Abstract
In this paper, surface-activation-based nanobonding technology and its applications are described. This bonding technology allows for the integration of electronic, photonic, fluidic and mechanical components into small form-factor systems for emerging sensing and imaging applications in health and environmental sciences. Here, we describe four different nanobonding techniques that have been used for the integration of various substrates — silicon, gallium arsenide, glass, and gold. We use these substrates to create electronic (silicon), photonic (silicon and gallium arsenide), microelectromechanical (glass and silicon), and fluidic (silicon and glass) components for biosensing and bioimaging systems being developed. Our nanobonding technologies provide void-free, strong, and nanometer scale bonding at room temperature or at low temperatures (<200 °C), and do not require chemicals, adhesives, or high external pressure. The interfaces of the nanobonded materials in ultra-high vacuum and in air correspond to covalent bonds, and hydrogen or hydroxyl bonds, respectively.
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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".