Graphene-based Nanocomposites for Detection of Small Biomolecules (AA, DA, UA, and Trp)
Bibliographic record
Abstract
Medical diagnostics have been expanded to new dimensions by graphene and its derivatives due to their unique chemical and physical characteristics, including excellent electrical and thermal conductivity, a large specific surface area, and easy biofunctionalization combined with low fabrication costs. Thereby, graphene-based materials have been widely used as a promising nanoplatform for nano-scale sensor and biosensor fabrication. Moreover, the molecular structures of graphene-based materials, especially oxygenated functional groups, facilitate their chemical functionalization and enable combining graphene-based nanoparticles with other inorganic and organic nanomaterials, biological polymers, and quantum dots to form a wide range of nanocomposites with improved sensitivity and selectivity for sensor applications. This chapter focuses on the synthesis and characterization of graphene-based nanocomposites for quantitative detection of significant small biomolecules, including uric acid (UA), ascorbic acid (AA), dopamine (DA), and tryptophan (Trp), in human metabolism. It also updates readers with recent advances and scientific progress in using graphene-based nanocomposites in sensing and biosensing applications. Finally, the future prospects of graphene-based biosensor development, along with their challenges and potential answers, are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".