A poly(arylene ethynylene)-based microfluidic fluorescence sensor array for discrimination of polycyclic aromatic hydrocarbons.
Elham GhohestaniJavad TashkhourianHoda SharifiN Maximilian BojanowskiKai SeehaferEmanuel SmarslyUwe H F BunzBahram HemmateenejadPublished in: The Analyst (2022)
Polycyclic aromatic hydrocarbons (PAHs) are persistent contaminants in the environment. Several of them have carcinogenic properties. There is considerable interest in their sensitive low-cost detection and monitoring. We present a simple paper-based microfluidic sensor for the rapid detection of PAHs. Craft punch patterning generated multiple detection zones inhabited by fluorescent poly(arylene ethynylene)s (PAEs). Changes in fluorescence image and/or intensity of the sensor array were recorded using a smartphone camera. The RGB color values of the photographed images were extracted through ImageJ software. 10 different PAHs were correctly identified using Principal Component Analysis and discrimination analysis (PCA-DA). 100% classification accuracy was achieved for model training, whereas validating the PCA-DA model by cross-validation resulted in 93% classification accuracy for 5.0 mg L -1 analyte.
Keyphrases
- polycyclic aromatic hydrocarbons
- deep learning
- label free
- low cost
- high throughput
- convolutional neural network
- machine learning
- single molecule
- single cell
- loop mediated isothermal amplification
- circulating tumor cells
- high resolution
- real time pcr
- energy transfer
- optical coherence tomography
- quantum dots
- living cells
- high intensity
- drinking water
- heavy metals
- high speed
- data analysis