Recent studies on applications of nanoresonators in sensors and molecular transportation
Bibliographic record
Abstract
Recently, interests have been orientated toward the development of nano-electromechanical systems such as nanoresonators. The ultra-high-frequency of nanoresonators facilitates a wide range of new applications such as ultra-high sensitive sensors, molecular transportation, high-frequency signal processing, biological imaging, quantum measurement and radio frequency communications. In this study, applications of nanoresonators in sensors and molecular transportation are introduced and reviewed. Studies on nanoresonator sensors made of carbon nanotubes and graphene sheets for detection of atoms/molecules based on vibration and wave propagation analyses are outlined. The principle of the nano-sensors is to detect shifts in resonant frequencies or the wave velocities in the sensors caused by surrounding foreign atoms or molecules. Furthermore, the feasibility of molecular transportation using propagation of torsional and impulse waves in nano-resonator devices is presented. An extended application of the transportation methods for building nanofiltering systems with ultra-high selectivity is survived. The article aims to provide a state-of-the-art introduction of the potential of resonators made of carbon nanotubes and graphene sheets, and inspire further applications of the nanoresonators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".