Fully Inkjet Printing Preparation of a Carbon Dots Multichannel Microfluidic Paper-Based Sensor and Its Application in Food Additive Detection

Microfluidic paper-based sensors as a new type of microsample detection technology are widely used in medical diagnosis, environmental monitoring, and food safety testing. Inkjet printing has the advantages of simplicity, speed, flexibility, high resolution, low cost, and efficient mass production and has become one of the most cutting-edge technologies in the manufacture of paper-based sensors. In this work, a fully inkjet printing preparation method was proposed for paper-based sensors, which can achieve high-precision, multichannel, and visual fluorescence detection. Three kinds of fluorescent carbon dots (CDs; r-CDs, b-CDs, and y-CDs) were fabricated into inkjet ink by adding a suitable ratio of solvent, PEG, and surfactant FS3100 to control its viscosity, surface tension, and other influencing factors, obtaining the best-visualized fluorescence response on paper. To optimize the full inkjet printing process of the paper-based sensor, we studied the influence of factors such as the hydrophobic material AKD formula, postprocessing conditions, and the structure of the hydrophilic and hydrophobic channels on the paper-based detection accuracy, and it was found that proper AKD concentration, curing time, and temperature can make AKD fully react with paper-based surface groups and produce more hydrophobic groups on the surface and inside of the filter paper, which can form paper-based microfluidic sensors with clear boundaries and fast transmission speed at low cost and high efficiency. The fabricated sensor is used for the fluorometric determination of vitamin C (AA), NO2-, and sunset yellow (SY) at the same time, and the limits of visual detection by eyes are 6 mmol/L (NO2-), 60 μmol/L (SY), and 40 mmol/L (AA). The mechanism of inkjet printing is investigated in detail, which is simple, reliable, and easy to realize mass production and can realize highly sensitive, on-site, and visual detection for food additives.