Measurement of low concentration and nano-quantity hydrogen sulfide in sera using unfunctionalized carbon nanotubes
Bibliographic record
Abstract
Hydrogen sulfide (H 2 S) is produced in small amounts by certain cells in the mammalian body and has a number of biological functions. H 2 S gas naturally produced by the body is not simply a toxic gas; it could be a vascular dilator and play a physiological role in regulating cardiovascular functions. In order to know the effects of H 2 S, it is necessary to accurately know its concentrations in the body. Conventional measurement methods have their limitations concerning the small amount and low concentration of H 2 S in the body. A new paradigm of using carbon nanotubes in H 2 S measurement expresses its potential. However, the influence of proteins in the mammalian body must be studied in the measurement of H 2 S by carbon nanotubes. In this paper, we demonstrate a successful measurement of low concentration (20 µM) and nano-quantity (0.5 µg) H 2 S in the serum by using carbon nanotubes and further with the fluorescence of confocal laser scanning microscopy and the luminescence of Raman microscopy. Statistical analysis of the experimental data shows that the relationship between concentrations and intensities is linear, which thus makes the carbon nanotube sensor highly promising for the measurement of H 2 S in sera.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 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".