Machine learning assists the sensor array constructed by the tri-emission carbon dots to detect multiple metal ions

Metal ions are related to the health of daily life, which urges to be detected and identified by efficient sensors. We first synthesize tri-emission carbon dots for the functional determination of six metal ions and obtain the multi-dimension data to construct the fluorescent sensor array in the work. Inspired by the data-driven method, machine learning is employed in a sensor array to detect and analyze the six metal ions. The ion classification and concentration models are constructed to identify metal ion types and corresponding concentrations. The classification model could correctly discriminate six metal ions, reaching 99.5 % accuracy and the concentration model exhibits an excellent performance range from 30 to 330 μM, with a mean concentration difference value of 0.12 μM and R2 value of 0.9845 for the unknown sample. Furthermore, six metal ions in tap water also can be distinguished with 96 % accuracy and the recovery rate is between 101.89 % and 106.37 %. This work shows that the machine learning combined sensor array has significant advantages in detecting multiple metal ions and provides a novel thought for analyzing sensing data, accelerating the development of data-driven strategies in the sensing field. © 2001 Elsevier Science. All rights reserved.