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 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 | 0.001 | 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".