Prototyping of Wrinkled Nano-/Microstructured Electrodes for Electrochemical DNA Detection
Bibliographic record
Abstract
Biosensing platforms are ideal for addressing the diagnostic needs of resource-poor areas; however, the translation of such systems from the laboratory to the point-of-need has been a slow process. Rapid prototyping methods that enable an application-specific biosensor to be created in a matter of hours from design to fabrication would expedite the clinical and field testing of such systems. Here, we demonstrate a benchtop method based on craft cutting and polymer-induced wrinkling for creating multiplexed electrochemical DNA biosensors. This fabrication method allows multiscale wrinkled electrodes with features in the millimeter to nanometer length scales to be created in a matter of hours. These wrinkled electrodes display an enhanced surface area compared to planar electrodes and are shown to be structurally tunable by changing the film thickness. We demonstrate that structural tunability of these electrodes is translatable to functional tunability as the density of surface-immobilized probe molecules can be manipulated using wrinkled electrodes of different thicknesses. Furthermore, a simple proof-of-concept electrocatalytic DNA biosensor is demonstrated for distinguishing between complementary and noncomplementary oligonucleotides.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".