Revealing and Resolving the Restrained Enzymatic Cleavage of DNA Self-Assembled Monolayers on Gold: Electrochemical Quantitation and ESI-MS Confirmation
Bibliographic record
Abstract
Herein, we report a combined electrochemical and ESI-MS study of the enzymatic hydrolysis efficiency of DNA self-assembled monolayers (SAMs) on gold, platform systems for understanding nucleic acid surface chemistry, and for constructing DNA-based biosensors. Our electrochemical approach is based on the comparison of the amounts of surface-tethered DNA nucleotides before and after exonuclease I (Exo I) incubation using electrostatically bound [Ru(NH 3 ) 6 ] 3+ as redox indicators. It is surprising to reveal that the hydrolysis efficiency of ssDNA SAMs does not depend on the packing density and base sequence, and that the cleavage ends with surface-bound shorter strands (9–13 mers). The ex-situ ESI-MS observations confirmed that the hydrolysis products for ssDNA SAMs (from 24 to 56 mers) are dominated with 10–15 mer fragments, in contrast to the complete digestion in solution. Such surface-restrained hydrolysis behavior is due to the steric hindrance of the underneath electrode to the Exo I/DNA binding, which is essential for the occurrence of Exo I-catalyzed processive cleavage. More importantly, we have shown that the hydrolysis efficiency of ssDNA SAMs can be remarkably improved by adopting long alkyl linkers (locating DNA strands further away from the substrates).
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".