Single-step fabrication of electrodes with controlled nanostructured surface roughness using optically-induced electrodeposition
Bibliographic record
Abstract
Abstract The customized fabrication of microelectrodes from gold nanoparticles (AuNPs) has attracted much attention due to their numerous applications in chemistry and biomedical engineering, such as for surface-enhanced Raman spectroscopy (SERS) and as catalyst sites for electrochemistry. Herein, we present a novel optically-induced electrodeposition (OED) method for rapidly fabricating gold electrodes which are also surface-modified with nanoparticles in one single step. The electrodeposition mechanism, with respect to the applied AC voltage signal and the elapsed deposition time, on the resulting morphology and particle sizes was investigated. The results from SEM and AFM analysis demonstrated that 80–200 nm gold particles can be formed on the surface of the gold electrodes. Simultaneously, both the size of the nanoparticles and the roughness of the fabricated electrodes can be regulated by the deposition time. Compared to state-of-the-art methods for fabricating microelectrodes with AuNPs, such as nano-seed-mediated growth and conventional electrodeposition, this OED technique has several advantages including: (1) electrode fabrication and surface modification using nanoparticles are completed in a single step, eliminating the need for prefabricating micro electrodes; (2) the patterning of electrodes is defined using a digitally-customized, projected optical image rather than using fixed physical masks; and (3) both the fabrication and surface modification processes are rapid, and the entire fabrication process only requires less than 6 s.
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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 | 0.000 | 0.000 |
| 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".