Graphene-Based Biosensors for Food Analysis
Bibliographic record
Abstract
Recent advances in bionanotechnology and its integration in a variety of areas including biosensors have resulted in the development of novel sensing platforms with highly improved performance. There has been great interest recently in the integration of nanomaterials and biomolecules for the development of biosensor devices. Among these nanomaterials, graphene shows unique electronic, mechanical and thermal properties. The potential harmful effect of food contaminants on human health and the subsequent need to detect them have led to significant interest in the development of graphene-based biosensors for this purpose. In this chapter, we discuss advances in the field of graphene-based biosensors for food safety. First, we briefly discuss the different preparation methods and properties of graphene and graphene-related materials (graphene oxide and reduced graphene oxide). Graphene functionalization using covalent and non-covalent approaches, an important step for biosensor fabrication, is also described. Then recent developments in the use of graphene in biosensors for allergens, small molecules, and pathogens in foodstuff are discussed. Finally, future perspectives on the biosensing applications of graphene in food safety are briefly described.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".