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.

Somatisation Disorder Detection via Speech: Introducing a Self-Supervised Learning Model.

With the depressive psychiatric disorders becoming more common, people are gradually starting to take it seriously. Somatisation disorders, as a general mental disorder, are rarely accurately identified in clinical diagnosis for its specific nature. In the previous work, speech recognition technology has been successfully applied to the task of identifying somatisation disorders on the Shenzhen Somatisation Speech Corpus. Nevertheless, there is still a scarcity of labels for somatisation disorder speech database. The current mainstream approaches in the speech recognition heavily rely on the well labelled data. Compared to supervised learning, self-supervised learning is able to achieve the same or even better recognition results while reducing the reliance on labelled samples. Moreover, self-supervised learning can generate general representations without the need for human hand-crafted features depending on the different recognition tasks. To this end, we apply self-supervised learning pre-trained models to solve few-labelled somatisation disorder speech recognition. In this study, we compare and analyse the results of three self-supervised learning models (contrastive predictive coding, wav2vec and wav2vec 2.0). The best result of wav2vec 2.0 model achieves 77.0 % unweighted average recall and is significantly better than CPC (p < .005), performing better than the benchmark of the supervised learning model.Clinical relevance- This work proposed a self-supervised learning model to resolve the few-labelled SD speech data, which can be well used for helping psychiatrists with clinical assistant to diagnosis. With this model, psychiatrists no longer need to spend a lot of time labelling SD speech data.

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.

Confined transformation of trifunctional Co2(OH)2CO3 nanosheet assemblies into hollow porous Co@N-doped carbon spheres for efficient microwave absorption.

Carbonaceous-magnetic composites are the most appealing candidates for electromagnetic wave absorption, and creating hollow interiors and nanopores in the composites is commonly recognized as an essential strategy to reinforce their overall performances. Herein, we propose a spatial confinement strategy mediated by Co2(OH)2CO3 nanosheet assemblies for achieving highly dispersed Co nanoparticles into hollow porous N-doped carbon shells (HP-Co@NCS). Systematic multi-technique characterizations indicate that the Co2(OH)2CO3 nanosheet assemblies simultaneously play a trifunctional role during the synthesis, including Co source, template of the hollow interior cavities, and micro-/mesopore porogen. The chemical composition can be modulated by simply varying the ratio of Co2(OH)2CO3 and carbon source (dopamine). The optimized HP-Co@NCS absorber exhibits a well-defined hollow structure and unprecedented high porosity (specific surface area of 742 m2 g-1) even with a high metallic Co content of 35.8 wt%. These profitable structural characteristics can facilitate incident EM waves penetrating the absorber's interior and promoting multiple reflections and scattering. Therefore, the HP-Co@NCS absorber exhibits efficient microwave absorption ability with a minimum reflection loss of -39.0 dB at a thin thickness of 2.5 mm and an effective absorption bandwidth up to 5.5 GHz (12.5-18.0 GHz) at a thin thickness of 2.0 mm. This work provides a new methodology to design advanced carbonaceous-magnetic composite materials with hollow porous structures for microwave absorption.

[Influence of deposition time on chromatics during nitrogen-doped diamond like carbon coating on pure titanium].

OBJECTIVE: The aim of this study was to observed the influence of deposition time on chromatics during nitrogen-doped diamond like carbon coating (N-DLC) on pure titanium by multi impulse are plasma plating machine. METHODS: Applying multi impulse are plasma plating machine to produce TiN coatings on pure titanium in nitrogen atmosphere, then filming with nitrogen-doped DLC on TiN in methane (10-80 min in every 5 min). The colors of N-DLC were evaluated in the CIE1976 L*a*b* uniform color scale and Mussell notation. The surface morphology of every specimen was analyzed using scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). RESULTS: When changing the time of N-DLC coating deposition, N-DLC surface showed different color. Golden yellow was presented when deposition time was 30 min. SEM showed that crystallization was found in N-DLC coatings, the structure changed from stable to clutter by varying the deposition time. CONCLUSION: The chromatics of N-DLC coatings on pure titanium could get golden yellow when deposition time was 30 min, then the crystallized structure was stable.