Covalent and Noncovalent Functionalization of Graphene Oxide with DNA for Smart Sensing
Bibliographic record
Abstract
Interfacing nanomaterials with DNA has resulted in the development of numerous biosensors, optimized for different targets and applications. Of all nanomaterials, graphene oxide (GO) has emerged as a prime sensing platform due to its high specific surface area, good aqueous stability, varied functional groups and desirable surface, and electrical and optical properties. This review starts with an introduction of GO and describes its physical and chemical properties. Then, the general strategies of interfacing DNA and GO to develop sensors are discussed. The trends in GO/DNA biosensor development are organized into classes based on the mode of DNA interaction with GO (physisorbed vs chemisorbed). Due to the intermediate DNA adsorption strength on GO, most of the sensors developed utilize physisorption of DNA to GO. Even within the realm of physisorbed probes, there are multiple sensing methods: direct adsorption, inhibited adsorption, competitive adsorption with the use of blocking agents, and tethered adsorption containing a strongly adsorbing block of DNA. Covalently linked DNA probes are also used to increase the biosensor stability. Each of these sensors has its advantages and disadvantages and the designs are discussed with representative examples in detail.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".