Monolayer transition metal dichalcogenides (TMDs) show promising potential for next-generation optoelectronics due to excellent light capturing and photodetection capabilities. Photodetectors, as important components of sensing, imaging and communication systems, are able to perceive and convert optical signals to electrical signals. Herein, the large-area and high-quality lateral monolayer MoS2/WS2 heterojunctions were synthesized via the one-step liquid-phase chemical vapor deposition approach. Systematic characterization measurements have verified good uniformity and sharp interfaces of the channel materials. As a result, the photodetectors enhanced by the photogating effect can deliver competitive performance, including responsivity of ~ 567.6 A/W and detectivity of ~ 7.17 × 1011 Jones. In addition, the 1/f noise obtained from the current power spectrum is not conductive to the development of photodetectors, which is considered as originating from charge carrier trapping/detrapping. Therefore, this work may contribute to efficient optoelectronic devices based on lateral monolayer TMD heterostructures.
This study presents a rapid and low-cost method to detect thyroid dysfunction using serum Raman spectroscopy combined with support vector machine (SVM). The serum samples taken from 34 thyroid dysfunction patients and 40 healthy volunteers were measured in this study. Tentative assignments of the Raman bands in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was used for feature extraction and reduced the dimension of high-dimension spectral data; then, SVM was employed to establish an effective discriminant model. To improve the efficiency and accuracy of the SVM discriminant model, we proposed artificial fish coupled with uniform design (AFUD) algorithm to optimize the SVM parameters. The average accuracy of 30 discriminant results reached 82.74%, and the average optimization time was 0.45 s. The results demonstrate that the serum Raman spectroscopy technique combined with the AFUD-SVM discriminant model has great potential for the detection of thyroid dysfunction. This technique could be used to develop a portable, rapid, and low-cost device for detecting thyroid function to meet the needs of individuals and communities.
In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.
The precise coagulation add-in in the wastewater process treatment is key for efficient contamination removal. However, the complexity of the coagulant chemical theory and affected by many factors (turbidity, pH, conductivity, flow rate, etc.) that it is difficult to determine the optimal dosage. The traditional method in the production process, such as PID controller had a bad adaptability on the complex systems and high performance required systems due to its inefficient parameter coordination, and it has a large time delay, difficult to achieve precise control. Excessive dosage will lead to waste and cost-waste, insufficient dosage could not guarantee the quality of effluent water. In this research study, we proposed an intelligent precisely dosing prediction algorithm based on LightGBM, using the characteristics of the influent water quality parameters PH, turbidity, electrical conductivity and flow rate to predict the dosage of coagulant. Perform experiments based on the actual data collected from the sewage treatment plant. Compared to experimental results with the optimal dosage solution, it demonstrated that the proposed approach could predict the dosage more accurate, resulting in intelligent and precise dosing add-in in water treatment process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.