The Applications of Carbon Nanotubes in Electrical and Optical Nanobiosensor
Bibliographic record
Abstract
Carbon nanotubes(CNTs) have attracted great attention in many fields due to their unique physical and electrochemical properties,such as large specific surface area,strong electron transfer capability,good adsorption performance,etc.Immobilization of enzymes,proteins,DNA and other biomacromolecules to the surface of carbon nanotubes can be achieved through covalent and non-covalent interactions,including physical absorption,electrostatic and hydrophobic interactions,which facilitates a direct,fast electron transfer from biomolecules to electrode and can be applied in electrochemical biosensors.On the other hand,carbon nanotubes have recently been applied in the design of optical biosensors.Multiple spectral estimation can be employed to quantitative analysis of the biological molecules,through mesurement of the characteritical Raman spectra and fluorescence emission of CNTs in near IR region.In this paper,the application of carbon nanotubes in the fields of electrical and optical biosensors are reviewed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".