Measurement of low concentration and nano-quantity hydrogen sulfide in aqueous solution: measurement mechanisms and limitations
Bibliographic record
Abstract
To measure hydrogen sulfide (H 2 S) level in biological samples in vivo on a real-time basis with high resolution is greatly needed for advancing our understanding of the biological role of H 2 S. Traditional H 2 S measurements usually need large tissue samples and complex procedures. However, H 2 S concentration is very low in human bodies and the tissue sample is limited for medical treatment purposes. There is a need to develop a new paradigm for the real-time measurement of a trace amount of hydrogen sulfide with a small amount of tissue samples or in vivo . We previously reported a method to measure low concentration H 2 S solution using carbon nanotubes and the fluorescence spectra of Raman and confocal laser scanning microscopes. We obtained that the measurement resolution with a confocal laser scanning microscope is higher than that with a Raman microscope; in particular, 10 µM concentration difference can be detected with a confocal laser scanning microscope. In this paper, we present the underlying mechanism and limitation of this method together with other traditional methods based on the theoretical analysis, which leads to the finding of further research on the measurement of low concentration and nano-quantity H 2 S solution.
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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 | 0.003 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".