An artificial intelligence handheld sensor for direct reading of nickel ion and ethylenediaminetetraacetic acid in food samples using ratiometric fluorescence cellulose paper microfluidic chip.

Abstract

User-friendly in-field sensing protocol is crucial for the effective tracing of intended analytes under less-developed countries or resources-limited environments. Nevertheless, existing sensing strategies require professional technicians and expensive laboratory-based instrumentations, which are not capable for point-of-care on-site analyses. To address this issue, artificial intelligence handheld sensor has been designed for direct reading of Ni²⁺ and EDTA in food samples. The sensing platform incorporates smartphone with machine learning-driven application, 3D-printed handheld device, and cellulose paper microfluidic chip stained with ratiometric red-green-emission carbon dots (CDs). Intriguingly, Ni²⁺ introduction makes green fluorescent (FL) of CDs glow but red FL fade because of the coordination of Ni²⁺ with CDs verified by density functional theory (DFT), concurrently manifesting continuous FL colour transition from red to green. Subsequent addition of EDTA renders FL of CDs-Ni²⁺ recover owing to the capture of Ni²⁺ from CDs by EDTA based on strong chelation effect of EDTA on Ni²⁺ confirmed via DFT, accompanying with a noticeable colour returning from green to red. Inspired by above FL phenomena, CDs-based cellulose paper microfluidic chips are first fabricated to facilitate point-of-care testing of Ni²⁺ and EDTA. Designed fully-automatic handheld sensor is utilized to directly output Ni²⁺ and EDTA concentration in water, milk, spinach, bread, and shampoo based on wide linear ranges of 0-48 μM and 0-96 μM, and low limits of detection of 0.274 μM and 0.624 μM, respectively. The proposed protocol allows for speedy straightforward on-site determination of target analytes, which will trigger the development of automated and intelligent sensors in near future.