Recent Advances in Inflammatory Diagnosis with Graphene Quantum Dots Enhanced SERS Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inflammatory diseases are some of the most common diseases in different parts of the world. So far, most attention has been paid to the role of environmental factors in the inflammatory process. The diagnosis of inflammatory changes is an important goal for the timely diagnosis and treatment of various metastatic, autoimmune, and infectious diseases. Graphene quantum dots (GQDs) can be used for the diagnosis of inflammation due to their excellent properties, such as high biocompatibility, low toxicity, high stability, and specific surface area. Additionally, surface-enhanced Raman spectroscopy (SERS) allows the very sensitive structural detection of analytes at low concentrations by amplifying electromagnetic fields generated by the excitation of localized surface plasmons. In recent years, the use of graphene quantum dots amplified by SERS has increased for the diagnosis of inflammation. The known advantages of graphene quantum dots SERS include non-destructive analysis methods, sensitivity and specificity, and the generation of narrow spectral bands characteristic of the molecular components present, which have led to their increased application. In this article, we review recent advances in the diagnosis of inflammation using graphene quantum dots and their improved detection of SERS. In this review study, the graphene quantum dots synthesis method, bioactivation method, inflammatory biomarkers, plasma synthesis of GQDs and SERS GQD are investigated. Finally, the detection mechanisms of SERS and the detection of inflammation are presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it