Mn<sup>2+</sup>-Assisted DNA Oligonucleotide Adsorption on Ti<sub>2</sub>C MXene Nanosheets
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
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, MoS2, and WS2, 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, Mn2+ was discovered to promote DNA adsorption on unmodified Ti2C MXene. Different from Ca2+ and Mg2+, Mn2+ can inverse the ζ-potential of the Ti2C 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 Mn2+ from the DNA/MXene complex. Finally, DNA-induced DNA desorption from the Ti2C 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.
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) | 0.003 | 0.017 |
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