Covalent and Noncovalent Functionalization of Graphene Oxide with DNA for Smart Sensing
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
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.
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.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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