Au Nanostructured Surfaces for Electrochemical and Localized Surface Plasmon Resonance-Based Monitoring of α-Synuclein–Small Molecule Interactions
Bibliographic record
Abstract
In this proof-of-concept study, the fabrication of novel Au nanostructured indium tin oxide (Au-ITO) surfaces is described for the development of a dual-detection platform with electrochemical and localized surface plasmon resonance (LSPR)-based biosensing capabilities. Nanosphere lithography (NSL) was applied to fabricate Au-ITO surfaces. Oligomers of α-synuclein (αS) were covalently immobilized to determine the electrochemical and LSPR characteristics of the protein. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were performed using the redox probe [Fe(CN)6](3-/4-) to detect the binding of Cu(II) ions and (-)-epigallocatechin-3-gallate (EGCG) to αS on the Au-ITO surface. Electrochemical and LSPR data were complemented by Thioflavin-T (ThT) fluorescence, surface plasmon resonance imaging (SPRi), and transmission electron microscopy (TEM) studies. EGCG was shown to induce the formation of amorphous aggregates that decreased the electrochemical signals. However, the binding of EGCG with αS increased the LSPR absorption band with a bathochromic shift of 10-15 nm. The binding of Cu(II) to αS enhanced the DPV peak current intensity. NSL fabricated Au-ITO surfaces provide a promising dual-detection platform to monitor the interaction of small molecules with proteins using electrochemistry and LSPR.
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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) | 0.001 | 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 | 0.001 | 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".