Aggregation‐Induced Quenching of Carbon Dots for Detection of Nitric oxide
Bibliographic record
Abstract
Abstract This study reports the synthesis of three types of carbon dots (CDs) prepared using citric acid as a carbon source and three different biomolecules (L‐serine, L‐threonine, and adenine) individually as an amine source. The obtained nanomaterials were characterized by powder XRD, TEM, FTIR, 13 C NMR, distortionless enhancement by polarization transfer using a 135‐degree decoupler pulse (DEPT 135), dynamic light scattering (DLS), and Small‐Angle X‐ray Scattering (SAXS) techniques. After ensuring the formation of desired structures, prepared CDs were used for exploring the detection capabilities of different reactive nitrogen species and reactive oxygen species. Following the screening of their detection capabilities specifically for nitric oxide, different sensing parameters viz the limit of detection, quenching constant, and interferences were evaluated. Among the synthesized CDs, the lowest detection limit of 0.1 μM was determined for the serine‐derived dots while 0.12 and 0.19 μM was obtained for adenine and threonine‐derived dots, respectively. Eventually, a possible sensing mechanism for the detection of NO by the prepared nanomaterials is proposed.
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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) | 0.000 | 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".