Using Carbon Nanotubes to Absorb Low-Concentration Hydrogen Sulfide in Fluid
Bibliographic record
Abstract
Hydrogen sulfide is a colorless and flammable gas under room temperature. Usually hydrogen sulfide is considered to be toxic; however, the recent research revealed that hydrogen sulfide in the cardiovascular system plays the role of a vascular dilator. The physiological role of hydrogen sulfide depends on its in vivo level. As such, the measurement of hydrogen sulfide with nano-quantity resolution becomes an important subject. Existing methods generally require bulky samples and are invasive and offline. It will be significantly helpful to measure hydrogen sulfide with a small amount of tissue in a noninvasive method The first attempt was to take a blood or serum sample with a trace amount to examine the interaction between hydrogen sulfide and carbon nanotube. The carbon nanotube is chosen because of a known fact that hydrogen sulfide can be adsorbed by activated carbon. The carbon nanotube is an excellent activated carbon in this regard. Fluorescence intensity of the carbon nanotube with and without immersion of it in a hydrogen sulfide medium was examined in the study. It was found that the intensities increase as the concentrations of hydrogen sulfide increase. Furthermore, the concentration of 10 microM hydrogen sulfide in water was successfully measured.
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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 | 0.001 | 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".