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Published on: March 13, 2026
Automated carousel-based electrochemical sensing toward microbiological and oncological settings
Lucas F de Lima1, Lucas Costa Faustino2, Nathália V B Mestanza3
1Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, São Paulo, SP, 05508-000, Brazil; Laboratório de Sensores Químicos Portáteis, Departamento de Química Analítica, Instituto de Química, Universidade Estadual de Campinas - UNICAMP, Campinas, SP, 13083-861, Brazil.
We developed a portable, low-cost automated electrochemical system (CELS) using 3D printing for accessible bioanalysis. CELS enables sensitive detection of bacteria and monitoring of tumor cell death in 3D models.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Electrochemistry
Background:
- Automated electrochemical sensing accelerates bioanalysis but often requires expensive infrastructure.
- Existing platforms are inaccessible for resource-limited labs and lack safety for pathogenic samples.
- Need for portable, low-cost systems for microbiological and oncological applications.
Purpose of the Study:
- Develop a portable, low-cost, semi-autonomous electrochemical system.
- Integrate 3D printing, automation, and wireless sensing.
- Enable safer and reproducible bioanalysis in controlled environments.
Main Methods:
- Developed the Carousel ElectroLab System (CELS) with 3D-printed electrodes and Arduino control.
- Utilized blue-laser treatment to enhance electrode performance (RSD <5%).
- Applied CELS for selective detection of Pseudomonas aeruginosa and monitoring doxorubicin-induced cytotoxicity in MCF-7 tumoroids.
Main Results:
- Achieved selective detection of Pseudomonas aeruginosa with a limit of detection of 0.89 CFU mL⁻¹.
- Monitored doxorubicin-induced cytotoxicity in 3D tumor models via LDH activity.
- Demonstrated improved electrochemical performance and reproducibility using laser-treated electrodes.
Conclusions:
- CELS offers a scalable, open-source alternative to conventional automated systems.
- Enables safer, reproducible analysis of pathogens and 3D tumor models.
- Provides a foundation for future integration with robotic fluidics and AI.
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