Generating complex vectorial optical fields via surface lattice resonances.

Vectorial optical fields (VOFs) with extra degrees of freedom hold promise for many photonic applications. However, current methods to generate VOFs are either bulky in size or exhibit limited functionalities. Here, we demonstrate a tunable VOF generator by exciting plasmonic surface lattice resonances (SLRs) with axial symmetry. By meticulously arranging bilayer circular arrays with opposite handedness, we achieve a high Q-factor of 103 via just a few particles despite the general belief that too small array size suppresses the SLRs. This work presents tunable complex VOFs with distinct inhomogeneous spatial polarization distributions, which may enable various applications in integrated and polarization optics.

Cellulose Nanofiber Films with Gold Nanoparticles Electrostatically Adsorbed for Facile Surface-Enhanced Raman Scattering Detection.

Cellulose nanofiber (CNF) holds great promise in applications such as surface-enhanced Raman scattering (SERS), catalysis, esthesia, and detection. This study aimed to build novel CNF-based SERS substrates through a facile synthetic method. Citrate-reduced gold nanoparticles (AuNPs) were adsorbed on the cationized CNF surface due to electrostatic interactions, and uniform AuNPs@(2,3-epoxypropyl trimethylammonium chloride)EPTMAC@CNF flexible SERS substrates were prepared by a simple vacuum-assisted filtration method. The probe molecule methylene blue was chosen to assess the performance of the CNF-based SERS substrate with a sensitivity up to 10-9 M, superior signal reproducibility (relative standard deviation (RSD) = 4.67%), and storage stability (more than 30 days). Tensile strength tests indicated that the CNF-based films had good mechanical properties. In addition, CNF-based substrates can easily capture and visually identify microplastics in water. These results demonstrate the potential application of the flexible, self-assembled AuNPs@EPTMAC@CNF flexible SERS substrate for prompt and sensitive detection of trace substances.

Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network.

With the increasing interest in microplastics (MPs) pollutants, quantitative analysis of MPs in water environment is an important issue. Vibrational spectroscopy, represented by Raman spectroscopy, is widely used in MP detection because they can provide unique fingerprint characteristics of chemical components of MPs, but it is difficult to provide quantitative information. In this paper, an ingenious method for quantitative analysis of MPs in water environment by combining Raman spectroscopy and convolutional neural network (CNN) is proposed. It is innovatively proposed to collect the average mapping spectra (AMS) of the samples to improve the uniformity of Raman spectroscopy detection, and to increase the effective detection range of concentration by filtering different volumes of the same MP solutions. In order to verify the universality and effectiveness of the proposed method, 6 different sizes of Polyethylene (PE) MPs were used as detection objects and mixed into 5 different actual water environments. The R2 and RMSE of CNN for identifying the concentration of PE solutions could reach 0.9972 and 0.033, respectively. Meanwhile, by comparing machine learning models such as Random Forest (RF) and Support Vector Machine (SVM) were compared, and CNN combined with Raman spectroscopy has significant advantages in identifying the concentration of MPs.

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption.

Ultra-broadband and efficient terahertz (THz) absorption is of paramount importance for the development of high-performance detectors. These detectors find applications in next-generation wireless communications, military radar systems, security detection, medical imaging, and various other domains. In this study, we present an ultra-wideband THz wave metasurface absorber (UTWMA) featuring a composite surface microstructure and a multilayer absorbing material (graphene). This UTWMA demonstrates remarkable capabilities by achieving highly efficient absorption levels, reaching 96.33%, within the 0.5-10 THz frequency range. To enhance the efficiency and precision of the design process, we have incorporated artificial neural networks, which enable rapid and accurate parameter selection. Moreover, we have conducted a comprehensive analysis of the absorption mechanism exhibited by the UTWMA at different frequencies. This analysis combines insights from the electric field distribution and effective medium theory. The findings presented in this paper are expected to catalyze further research in the domain of broadband THz technology, particularly in the context of metasurfaces and related fields. Additionally, this work paves the way for the development of compact, supercontinuous THz photovoltaic or photothermal electrical devices.

Component identification for the SERS spectra of microplastics mixture with convolutional neural network.

With the increasing interest in microplastics (MPs) pollutants, relevant detection technologies are also developing. In MPs analysis, vibrational spectroscopy represented by surface-enhanced Raman spectroscopy (SERS) is widely used because they can provide unique fingerprint characteristics of chemical components. However, it is still a challenge to separate various chemical components from the SERS spectra of MPs mixture. In this study, it is innovatively proposed to combine the convolutional neural networks (CNN) model to simultaneously identify and analyze each component in the SERS spectra of six common MPs mixture. Different from the traditional method, which requires a series of spectral preprocessing such as baseline correction, smoothing and filtering, the average identification accuracy of MP components is as high as 99.54 % after the unpreprocessed spectral data is trained by CNN, which is better than other classical algorithms such as support vector machine (SVM), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), Random Forest (RF), and K Near Neighbor (KNN), with or without spectral preprocessing. The high accuracy shows that CNN can be used to quickly identify MPs mixture with unpreprocessed SERS spectra data.

A gold nanoparticle doped flexible substrate for microplastics SERS detection.

Due to overuse of plastic products, decomposed microplastics (MPs) are widely spread in aquatic ecosystems, and will cause irreparable harm to the human body through the food chain. Traditional MP detection methods require cumbersome sample pre-processing procedures and complex instruments, so there is an urgent demand to develop methods to achieve simple on-site detection. Herein, a simple, sensitive, accurate, and stable MP detection method based on surface-enhanced Raman scattering (SERS) is investigated. Considering the hydrophobic problems of MPs, gold nanoparticle (AuNP) doped filter paper as a flexible SERS substrate is applied to capture MPs in the fiber pores. Benefitting from the electromagnetic (EM) hot spots generated by AuNPs, the Raman signal of MPs can be effectively enhanced. Meanwhile, the flexible SERS substrate has good sensitivity to a minimum detectable concentration of 0.1 g L-1 for polyethylene terephthalate (PET) in water, and the maximum enhancement factor (EF) can reach 360.5. Furthermore, the practicability of the developed method has been proved by the successful detection of MPs in tap water and pond water. This research provides an easy process, high sensitivity, and good reproducibility method for MP detection.

Extraction, Antimicrobial, and Antioxidant Activities of Crude Polysaccharides from the Wood Ear Medicinal Mushroom Auricularia auricula-judae (Higher Basidiomycetes).

In this study, crude polysaccharides of culinary-medicinal mushroom Auricularia auricular-judae were extracted by hot water extraction and alcohol precipitation, and their antimicrobial and antioxidant activities were investigated. An optimum extraction condition was obtained at a ratio of liquid to solid 70 mL/g, temperature 90°C, time 4 h and extraction number 4. Accordingly, the best yield of crude polysaccharides was 6.89% with 76.12% in purity. Some bacteria and fungi were used for antimicrobial studies. It was found that crude A. auricula-judae had great antimicrobial activities against Escherichia coli and Staphylococcus aureus, but no activities on the others. The inhibitory diameters of antimicrobial zones for the two were 5.55 ± 0.182 and 9.84 ± 0.076 mm, respectively. Moreover, crude A. auricula-judae had significant antioxidant activities in scavenging free radicals, reducing power assays, and Fe2+ chelating ability assay. Results revealed that crude A. auricula-judae has a great potential as antimicrobial and antioxidant, and it can be a supplementary food for human health.

Fourier algorithm method for reconstruction of large-aperture digital holograms based on phase compensation.

To simplify the reconstruction calculation of a large-aperture digital hologram we propose a novel Fourier-transformation reconstruction algorithm. When the reconstructed wave is the same as or similar to the reference wave, the higher-order phase term of reconstruction can be compensated for. For example, the variation between the higher-order phase term and the aperture angle with a different field of view in in-line phase-shifting digital holography is analyzed.