Theoretical calculation boosting the chemical vapor deposition growth of graphene film

Chemical vapor deposition (CVD) is a promising method for the mass production of high-quality graphene films, and great progress has been made over the last decade. Currently, the CVD growth of graphene is being pushed to achieve further advancements, such as super-clean, ultra-flat, and defect-free materials, as well as controlling the layer, stacking order, and doping level during large-scale preparation. The production of high-quality graphene by CVD relies on an in-depth knowledge of the growth mechanisms, in which theoretical calculations play a crucial role in providing valuable insights into the energy-, time-, and scale-dependent processes occurring during high-temperature growth. Here, we focus on the theoretical calculations and discuss the recent progress and challenges that need to be overcome to achieve controllable growth of high-quality graphene films on transition-metal substrates. Furthermore, we present some state-of-the-art graphene-related structures with novel properties, which are expected to enable new applications of graphene-based materials. (c) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Roles of Transition Metal Substrates in Graphene Chemical Vapor Deposition Growth

Graphene has attracted great attention owing to its excellent physical and chemical properties and potential applications. Presently, we can grow large-scale single-crystal graphene on transition metal substrates, especially Cu(111) or CuNi(111) surfaces, using the chemical vapor deposition (CVD) method. To optimize graphene synthesis for large-scale production, understanding the growth mechanism at the atomic scale is critical. Herein, we summarize the theoretical studies on the roles of the metal substrate in graphene CVD growth and the related mechanisms. Firstly, the metal substrate catalyzes the carbon feedstock decomposition. The dissociation of CH4, absorption, and diffusion of active carbon species on various metal surfaces are discussed. Secondly, the substrate facilitates graphene nucleation with controllable nucleation density. The dissociation and diffusion of carbon atoms on the CuNi alloy surface with different Ni compositions are revealed. The metal substrate also catalyzes the growth of graphene by incorporating C atoms from the substrate into the edge of graphene and repairing possible defects. On the most used Cu(111), each armchair site on the edge of graphene is intended to be passivated by a Cu atom and lowers the barrier of incorporating C atoms into the graphene edge. The potential route of healing the defects during graphene CVD growth is summarized. Moreover, the substrate controls the orientation of the epitaxial graphene. The graphene edge-catalyst interaction is strong and is responsible for the orientation determination of a small graphene island in the early nucleation stage. There are three modes for graphene growth on metal substrate, i.e. embedded mode, step-attached mode and onterrace mode, and the preferred growth modes are not all alike but vary from metal to metal. On a soft metal like Cu(111), graphene tends to grow in step-attached or embedded modes and therefore has a fixed orientation relative to the metal crystal lattice. Finally, the formation of wrinkles and step bunches in graphene because of the difference in thermal expansion coefficients between graphene and the metal substrate is discussed. The large friction force and strong interaction between graphene and the substrate make it energetically unfavorable for the formation of wrinkles. Different from the formation of wrinkles, the main driving force behind metal surface step-bunching in CVD graphene growth, even in the absence of a compression strain is revealed. Although significant effort is still required to adequately understand graphene catalytic growth, these theoretical studies offer guidelines for experimental designs. Furthermore, we provide the key issues to be explored in the future

Rational Design of Binary Alloys for Catalytic Growth of Graphene via Chemical Vapor Deposition

Chemical vapor deposition is the most promising technique for the mass production of high-quality graphene, in which the metal substrate plays a crucial role in the catalytic decomposition of the carbon source, assisting the attachment of the active carbon species, and regulating the structure of the graphene film. Due to some drawbacks of single metal substrates, alloy substrates have gradually attracted attention owing to their complementarity in the catalytic growth of graphene. In this review, we focus on the rational design of binary alloys, such as Cu/Ni, Ni/Mo, and Cu/Si, to control the layer numbers and growth rate of graphene. By analyzing the elementary steps of graphene growth, general principles are summarized in terms of the catalytic activity, metal–carbon interactions, carbon solubility, and mutual miscibility. Several challenges in this field are also put forward to inspire the novel design of alloy catalysts and the synthesis of graphene films bearing desirable properties

High activity levels of avian influenza upwards 2018–2022: A global epidemiological overview of fowl and human infections

Due to growing activities of avian influenza, more attention should be paid to avian influenza virus infections. Global summaries or national reports lack data on epidemiological patterns of avian influenza. A descriptive epidemiological analysis of avian influenza outbreaks from 2018 to 2022 was conducted, particularly fowl infections, human infections, and sequence alterations. The number of fowl infection outbreaks in the first half of 2022 was the highest level in the five-year period. Countries or regions could reliably be classified into three clusters according to fowl infection activity scores, with 60.0% of countries or regions in C1 in Europe. Additionally, two host infection patterns of countries were noted, led by the Taiwan (China) region and Germany. Human infections also increased, with 88.1% of cases being in China with an increasing risk of cases in northern China. Sequences that were furin cleavage motif present spread from Asia to Europe and North America over the five-year period. Continuous changes in the global activities of avian influenza highlight the need for sustained global surveillance, including strengthening monitoring capacity for vulnerable population and dynamically detecting new cases or genetic variations of the avian influenza virus under the One Health framework

Slip-Line-Guided Growth of Graphene

Manipulating the crystal orientation of emerging 2D materials via chemical vapor deposition (CVD) is a key premise for obtaining single-crystalline films and designing specific grain-boundary (GB) structures. Herein, the controllable crystal orientation of graphene during the CVD process is demonstrated on a single-crystal metal surface with preexisting atomic-scale stair steps resulting from dislocation slip lines. The slip-line-guided growth principle is established to explain and predict the crystal orientation distribution of graphene on a variety of metal facets, especially for the multidirectional growth cases on Cu(hk0) and Cu(hkl) substrates. Not only large-area single-crystal graphene, but also bicrystal graphene with controllable GB misorientations, are successfully synthesized by rationally employing tailored metal substrate facets. As a demonstration, bicrystal graphenes with misorientations of approximate to 21 degrees and approximate to 11 degrees are constructed on Cu(410) and Cu(430) foils, respectively. This guideline builds a bridge linking the crystal orientation of graphene and the substrate facet, thereby opening a new avenue for constructing bicrystals with the desired GB structures or manipulating 2D superlattice twist angles in a bottom-up manner

Influenza epidemic trend surveillance and prediction based on search engine data: deep learning model study

Background: influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve.Objective: this study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods.Methods: we collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend.Results: this study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China.Conclusions: complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.</p

Healthcare Workers’ Attitudes toward Influenza Vaccination: A Behaviour and Social Drivers Survey

This study aimed to understand the intention and correlation of receiving and recommending influenza vaccine (IV) among healthcare workers (HCWs) in China during the 2022/2023 season using the behavior and social drivers (BeSD) tools. A self-administered electronic survey collected 17,832 participants on a media platform. We investigated the willingness of IV and used multivariate logistic regression analysis to explore its associated factors. The average scores of the 3Cs’ model were compared by multiple comparisons. We also explored the factors that potentially correlated with recommendation willingness by partial regression. The willingness of IV was 74.89% among HCWs, and 82.58% of the participants were likely to recommend it to others during this season. Thinking and feeling was the strongest domain independently associated with willingness. All domains in BeSD were significantly different between the hesitancy and acceptance groups. Central factors in the 3Cs model were significantly different among groups (p p  <  0.001) after controlling for their IV willingness and perceived risk. HCWs’ attitudes towards IV affect their vaccination and recommendation. The BeSD framework revealed the drivers during the decision-making process. Further study should classify the causes in detail to refine HCWs’ education