A Study on the Field Emission Characteristics of High-Quality Wrinkled Multilayer Graphene Cathodes.

Field emission (FE) necessitates cathode materials with low work function and high thermal and electrical conductivity and stability. To meet these requirements, we developed FE cathodes based on high-quality wrinkled multilayer graphene (MLG) prepared using the bubble-assisted chemical vapor deposition (B-CVD) method and investigated their emission characteristics. The result showed that MLG cathodes prepared using the spin-coating method exhibited a high field emission current density (~7.9 mA/cm2), indicating the excellent intrinsic emission performance of the MLG. However, the weak adhesion between the MLG and the substrate led to the poor stability of the cathode. Screen printing was employed to prepare the cathode to improve stability, and the influence of a silver buffer layer was explored on the cathode's performance. The results demonstrated that these cathodes exhibited better emission stability, and the silver buffer layer further enhanced the comprehensive field emission performance. The optimized cathode possesses low turn-on field strength (~1.5 V/µm), low threshold field strength (~2.65 V/µm), high current density (~10.5 mA/cm2), and good emission uniformity. Moreover, the cathode also exhibits excellent emission stability, with a current fluctuation of only 6.28% during a 4-h test at 1530 V.

Evaluating the Field Emission Properties of N-Type Black Silicon Cold Cathodes Based on a Three-Dimensional Model.

Black silicon (BS), a nanostructured silicon surface containing highly roughened surface morphology, has recently emerged as a promising candidate for field emission (FE) cathodes in novel electron sources due to its huge number of sharp tips with ease of large-scale fabrication and controllable geometrical shapes. However, evaluating the FE performance of BS-based nanostructures with high accuracy is still a challenge due to the increasing complexity in the surface morphology. Here, we demonstrate a 3D modeling methodology to fully characterize highly disordered BS-based field emitters randomly distributed on a roughened nonflat surface. We fabricated BS cathode samples with different morphological features to demonstrate the validity of this method. We utilize parametrized scanning electron microscopy images that provide high-precision morphology details, successfully describing the electric field distribution in field emitters and linking the theoretical analysis with the measured FE property of the complex nanostructures with high precision. The 3D model developed here reveals a relationship between the field emission performance and the density of the cones, successfully reproducing the classical relationship between current density J and electric field E (J-E curve). The proposed modeling approach is expected to offer a powerful tool to accurately describe the field emission properties of large-scale, disordered nano cold cathodes, thus serving as a guide for the design and application of BS as a field electron emission material.

A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis.

PURPOSE: To develop an assistant tool based on machine learning for early frailty screening in patients receiving maintenance hemodialysis. METHODS: This is a single-center retrospective study. 141 participants' basic information, scale results and laboratory findings were collected and the FRAIL scale was used to assess frailty. Then participants were divided into the frailty group (n = 84) and control group (n = 57). After feature selection, data split and oversampling, ten commonly used binary machine learning methods were performed and a voting classifier was developed. RESULTS: The grade results of Clinical Frailty Scale, age, serum magnesium, lactate dehydrogenase, comorbidity and fast blood glucose were considered to be the best feature set for early frailty screening. After abandoning models with overfitting or poor performance, the voting classifier based on Support Vector Machine, Adaptive Boosting and Naive Bayes achieved a good screening performance (sensitivity: 68.24% ± 8.40%, specificity:72.50% ± 11.81%, F1 score: 72.55% ± 4.65%, AUC:78.38% ± 6.94%). CONCLUSION: A simple and efficient early frailty screening assistant tool for patients receiving maintenance hemodialysis based on machine learning was developed. It can provide assistance on frailty, especially pre-frailty screening and decision-making tasks.