Mechanistic Control of the Growth of Three-Dimensional Gold Sensors
Bibliographic record
Abstract
Three-dimensional (3D) electrodes with large surface areas are highly effective biomolecular sensors. These structures can be generated via the electrodeposition of gold inside microscale apertures patterned on the surface of a microelectronic chip. Such electrodes enable the ultrasensitive analysis of nucleic acids, proteins, and small molecules. Since the performance of these electrodes is directly related to their surface area, the ability to control their microscale morphology is critical. Here, we explore an electrochemical model based on the theory of nucleation and growth to better understand how to control the morphology of these electrodes. The insights gained from this model enabled us to create preferential conditions for the formation of different morphological features. We demonstrate for the first time that electrodeposition of 3D nanostructured microelectrodes inside a microscale aperture is governed by two stages of nucleation and growth. The first stage involves the creation of primary nuclei at the bottom of the aperture. The second stage features the generation of new nuclei upon exposure to the bulk solution. Depending on the overpotential, the deposition is then continued by either rapid growth of the original nuclei or fast growth of new nuclei. Faster electrodeposition at high overpotentials promotes directional growth, generating spiky structures. More isotropic growth is observed with low overpotentials, generating rounder features. Ultimately we determine the efficiency of DNA hybridization on a variety of structures and identify the optimal morphologies for rapid DNA–DNA duplex formation.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".