Borophene Quantum Dots with Enhanced Nanozymatic Activity for the Detection of H<sub>2</sub>O<sub>2</sub> and Cardiac Biomarkers
Bibliographic record
Abstract
Quantum dots with unconventional properties can bring massive opportunities in multidirectional applications. Herein, borophene quantum dots (BQDs) have been synthesized that exhibit nanozymatic activity in addition to their conventional fluorescence emission nature and antibacterial activity. Fluorescence emission peak position and decay time were 450 nm and 2.8 ns, respectively. The average size of the BQDs was 5.2 nm. A significant 5-fold enhancement of the nanozymatic activity of BQDs was observed through an electrochemical process that enabled H 2 O 2 detection with an exceptional limit of detection (LOD) value of 3.5 nM. A mechanism study revealed that two factors, for example, radical scavenging and electroassisted oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) have played significant roles in enhancing detection sensitivity and reducing detection time. BQDs possessed over 80% radical scavenging ability and, under an electrochemical field, demonstrated a 50-fold increase in the oxidation reaction of TMB compared with the conventional method. These remarkable findings were further utilized for the quantification of cholesterol and choline in a simulated blood sample with LOD values of 0.13 and 0.38 μM, respectively. The groundbreaking results presented in this study have the potential to open a pathway to move from the conventional nanozymatic detection method to an electroassisted one in the future.
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 | 0.001 | 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".