An intelligent sensing platform for detecting and identifying biochemical substances based on terahertz spectra.

This paper presents the development of an intelligent sensing platform dedicated to accurately identifying terahertz (THz) spectra obtained from various biochemical substances. The platform currently has two distinct identification modes, which focus on identifying five amino acids, namely phenylalanine, methionine, lysine, leucine, and threonine, and five carbohydrates, namely aspartame, fructose, glucose, lactose monohydrate, and sucrose based on their THz spectra. The first mode, called One-dimensional THz Spectrum Identification (OTSI), combines THz time-domain spectroscopy (THz-TDS) with the proposed mini convolutional neural network (MCNN) model. THz-TDS detects biochemical substances, while the MCNN model identifies the THz spectra. The MCNN model has a simple structure and only needs to deal with the THz absorption coefficients of biochemical substances, which are less computationally intensive and easily converged. The model can achieve 99.07 % accuracy in identifying one-dimensional THz spectra of the ten biochemical substances. The second mode, THz Spectrum Image-based Identification (TSII), applies the YOLO-v5 target detection model to THz spectral image recognition. The YOLO-v5 model uses THz absorption peaks as identification features and can identify biochemical substances based on only one or several THz absorption peaks. The overall identifying accuracy of the YOLO-v5 model for ten biochemical substances is 96.20 %. We also compared the MCNN and YOLO-v5 models with other deep learning and machine learning models, which demonstrate that they have better performance. This feature broadens the platform's utility in biomolecular analysis and paves the way for further research and development in detecting and analyzing diverse biological compounds.

Fabrication of polydimethylsiloxane (PDMS)-Based Flexible Surface-Enhanced Raman Scattering (SERS) Substrate for Ultrasensitive Detection.

This article presents a fabrication method for a flexible substrate designed for Surface-Enhanced Raman Scattering (SERS). Silver nanoparticles (AgNPs) were synthesized through a complexation reaction involving silver nitrate (AgNO3) and ammonia, followed by reduction using glucose. The resulting AgNPs exhibited a uniform size distribution ranging from 20 nm to 50 nm. Subsequently, 3-aminopropyl triethoxysilane (APTES) was employed to modify a PDMS substrate that had been surface-treated with oxygen plasma. This process facilitated the self-assembly of AgNPs onto the substrate. A systematic evaluation of the impact of various experimental conditions on substrate performance led to the development of a SERS substrate with excellent performance and an Enhanced Factor (EF). Utilizing this substrate, impressive detection limits of 10-10 M for R6G (Rhodamine 6G) and 10-8 M for Thiram were achieved. The substrate was successfully employed for detecting pesticide residues on apples, yielding highly satisfactory results. The flexible SERS substrate demonstrates great potential for real-world applications, including detection in complex scenarios.

Optical metasurfaces for multiplex high-performance grating-type structural colors.

Optical metasurfaces provide a significant approach for the production of structural colors due to their excellent optical control abilities. Herein, we propose trapezoidal structural metasurfaces for achieving multiplex grating-type structural colors with high comprehensive performance originating from the anomalous reflection dispersion in the visible band. Single trapezoidal metasurfaces with different x-direction periods can tune the angular dispersion regularly from 0.036 rad/nm to 0.224 rad/nm to generate various structural colors, and composite trapezoidal metasurfaces with three kinds of combinations can achieve multiplex sets of structural colors. The brightness can be controlled by adjusting the distance between the trapezoids in a pair accurately. The designed structural colors have higher saturation than traditional pigmentary colors, whose excitation purity can reach 1.00. The gamut is about 158.1% of the Adobe RGB standard. This research has application potential in ultrafine displays, information encryption, optical storage, and anti-counterfeit tagging.