The Carbon Nanotube Based Micro Bubble Generator in Micro Channel with Dynamic Fluid
Bibliographic record
Abstract
Based on the previous study of micro bubble generation using carbon nanotube heating elements with uW power input in static droplet of water, the dynamic characteristic of micro bubble generated by CNTs heater draw us much attention. In this summary, the micro bubble transportation, diameter control in micro channel and the departure of micro bubble from CNTs heater by pulse current stimulation in water tank will be presented. On the one hand, PDMS, after fabricating micro channel on it, will be bonded to the silicon substrate by oxygen plasma to form micro channel device. In our research, we could observe that the movement of vapor bubble along the micro channel by pulse flow or constant flow. The power consumption for bubble generation in micro channel is relative higher than that in static droplet of water, but it keeps staying around 275 uW. Furthermore we also use the injected cool water to control the diameter of the micro bubble. On the other hand, from micro bubble departure in water tank experiment, we observe that the micro bubble has shake response by pulse current input on the CNTs heater, which accelerate the departure of micro bubble. With low power consumption, dynamic transportation and controllable diameter, the micro bubble generated by CNTs heater in micro channel offers a promising future in lots of application, e.g., micro manipulation and micro transportation.
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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 | 0.000 | 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) | 0.001 | 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 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".