Researchers from Brazil's Federal University of Pelotas and Federal University of Rio Grande do Sul have developed a laser-induced graphene sensor that can detect dopamine with high sensitivity in tear fluid, pointing to a possible noninvasive route for monitoring neurological disorders.
Dopamine is central to movement, cognition, and emotional regulation, and abnormal levels are linked to disorders including Parkinson’s disease and schizophrenia. Because current monitoring methods often rely on blood, urine, or implanted devices, the researchers focused on tears as a faster and less invasive alternative. The team built the sensor using laser-induced graphene and then functionalized it with nickel nitrate and urea. This combination increased the number of active sites, improved electron transfer, and boosted the oxidation signal from dopamine, which is the key electrochemical event the sensor measures.
That design translated into strong analytical performance. In phosphate-buffered saline, the sensor delivered a linear detection range of 0.25–16.44 μmol·L–1, a limit of detection of 17.86 nmol·L–1, and a limit of quantification of 54.14 nmol·L–1, with 𝑅2=0.98. In synthetic tear fluid, it maintained a reliable response across 3.23–9.32 μmol·L–1, and recovery in real-sample analysis was close to 100%.
The concentration range is particularly relevant because previously reported dopamine levels in tears are around 3.38 μmol·L–1, which falls within the sensor’s working window. The device also maintained high selectivity in complex tear-like media, reliably detecting dopamine even in the presence of other typical tear components.
The overall result is a compact, low-cost, nonenzymatic sensing platform that combines scalable laser fabrication with strong electrochemical performance. Rather than relying on enzymes, the sensor uses the electroactive graphene structure and nickel-based surface chemistry to generate a measurable current response, which could make it attractive for future point-of-care tear-based diagnostics.
