The rapid emergence of deep learning, e.g., deep convolutional
neural networks (DCNNs) as one-click image analysis with super-resolution,
has already revolutionized colorimetric determination. But it is severely
limited by its data-hungry nature, which is overcome by combining
the generative adversarial network (GAN), i.e., few-shot learning
(FSL). Using the same amount of real sample data, i.e., 414 and 447
samples as training and test sets, respectively, the accuracy could
be increased from 51.26 to 85.00% because 13,500 antagonistic samples
are created and used by GAN as the training set. Meanwhile, the generated
image quality with GAN is better than that with the commonly used
convolution self-encoder method. The simple and rapid on-site determination
of Cr(VI) with 1,5-diphenylcarbazide (DPC)-based test paper is a favorite
for environment monitoring but is limited by unstable DPC, poor sensitivity,
and narrow linear range. The chromogenic agent of DPC is protected
by the blending of polyacrylonitrile (PAN) and then loaded onto thin
chromatographic silica gel (SG) as a Cr(VI) colorimetric sensor (DPC/PAN/SG);
its stability could be prolonged from 18 h to more than 30 days, and
its repeatable reproducibility is realized via facile electrospinning.
By replacing the traditional Ed method with DCNN, the detection limit
is greatly improved from 1.571 mg/L to 50.00 μg/L, and the detection
range is prolonged from 1.571–8.000 to 0.0500–20.00
mg/L. The complete test time is shortened to 3 min. Even without time-consuming
and easily stained enrichment processing, its detection limit of Cr(VI)
in the drinking water can meet on-site detection requirements by USEPA,
WHO, and China.