Comparison of MoS<sub>2</sub>, WS<sub>2</sub>, and Graphene Oxide for DNA Adsorption and Sensing
Bibliographic record
Abstract
Interfacing DNA with two-dimensional (2D) materials has been intensely researched for various analytical and biomedical applications. Most of these studies have been performed on graphene oxide (GO) and two metal dichalcogenides, molybdenum disulfide (MoS 2 ) and tungsten disulfide (WS 2 ); all of them can all adsorb single-stranded DNA. However, they use different surface forces for adsorption based on their chemical structures. In this work, fluorescently labeled DNA oligonucleotides were used and their adsorption capacities and kinetics were studied as a function of ionic strength, DNA length, and sequence. Desorption of DNA from these surfaces was also measured. DNA is more easily desorbed from GO by various denaturing agents, whereas surfactants yield more desorption from MoS 2 and WS 2 . Our results are consistent with the fact that DNA can be adsorbed by GO via π–π stacking and hydrogen bonding, and MoS 2 and WS 2 mainly use van der Waals force for adsorption. Finally, fluorescent DNA probes were adsorbed by these 2D materials for detecting complementary DNA. For this assay, GO gave the highest sensitivity, whereas they all showed a similar detection limit. This study has enhanced our fundamental understanding of DNA adsorption by two important types of 2D materials and is useful for further rational optimization of their analytical and biomedical applications.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".