Boosting Electrochemiluminescence Immunoassay Sensitivity via Co–Pt Nanoparticles within a Ti<sub>3</sub>C<sub>2</sub> MXene-Modified Single Electrode Electrochemical System on Raspberry Pi
Bibliographic record
Abstract
Point-of-care testing plays a crucial role in diagnostics within resource-poor areas, necessitating the utilization of portable and user-friendly devices. The adaptation of biosensors for point-of-care applications requires careful considerations, such as miniaturization, cost-effectiveness, and streamlined sample processing. In recent years, the electrochemiluminescence (ECL) immunoassay has gained significant attention due to its visual detection capabilities and ability to facilitate high-throughput analysis. However, the development of a practical and cost-effective ECL device remains a challenging task. This study presents the development of an integrated MXene-modified single-electrode electrochemical system (SEES) for visual and high-throughput ECL immunoassays incorporating a Raspberry Pi system. The SEES was designed by affixing a plastic sticker with multiple perforations onto a single carbon ink screen-printed electrode, which operates based on a resistance-induced potential difference. Leveraging the excellent adsorption and bioaffinity properties of the carbon ink screen-printed electrode, effective immobilization of antibodies was achieved. Furthermore, the incorporation of Co–Pt nanoparticles enhanced the ECL intensity and electron transfer kinetics, enabling the sensitive detection of SARS-CoV-2. The developed system comprised 18 individual reaction cells, allowing for simultaneous analysis while maintaining sample isolation. Impressively, the system achieved a remarkable minimum virus detection limit of 10 –14 g mL –1, accompanied by a high R 2 value of 0.9798. These findings highlight the promising potential of our developed system for efficient point-of-care testing in resource-limited settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".