Optimization of a gold electrodeposited platform for the development of electrochemical immunosensors: The case of study of acute kidney injury
Bibliographic record
Abstract
In this work, we present a systematic approach for the optimization of a stable and reproducible platform for the development of unlabelled immunosensors based on electrodeposited (ED) gold nanoparticles (AuNPs) on screen-printed carbon electrodes (SPCEs). The modification was performed in a [AuCl 4 ] - solution sweeping the potential between 1.1 V and - 0.1 V vs Ag/AgClsat. The influence of the gold concentration and number of ED scans on surface morphology was investigated through Scanning Electron Microscopy (SEM), Energy dispersive X-ray (EDX), and Cyclic Voltammetry (CV). The results were discussed by considering the average AuNPs diameter determined for each modification and by comparing the features of the realized platforms to those of commercial gold screen-printed electrodes (SPEs). The best performing platform in terms of electrochemical behaviour, stability, and reproducibility was selected for the development of a label-free immunosensor. The target analyte was neutrophil-associated lipocalin (NGAL), a 25 kDa protein that serves as a biomarker for Acute Kidney Injury (AKI), one of the primary causes of in-hospital mortality globally. In contrast to creatinine, NGAL allows for the early prediction of AKI-related clinical events, facilitating timely interventions, which could significantly enhance outcomes in high-risk patients. To this aim, the electrode surface was first modified with a self-assembled monolayer (SAM) of 3-mercaptopropionic acid (MPA) and then functionalized by immobilizing the NGAL antibody via EDC/NHS coupling. The LOD (0.56 μg/mL) and the high sensitivity obtained (21.8 μA mL/μg) were compatible with the diagnostic range required for AKI. Representation of the various surface modifications of a screen-printed electrode achieved through gold electrodeposition, during the development of the NGAL sensing platform.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".