Mn<sup>2+</sup>-Assisted DNA Oligonucleotide Adsorption on Ti<sub>2</sub>C MXene Nanosheets
Bibliographic record
Abstract
As a new type of 2D nanomaterial, MXene (transition metal carbide/nitride) nanosheets are already widely used in catalysis, sensing, and energy research. DNA is a popular sensing molecule. Compared to other 2D materials such as graphene oxide, MoS 2, and WS 2, few fundamental studies were carried out on DNA adsorption by MXene. Due to its exfoliation and delamination process, the surface of MXene is abundant in −F, −OH, and −O– groups, rendering the surface negatively charged and repelling DNA. In previous studies, surface modification of MXene was performed to promote DNA adsorption. Herein, Mn 2+ was discovered to promote DNA adsorption on unmodified Ti 2 C MXene. Different from Ca 2+ and Mg 2+, Mn 2+ can inverse the ζ-potential of the Ti 2 C MXene to positive. DNA mainly uses its phosphate backbone for adsorption, while its bases contribute significantly less. In addition, delayed DNA desorption was observed through the addition of inorganic phosphate due to the formation of manganese phosphate to gradually extract Mn 2+ from the DNA/MXene complex. Finally, DNA-induced DNA desorption from the Ti 2 C MXene can hardly distinguish the complementary DNA from a random DNA, which is very different from that for graphene oxide. This difference is likely due to the distinct surface chemistry between the MXene and graphene oxide.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".