TiO₂ films exhibiting structural colors were successfully prepared using one-step electrochemical oxidation. Results of theoretical analyses and digital simulations revealed that the structural color of a TiO₂ thin film could be regulated by adjusting oxidation voltage and oxidation time with different oxidation voltages leading to changes in structural color annulus number. At a low oxidation voltage, each thin film exhibited a single structural color, while thin films with different structural colors were obtained by varying the oxidation time. By contrast, at a higher oxidation voltage, each film exhibited iridescent and circular structural color patterns associated with symmetrical decreases in surface oxidation current density along radial lines emanating from the film center to its outer edges. TiO₂ films exhibiting iridescent structural colorations have broad application prospects in industrial fields related to photocatalysis and photovoltaic cells.

Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to the query image. In this way, a shoeprint can not only be used as an exhibit for bringing criminal charges but also to provide a clue to a case. The goal of this work is to present an overview of the existing works conducted in shoeprint image retrieval. We detail the different phases of the shoeprint retrieval task and present a summary of the state-of-the-art methods. We analyzed the difficulties and problems in this field and discussed future work directions. This review may help neophytes become involved in research easily and quickly.

Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 10⁷ and the limit of detection (LOD) was as low as 1 × 10⁻¹⁰ M. Then, SERS technology combined with support vector machine (SVM), decision tree (DT), Naive Bayes (NB) and other machine learning algorithms were used to classify and identify the three TCM decoctions, and the classification accuracy rate was as high as 97.78 %. In summary, it is expected that the proposed method combining SERS and machine learning method will have a high development in the practical application of multi-component analytes in TCM.

An effective SERS-based detection method assisted by machine learning algorithm has been developed to intelligently identify the pharmacodynamic substances in traditional Chinese medicine (TCM) which aided in the quality control and identification of TCM. In this work, the ultrasensitive grating-like core-shell Ag@SiO ₂ @Cu@moth wing (Ag@SiO ₂ @Cu@MW) SERS platform was explored which can precisely control the intergap distances by adjusting the thickness of the SiO ₂ layer. This SERS platform demonstrated reproducibility and reliability, with a low relative standard deviation (RSD) of 7.38% and an enhancement factor (EF) of 2.49 × 10 ⁷ . The optimized Ag ₃₀ @SiO ₂ (4)@Cu ₂₀ @MW substrate were then applied in the accurately analyzing pharmacodynamic substances. Specifically, the label-free SERS analysis showed the distinct spectral features for Jujuboside A, Saikosaponin A and Timosaponin A-III. Machine learning algorithms, such as principal component analysis (PCA), decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN) were employed and further in differentiating with the three pharmacodynamic substances Raman spectrum groups. These results indicate that SERS technology in combination with machine learning algorithms can not only achieve rapid and accurate detection of different types of pharmacodynamic substances, but also promote the modernization and international application of TCM.

Because of the advantages of rapid sampling and real-time detection, the surface-enhanced Raman scattering (SERS) technique provides greater potential for ultrasensitive detection of active components in traditional Chinese medicine. In this paper, an ultrasensitive SERS detection system was successfully designed using controllable magnetron sputtering technology for the rapid and quantitative detection of artemisinin, a traditional Chinese medicine. Using nickel foam (NF) with a three-dimensional porous mesh structure as a template, Ag nanoparticle layers were sputtered on the NF surface by the magnetron sputtering technique to prepare Ag-NF-20 (the magnetron sputtering time was 20 min) SERS substrates with high SERS performance. According to the SERS signal response of the R6G molecule on the Ag-NF-20 substrate, this efficient SERS platform showed excellent Raman signals and enhanced performance and time-stability. The results suggested that owing to the abundant electromagnetic enhancement “hot spots” distributed on Ag-NF-20 nanostructures, an experimental enhancement factor value of 6.57 × 10⁵ was obtained. The time-stability for 30 days in an atmospheric environment was also determined, which revealed that a minimum decrease of 2.6% in Raman signal intensity was recorded, indicating the excellent time-stability of the Ag-NF-20 substrate. Furthermore, when applying this in the detection of artemisinin, the limit of detection value for artemisinin was 1 × 10⁻⁴ ng/ml. Therefore, the Ag-NF-20 SERS substrate has great potential for trace detection of other active ingredients in traditional Chinese medicine and is expected to expand the detection of pharmacodynamic substances in clinical drugs.

Accurate and sensitive detection of bilirubin, cholic acid, and Alanine aminotransferase (ALT) is essential for the early detection of hepatobiliary diseases in healthcare and the medical field. Integrating surface-enhanced Raman scattering (SERS) with deep learning algorithm has shown great application prospect in the early identification of markers of hepatobiliary diseases. Here, an regular Ag decorated Cu-Ag nanoisland-liked SERS biosensor (Ag@5/Ag-Cu@20/PSS) was constructed by using pattered sappire substrate as a template. An additional 5 mins of Ag nanofilm significantly improved the SERS signal intensity and homogeneity, and the sensitivity was enhanced by two orders of magnitude up to 10^-10 M for 4-aminothiophenol (4-ATP). Importantly, deep learning algorithms included temporal convolutional networks (TCN), convolutional neural networks (CNN), recurrent neural network (RNN) and Transformer were used to deeply mine SERS spectral data of bilirubin, cholic acid and ALT, extract effective features, and establish an accurate classification and prediction model. Results indicating that the developed SERS biosensor combined with Transformer model demonstrated excellent accuracy (ACC) of 0.9816±0.0090 in qualitatively and quantitatively analyzing of bilirubin, cholic acid, and ALT. The successful application of the SERS biosensor will help improve the accuracy of early identification of bilirubin, cholic acid, and ALT disease markers and promote the early intervention and treatment of hepatobiliary diseases.