New chip speeds up and optimizes clinical exams on a large scale; understand
A survey published in the scientific journal ACS Sensors presented a new chip developed by Brazilian scientists from the National Center for Research in Energy and Materials (CNPEM) and the São Carlos Institute of Physics at USP (IFSC-USP), in partnership with the University of Colorado (USA).
According to researcher Renato Lima, the main advance lies in the ability to integrate many sensors into a single system without increasing operational complexity.
"The technology solves an old problem in the area of electrochemical sensors: the difficulty of concentrating many sensors on a single chip without increasing the complexity of the system's operation", he explained.
The device measures 75×35 millimeters and brings together more than 100 microscopic sensors that work in an integrated manner.
For USP researcher and professor Osvaldo Novais de Oliveira Junior, the difference lies in the mode of operation: "The device brings together on a chip more than 100 microscopic sensors that work in an integrated manner. The innovation lies in the way these sensors operate: they alternate their functions during tests", he stated.
This alternation reduces the need for electrical connections, which simplifies the design and reduces production costs. "This simplification makes the chip more compact and easier to produce, in addition to reducing the cost of each sensor", explained Osvaldo.
In laboratory tests, the chip was able to monitor the proliferation of cancer cells, identify a biomarker for the Mpox virus and analyze samples that simulated human urine. For scientists, this demonstrates versatility in different biomedical applications.
"In other words, the chip can have multiple functionalities, as long as the active sensing layer is adapted to the substances to be detected or monitored", highlighted the professor.
The team also demonstrated that the system can be coupled to portable equipment for rapid analysis of electrochemical properties. In the future, researchers are evaluating the use of complementary techniques, such as image analysis supported by machine learning for more advanced diagnoses.
Despite the results, the authors emphasize that the progress is still in the laboratory phase. "For them to be produced on a large scale, a large investment in device engineering is necessary to develop procedures that allow the manufacturing of hundreds or thousands of chips, with reproducible results", explained the professor.
Meta begins testing first internal AI training chip
Source: CNN