A Fresh Perspective on the Thermal Stability of Thiol-Based DNA SAMs
Bibliographic record
Abstract
The stability of gold surfaces modified with DNA self assembled monolayers has drawn increasing attention as the lifespan of the created sensors is an important consideration for biosensor development and applications.1–4 In this study, we investigated the thermal stability of a mixed self assembled monolayer (SAMs) composed of a short alkythiol and alkythiol modified DNA immobilized on a single crystal gold bead electrode at either open circuit potential (OCP) or by electrodeposition.5,6 Using in-situ fluorescence microscopy and electrochemical measurements, different SAMs with varied surface properties were studied, and the relative stability towards increasing temperatures was evaluated. The results show that the stability strongly depends on the preparation method, the DNA coverage and on the underlying surface crystallography. The single crystal bead electrode enabled study of a number of surface crystallographies in a self-consistent manner. For the surfaces made without potential control, the {111} regions were the least thermally stable surface and completely desorbed from the gold surface after heating to 85 °C in some cases. On the other hand, {100} is the most stable region on which SAMs can survive higher than 85 °C in buffer. The {110}, {210} and {311} surfaces were also studied. The surfaces with hexagonal symmetry were less stable than those with square or rectangular symmetry. The different methods used to prepare the DNA SAM also showed significant changes in the stability. In general, SAMs made with potential assisted deposition showed higher stability than those made at OCP. This unique finding can be used to comment on typical sensor surface which are polycrystalline flat gold surfaces composed of a variety of grains and grain boundaries. These results may help to explain the irreproducible results from the DNA SAMs and such as the reported inconsistent results during DNA mismatch discrimination while the solution was heated during experiment.7 Modifying the method used to prepare the DNA SAMs and carefully controlling the composition of the gold substrate may result in significant improvements in the SAM stability. Moreover, increasing the thermal stability will result in longer term storage stability.8,9 References (1) Brittain, W. J.; Brandsetter, T.; Prucker, O.; Rühe, J. ACS Appl. Mater. Interfaces 2019, 11, 39397–39409. (2) Civit, L.; Fragoso, A.; O’Sullivan, C. K. Electrochem. commun. 2010, 12, 1045–1048. (3) Flechsig, G. U.; Peter, J.; Hartwich, G.; Wang, J.; Gründler, P. Langmuir 2005, 21, 7848–7853. (4) Ge, D.; Wang, X.; Williams, K.; Levicky, R. Langmuir 2012, 28, 8446–8455. (5) Leung, K. K.; Gaxiola, A. D.; Yu, H.-Z.; Bizzotto, D. Electrochim. Acta 2018, 261, 188–197. (6) Yu, Z. L.; Casanova-Moreno, J.; Guryanov, I.; Maran, F.; Bizzotto, D. J. Am. Chem. Soc. 2015, 137, 276–288. (7) Xu, X.; Makaraviciute, A.; Kumar, S.; Wen, C.; Sjödin, M.; Abdurakhmanov, E.; Danielson, U. H.; Nyholm, L.; Zhang, Z. Anal. Chem. 2019, 91, 14697–14704. (8) McAteer, K.; Simpson, C. E.; Gibson, T. D.; Gueguen, S.; Boujtita, M.; El Murr, N. J. Mol. Catal. - B Enzym. 1999, 7, 47–56. (9) Young Choi, J.; Yang, I. M. Pharm. Anal. Acta 2016, 07 . Figure 1 Figure 1
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".