Graphdiyne Quantum Dots for H<sub>2</sub>O<sub>2</sub> and Dopamine Detection
Bibliographic record
Abstract
The fluorescence and nanozymatic nature of quantum dots (QDs) in a single entity provide an excellent opportunity for the development of sensitive fluorescence and colorimetric biosensing and bioimaging approaches. Herein, the hydrothermal synthesis method of fluorescent graphdiyne quantum dots (GDY QDs) from graphdiyne sheets is presented. This research reports, for the first time, the nanozymatic activity of GDY QDs besides their conventional fluorescent nature, which catalyzes the oxidation of H 2 O 2 through the peroxidase biomimetic nature and enables detection of H 2 O 2 and dopamine. The peaks of the ultraviolet–visible (UV–vis) spectrum and the fluorescence emission of GDY QDs are located at 395 and 450 nm, respectively. The X-ray photoelectron spectroscopic (XPS) results reveal that GDY and GDY QDs have similar carbon skeletons. Moreover, the intensity of C–O and C═O peaks in GDY QDs is stronger than that of GDY, suggesting the successful surface oxidation of GDY, which is crucial for hydrophilicity and catalytic activity. Kinetic analysis reveals that as-synthesized GDY QDs exhibit Michaelis–Menten kinetic behavior. The terephthalic acid and DPPH test confirms the hydroxyl radical formation and scavenging nature of GDY QDs, playing a significant role in enhancing the nanozymatic activity. The biomimetic nature of GDY QDs is applied for H 2 O 2 and dopamine detection, and limit of detection (LOD) values of 0.13 and 8.65 μM are obtained for H 2 O 2 and dopamine, respectively. The findings of this study will open a door to the development of carbon-based nanocrystals with an enzyme-mimicking nature that can lead to the development of analytical approaches, in particular, colorimetric nanobiosensors.
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 | 0.000 |
| 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".