Electrochemically modulating the geometry of gold nanostructures for enhanced electrochemistry and antifouling performance
Bibliographic record
Abstract
BACKGROUND: Biofouling, caused by nonspecific adsorption of biomolecules, compromises electrochemical sensor performance by blocking surface access and reducing sensitivity and reproducibility. Surface nanostructuring offers an effective route to counteract this effect and improve sensor reliability in complex biological media. However, their contributions to antifouling performance, caused by increases in electroactive surface area or the complexity of structured morphologies, have not been systematically investigated. RESULTS: We report a tuneable electrodeposition-based strategy to engineer gold nanostructures (GNS) with distinct geometries. Constant potential deposition (CPD) produced coral-shaped GNS, while pulsed-wave deposition (PWD) generated pine-needle-shaped GNS through a distinct anisotropic growth mode. Morphologies were confirmed by SEM, XPS, XRD, and water contact angle analysis. Electrochemical characterization (CV, SWV, EIS) revealed enhanced redox behaviour and reduced impedance in all GNS-modified electrodes compared to the unmodified gold-based screen-printed electrode (SPE). Pine-needle GNS demonstrated superior antifouling performance, retaining 59 % redox signal in bovine serum albumin, compared to 43 % for coral-shaped GNS. Crucially, by using a stepwise surface engineering approach with minimal variation in material composition, we demonstrated that nanostructure geometry, not just surface area, is the dominant factor governing both antifouling behaviour and electrochemical performance. A unifying relationship between electroactive surface area (ESA) and redox response was also observed across all GNS types. SIGNIFICANCE: This study highlights nanostructure shape as a key design parameter for enhancing sensor performance in biological environments. The modular deposition approach provides a robust platform for fabricating antifouling, high-sensitivity electrodes. These findings support future development of electrochemical sensors for clinical diagnostics and point-of-care applications.
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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 | 0.000 | 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) | 0.001 | 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".