Preparing DNA SAM Electrochemical Sensors Using Potential Assisted Deposition Methods. Controlling the Coverage and Local Organization of the DNA in the SAM.
Bibliographic record
Abstract
Typically, DNA SAMs used for electrochemical based sensing are prepared without controlling the gold electrode potential[1]. Applying a potential during DNA deposition enables control over the coverage and DNA organization[2-4]. These characteristics are determined by the composition of the immobilization buffer, the immersion time, the DNA concentration, the extent of backfilling with a small alkylthiol and whether deposition occurs on a bare or alkythiol coverage gold surface[3-4]. Few studies have explored the influence of the underlying surface crystallography. Here, we show the possibilities of using electrochemical potential control to manipulate the characteristics of the DNA SAM. Moreover, the use of a single crystal gold bead electrode, coupled with the use of a fluorophore tagged DNA and fluorescence microscopy, enables examination of the influence of the underlying surface crystallography on the DNA SAM prepared. Various constant potentials were used as well as using a square-wave potential perturbation during deposition. There are significant differences in the DNA SAM produced under these conditions. In some cases, the DNA coverage was low on the <111> surfaces, higher on more open or atomically rough regions. In other cases, the coverage was higher on the <100> surfaces as compared to the other regions. The use of constant positive or negative potentials (vs SCE) resulted in significant differences in the relative coverages among regions of different atomic arrangements (Fig 1). In addition, a significant difference was observed between immobilization buffers that contain Cl and those that are Cl free. Also, the use of constant or square-wave potential perturbations resulted in significantly different local organizations of DNA in the SAM. Examples of these types of DNA SAM surfaces will be presented with some conclusions on the implications for use on polycrystalline planar gold surfaces. References: [1] Xiao, Y.; Lai, R. Y.; Plaxco, K. W. Preparation of Electrode-Immobilized, Redox-Modified Oligonucleotides for Electrochemical DNA and Aptamer-Based Sensing. Nat. Protoc. 2007 , 2 (11), 2875–2880. [2] Jambrec, D.; Kayran, Y. U.; Schuhmann, W. Controlling DNA/Surface Interactions for Potential Pulse‐Assisted Preparation of Multi‐Probe DNA Microarrays. Electroanal. 2019 , 410 , 3. [3] Leung, K. K.; Gaxiola, A. D.; Yu, H. Z.; Bizzotto, D. Tailoring the DNA SAM Surface Density on Different Surface Crystallographic Features Using Potential Assisted Thiol Exchange. Electrochim. Acta 2018 , 261 , 188–197. [4] Leung, K. K.; Yu, H. Z.; Bizzotto, D. Electrodepositing DNA Self-Assembled Monolayers on Au: Detailing the Influence of Electrical Potential Perturbation and Surface Crystallography. ACS Sens. 2019 , 4 (2), 513–520. Figure 1: Figure 1
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 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".