Revealing the Hidden Spin-Polarized Bands in a Superconducting Tl Bilayer Crystal.

The interplay of spin-orbit coupling and crystal symmetry can generate spin-polarized bands in materials only a few atomic layers thick, potentially leading to unprecedented physical properties. In the case of bilayer materials with global inversion symmetry, locally broken inversion symmetry can generate degenerate spin-polarized bands, in which the spins in each layer are oppositely polarized. Here, we demonstrate that the hidden spins in a Tl bilayer crystal are revealed by growing it on Ag(111) of sizable lattice mismatch, together with the appearance of a remarkable phenomenon unique to centrosymmetric hidden-spin bilayer crystals: a novel band splitting in both spin and space. The key to success in observing this novel splitting is that the interaction at the interface has just the right strength: it does not destroy the original wave functions of the Tl bilayer but is strong enough to induce an energy separation.

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas.

The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R2 of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model.

Microextraction based on microplastic followed by SERS for on-site detection of hydrophobic organic contaminants, an indicator of seawater pollution.

Microplastics (MPs) have been proven to concentrate hydrophobic organic contaminants (HOCs) from seawater as the sorbent phase, and the concentration of HOCs in aqueous solutions could be estimated from MPs preloaded with HOCs by equilibrium partition coefficient. This study firstly proposed to in situ quantify fluoranthene (a representative HOCs) pre-concentrated on MPs using surface enhance raman scattering (SERS) in combination with mathematical models, as an efficient monitoring tool for fluoranthene pollution in the aquatic environment. AgNPs-coated quartz (AgNPs@SiO2) substrate was fabricated. The SERS substrate was tested using fluoranthene standard solution with the minimal detectable concentration of 1 ng/mL achieved. Applying SERS for the detection of fluoranthene sorbed on MPs, the detection limit of fluoranthene on MPs was 3.3 ng/g, where the concentration in the corresponding equilibrium seawater was 0.97 ng/mL. Since more than one fluoranthene peak was observed, the quantitative detection was investigated by interval partial least square model. Eight characteristic peak ranges were selected to develop the model for predicting fluoranthene concentration, with R2c and R2v of 0.90 and 0.82, respectively. The study provides a promising solution to monitor trace level of contaminations in aquatic environment, using MPs as the passive sampler.

Hyperspectral Imaging Based Method for Rapid Detection of Microplastics in the Intestinal Tracts of Fish.

Microplastics (MPs) in aquatic organisms are raising increasing concerns regarding their potential damage to ecosystems. To date, Raman and Fourier transform infrared spectroscopy techniques have been widely used for detection of MPs in aquatic organisms, which requires complex protocols of tissue digestion and MP separation and are time- and reagent-consuming. This novel approach directly separates, identifies, and characterizes MPs from the hyperspectral image (HSI) of the intestinal tract content in combination with a support vector machine classification model, instead of using the real digestion/separation protocols. The procedures of HSI acquisition (1 min) and data analysis (5 min) can be completed within 6 min plus the sample preparation and drying time (30 min) where necessary. This method achieved a promising efficiency (recall >98.80%, precision >96.22%) for identifying five types of MPs (particles >0.2 mm). Moreover, the method was also demonstrated to be effective on field fish from three marine fish species, revealing satisfying detection accuracy (particles >0.2 mm) comparable to Raman analysis. The present technique omits the digestion protocol (reagent free), thereby significantly reducing reagent consumption, saving time, and providing a rapid and efficient method for MP analysis.

Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology.

Microplastics (MPs) pollution in marine systems is attracting worldwide attentions, which highlights a pressing need for efficient detection methods. Traditional protocols generally identify the suspected particles individually, which are time-consuming. Hyperspectral imaging technique has emerged as a simple and rapid method to characterize MPs in seawater. However, hyperspectral image consists of amount of redundant and high correlated spectral information, resulting in the Hughes phenomenon for classification. This work aimed to identify MPs from the hyperspectral image using support vector machine (SVM) algorithm, which presents a good performance for analyzing nonlinear and high-dimensional data and is insensitive to the Hughes effect. In this work, SVM was performed to quantify and identify MPs in both of seawater and seawater filtrates. The factors which may affect the accuracy of SVM model were investigated, including organic particles, polymer types and particle sizes. SVM model yielded a satisfactory accuracy for all the tested pure polymers and it presented a highly robust for detecting MPs in a wide range of types and particle sizes. Finally, common household polymers were chosen to validate the developed model. The results illustrate that hyperspectral imaging technology combination with SVM method exhibits a high robustness and recovery rate for MPs detection.

A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics.

Hyperspectral imaging technology has been investigated as a possible way to detect microplastics contamination in soil directly and efficiently in this study. Hyperspectral images with wavelength range between 400 and 1000 nm were obtained from soil samples containing different materials including microplastics, fresh leaves, wilted leaves, rocks and dry branches. Supervised classification algorithms such as support vector machine (SVM), mahalanobis distance (MD) and maximum likelihood (ML) algorithms were used to identify microplastics from the other materials in hyperspectral images. To investigate the effect of particle size and color, white polyethylene (PE) and black PE particles extracted from soil with two different particle size ranges (1-5 mm and 0.5-1 mm) were studied in this work. The results showed that SVM was the most applicable method for detecting white PE in soil, with the precision of 84% and 77% for PE particles in size ranges of 1-5 mm and 0.5-1 mm respectively. The precision of black PE detection achieved by SVM were 58% and 76% for particles of 1-5 mm and 0.5-1 mm respectively. Six kinds of household polymers including drink bottle, bottle cap, rubber, packing bag, clothes hanger and plastic clip were used to validate the developed method, and the classification precision of polymers were obtained from 79% to 100% and 86%-99% for microplastics particle 1-5 mm and 0.5-1 mm respectively. The results indicate that hyperspectral imaging technology is a potential technique to determine and visualize the microplastics with particle size from 0.5 to 5 mm on soil surface directly.