Porous carbon film/WO_3-x nanosheets based SERS substrate combined with deep learning technique for molecule detection

The Surface-enhanced Raman scattering (SERS) is an attractive optical detecting method with high sensitivity and detectivity, however challenges on large-area signal uniformity and complex spectra analysis methods always retards its wide application. Herein, a highly sensitive and uniform SERS detection strategy supported by porous carbon film/WO_3-x nanosheets (PorC/WO_3-x) based noble-metal-free SERS substrate and deep learning algorithm are reported. Experimentally, the PorC/WO_3-x substrate was prepared by high-temperature annealing the PorC/WO₃ films under the argon atmosphere. The defect density of the WO₃ was controlled by tuning the reducing reaction time during the annealing process. The SERS performance was evaluated by using R6G as the Raman reporter, it showed that the SERS intensity obtained on the substrate with the optimal annealing time of 3 h was about 8 times as high as that obtained on the PorC/WO₃ substrate without annealing treatment. And detection limit of 10^-7 M and Raman enhancement factor of 10⁶ could be achieved. Moreover, the above optimal SERS substrate was utilized to detect flavonoids of quercetin, 3-hydroxyflavone and flavone, and a deep learning algorithms was incorporated to identify the quercetin. It revealed that quercetin can be accurately detected within the above flavonoids, and lowest detectable concentration of 10^-5 M can be achieved.