The environmental tobacco smoke (ETS) exposure increases the risk of lower respiratory tract infections (LRTI) in children.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"8283",leadTitle:null,fullTitle:"Innovations in Higher Education - Cases on Transforming and Advancing Practice",title:"Innovations in Higher Education",subtitle:"Cases on Transforming and Advancing Practice",reviewType:"peer-reviewed",abstract:"Higher education contributes to the development of countries and their competitiveness in a global marketplace. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"17353",title:"Cigarette Smoking and Lower Respiratory Tract Infection",doi:"10.5772/17652",slug:"cigarette-smoking-and-lower-respiratory-tract-infection",body:'\n\t\tAcute bronchitis, one of the most common diagnoses in ambulatory care medicine, accounted for approximately 2.5 million visits to U.S. physicians in 1998 (Slusarcick & McCaig, 2000). This condition consistently ranks as one of the top ten diagnoses for which patients seek medical care, with cough being the most frequently mentioned symptom necessitating office evaluation (Knutson & Braun, 2002; Saldías et al., 2007). The diagnosis is based on clinical findings, without standardized diagnostic signs and sensitive or specific confirmation laboratory tests (Oeffinger et al., 1997).
\n\t\t\tAcute bronchitis is usually caused by a viral infection, especially by influenza, parainfluenza and respiratory syncytial virus, it is also caused by adenovirus, coronavirus and rhinovirus (Marrie, 1998). When microbiological studies are performed, less than 10-20% of patients will have evidence of acute bacterial infection (Macfarlane et al., 2001). Thus, Bordetella pertussis, Mycoplasma pneumoniae and Chlamydia pneumoniae have been clearly established as causes of acute bronchitis. But there is no clear evidence that Streptococcus pneumoniae, Haemophilus influenzae or Moraxella catarrhalis cause acute bronchitis in adults without underlying lung disease; studies have failed to distinguish between colonization and acute infection (Ramirez-Ronda et al., 1981; Treanor & Hayden, 2000). However, these bacteria are important causes of superinfections after acute viral respiratory illnesses (Hament et al., 1999; Peltola & McCullers, 2004).
\n\t\t\tThe devastating health impact of cigarette smoking is well known (Kuper et al., 2002; Stewart et al., 2008). Despite ongoing efforts to reduce smoking prevalence, over 1.1 billion people continue to smoke, representing one-sixth of the world’s population (Jha et al., 2002). Cigarette smoking is a major risk factor for premature mortality due to cancer, cardiovascular and cerebrovascular disease, and chronic obstructive pulmonary disease (Dye & Adler, 1994). About half of all smokers will develop a serious smoking-related illness, such as chronic obstructive pulmonary disease (COPD), which is characterized by irreversible airway obstruction, or cardiovascular disease. Furthermore, about 1–5% of smokers will develop a smoking-related malignancy, mostly lung adenocarcinoma or other epithelial cell tumours. But cigarette smoking also appears to be a major risk factor for respiratory tract infections (Marcy & Merrill, 1987). Both active and passive cigarette smoke exposure increase the risk of infections. Passive exposure to tobacco smoke in children contributes significantly to morbidity and mortality (Cheraghi & Salvi, 2009). Children in particular, seem to be the most susceptible population for the harmful effects of environmental tobacco smoke (ETS). Exposure to ETS amongst children at homes have been reported to vary from 27.6% in Africa, 34.3% in South East Asia, 50.6% in Western Pacific, and up to 77.8% in Europe (Warren et al., 2008). The morbidity and mortality of infectious diseases associated to smoking are not widely appreciated by physicians. The mechanism of increased susceptibility to infections in smokers is multifactorial and includes alteration of the structural (Dye & Adler, 1994; Marcy & Merrill, 1987) and immunologic host defenses (Sopori et al., 1994; Sopori et al., 1998). We reviewed the epidemiology of smoking-related lung infections and the mechanisms by which smoking increases the risk of infection.
\n\t\tThe specific mechanisms by which cigarette smoking increases the risk of respiratory infections are incompletely understood (Saldías et al., 2007; Domagala-Kulawik, 2008). They are multifactorial and probably interactive in their effects. Mechanisms by which smoking increases the risk of infections include structural changes in the respiratory tract (Dye & Adler, 1994) and a decrease in immune response (Sopori et al., 1998).
\n\t\t\tThe ciliated respiratory epithelium, the main target of most respiratory viruses, is the first line of defense against harmful environmental agents and protects by sweeping particles away in the overlying mucus gel layer, phagocytosing and killing some pathogens, maintaining a barrier through tight junctions and priming, activating and recruiting other immune cells. Cigarette smoke and many of its components produce structural changes in the respiratory tract. These changes include peribronchiolar inflammation and fibrosis, increased mucosal permeability, impairment of the mucociliary clearance, changes in pathogen adherence, and disruption of the respiratory epithelium (Dye & Adler, 1994). These changes are thought to predispose to the development of upper and lower respiratory tract infections, which may amplify the cigarette smoke–induced lung inflammation. A number of components of cigarette smoke, including acrolein, acetaldehyde, formaldehyde, free radicals produced from chemical reactions within the cigarette smoke, and nitric oxide, may contribute to the observed structural alterations in the airway epithelial cells (Marcy & Merrill, 1987).
\n\t\t\t\tSmoke directly compromises the integrity of this physical barrier, increases the permeability of the respiratory epithelium and impairs mucociliary clearance (Dye & Adler, 1994; Jones et al., 1980; Burns et al., 1989). Although cigarette smoke has been shown to activate epithelial cells to produce pro-inflammatory mediators (Mio et al., 1997), it attenuates the in vitro production of pro-inflammatory mediators by epithelial cells following stimulation with pathogen-associated molecular patterns (PAMPs), such as lipopolysaccharide or double-stranded RNA (Laan et al., 2004; Bauer et al., 2008). Smoke also induces direct oxidative damage to membrane lipids and causes extensive single-strand DNA breaks, triggering repair and apoptotic cascades (Kim et al., 2004). Thus, cigarette smoke acutely suppresses the respiratory epithelium and chronically can cause damage, inflammation and may ultimately transform it.
\n\t\t\tCigarette smoke has been shown to affect a wide range of host defense mechanisms (Sopori et al., 1994). However, findings between studies can be controversial and sometimes contradictory, probably because of differences in smoking history, genetic susceptibility and socioeconomic status (such as exercise, nutrition, occupation and ambient air quality, which can modify disease). Similar issues apply to animal models and in vitro systems, in which parameters of smoke exposure, such as duration, frequency and mode vary markedly between studies (Domagala-Kulawik, 2008; Stämpfli & Anderson, 2009). Accordingly, as the patterns of smoke exposure are so varied individually and geographically, no single experimental smoke exposure system can replicate the diversity of human smoking patterns, and each experimental system probably reflects only facets of the overall picture.
\n\t\t\t\t\n\t\t\t\t\t\tWhite blood cell count and distribution in peripheral blood. Smokers usually exhibit an elevated peripheral white blood cell count, around 30% higher than that of nonsmokers (Friedman et al., 1973; Yeung & Buncio, 1984; Tollerud et al., 1989; Mili et al., 1991). It has been shown a significant relationship between the white blood cell count in smokers and the plasma concentration of nicotine (Taylor et al., 1986). It has been suggested that nicotine induced catecholamine release might be the mechanism for this effect (Friedman et al., 1973). Other studies support the hypothesis that cigarette smoking causes bone marrow stimulation (Van Eeden & Hogg, 2000). It has been suggested that proinflammatory factors released from alveolar macrophages, such as tumor necrosis factor α, interleukin (IL) 1, IL-8, and granulocyte-macrophage colony-stimulating factor, are probably responsible for the stimulation of bone marrow by cigarette smoking. It has been reported the same relationship between cigarette smoking and increased leukocyte count in adolescents, indicating that there appears to be a rapid effect of cigarette smoking on white blood cell count that is unlikely to be due to smoking induced chronic disease conditions as seen in adult smokers (Tell et al., 1985).
\n\t\t\t\t\tReports of the effects of smoking on the different subsets of lymphocyte T cells are conflicting. Light to moderate smokers were reported to have a significant increase in CD3+ and CD4+ counts and a trend toward increased CD8+ lymphocyte count (Miller et al., 1982; Hughes et al., 1985; Tollerud et al., 1989; Mili et al., 1991). By contrast, studies of heavy smokers (over 50 pack-years) reported a decrease in CD4+ and a significant increase in CD8+ cell counts. Thus, the decrease observed in the ratio of CD4+ to CD8+ lymphocytes in heavy smokers was due predominantly to an increase of CD8+ cells (Ginns et al., 1982). These effects appeared to be reversible as soon as 6 weeks after smoking cessation (Miller et al., 1982). Other studies have reported no difference in the CD4+ and CD8+ lymphocyte counts among moderate smokers (Costabel et al., 1986). Since CD4+ cells facilitate B-cell proliferation and differentiation and immunoglobulin synthesis, the decrease in this subset observed in heavy smokers might contribute to the increased susceptibility to infections in this population.
\n\t\t\t\t\t\n\t\t\t\t\t\tAirways and lung parenchyma. Bronchoalveolar lavage studies have demonstrated a marked decrease in the absolute number of CD4+ cells, and an increase in CD8+ cells with a lower CD4+/CD8+ cell ratio in moderate smokers vs nonsmokers (Leatherman et al., 1984; Costabel et al., 1986; Wewers et al., 1998). No significant changes in these variables in the peripheral blood were found in this population of moderate smokers, in contrast with the findings in heavy smokers discussed previously. Thus, changes in lymphocyte population in the bronchoalveolar lavage in smokers may disclose pathologic changes earlier than in blood. Moreover, these findings suggest that smokers have a deficit in cell-mediated immunity in the lung alveolus, a site critical in the first-line defense against infection.
\n\t\t\t\t\tThe retention of CD8+ T cells in the lungs of chronic smokers warrants particular attention as it is a hallmark of COPD and it is known that these cells can activate alveolar macrophages to produce matrix metalloproteinase 12, a potent elastin-degrading enzyme that has been linked to emphysema (Hautamaki et al., 1997; Grumelli et al., 2004). Furthermore, CD8+ T cells are required for inflammation and tissue destruction in smoke-induced emphysema in mice (Maeno et al., 2007). Cigarette smoke has also been found to promote the retention of virus-specific CD8+ memory effector T cells, but to weaken their defensive ability (Gualano et al., 2008).
\n\t\t\t\t\tSmoking is also associated with significant increases in the percentage of macrophages in bronchoalveolar lavage fluid (Wewers et al., 1998). Owing to their strategic positioning within the alveolar space, alveolar macrophages have a key role in sensing and eliminating microbial agents early in the course of an infection. Cigarette smoking increases the number of alveolar macrophages (Sopori et al., 1998) and activates them to produce pro-inflammatory mediators, reactive oxygen species and proteolytic enzymes (de Boer et al., 2000; Russell et al., 2002), thereby providing a cellular mechanism that links smoking with inflammation and tissue damage. Similar to its effects on the respiratory epithelium, cigarette smoke compromises the ability of alveolar macrophages to phagocytose bacteria (King et al., 1988; Berenson et al., 2006) and apoptotic cells (Hodge et al., 2007) and to sense PAMPs (Drannik et al., 2004; Chen et al., 2007; Gaschler et al., 2008). Importantly, cigarette smoke may not simply suppress the function of alveolar macrophages as previously suggested, but instead might skew their inflammatory mediator profile. The nature of the skewing may be a determinant of disease susceptibility. Accordingly, one study reported a distinctive state of activation of alveolar macrophages in smokers that distinguished them from those in non-smokers (Woodruff et al., 2005). This highlights a key emerging concept — smoke may induce partial M1 deactivation or partial M2 activation of macrophages. The balance and intensity of this skewing has direct implications for the immune system and its response to disease because effective host defense requires a macrophage activation programme that is appropriate for the particular type of pathogen and because M1-type macrophages can cause marked lung damage (emphysema), whereas M2-type macrophages are linked to tumour progression. The molecular mechanisms of altered alveolar macrophage responsiveness and skewing are not presently understood but they are at least partially reversible by exposure to the reduced form of glutathione, which implicates oxidative damage of effector pathways. The infection risk is compounded by host deficiencies or polymorphisms in innate and adaptive immune response genes, in particular those encoding pattern recognition receptors, such as mannose-binding lectin, and their signal transduction intermediates (Becker & O’Neill, 2007).
\n\t\t\t\t\tIn the lungs, dendritic cells (DCs), which are the most potent antigen-presenting cells and are indispensable for the initiation of T cell-mediated immune responses (Mellman & Steinman, 2001), are probably highly susceptible to smoke-induced effects because of their anatomical position (in the lumen and directly beneath the epithelium of the lung) (McComb et al., 2008). Although it is known that the DC-directed chemokine CX3CL1 is upregulated in emphysema (McComb et al., 2008), there are only a few studies assessing the effects of smoking on lung DCs in humans and animal models (Tsoumakidou et al., 2008). Clinical studies suggest that the number of mature DCs is reduced in the large airways of patients with COPD who smoke (Jahnsen et al., 2006). Following smoking cessation, the numbers of mature DCs increase and are similar to non-smoking healthy controls. By contrast, the number of immature DCs is increased in the small airways of patients with COPD compared with individuals who have never smoked and individuals who smoke but do not have COPD (McComb et al., 2008). These data indicate that smoking behavior may affect DC numbers and maturity state.
\n\t\t\t\t\t\n\t\t\t\t\t\tLeukocytes function. Polymorphonuclear leukocytes from the peripheral blood of smokers exhibit depressed migration and chemotaxis compared with PMNs from nonsmokers (Noble & Penny, 1975; Corberand et al., 1979). The motility and chemotaxis of PMNs are depressed in the oral cavity of smokers compared with nonsmokers (Eichel & Shahrik, 1969; Noble & Penny, 1975). The whole cigarette smoke, its gas phase and the water-soluble fraction are potent inhibitors of PMN chemotaxis (Bridges et al., 1977). Of the water-soluble fraction of cigarette smoking, the unsaturated aldehydes (acrolein and crotonaldehyde) were the major contributors to the inhibitor properties. The non-volatile components of cigarette smoking also inhibit chemotaxis by a mechanism that differs from that of the unsaturated aldehydes present in the vapour phase of smoke (Bridges et al., 1977; Bridges & Hsieh, 1986). The non-volatile component did not inhibit migration. Nicotine had no effect on PMN migration and chemotaxis (Sasagawa et al., 1985). Macrophages from the lungs of smokers have a greater inhibitory effect on lymphocyte proliferation than macrophages from the lungs of nonsmokers. Thus, the immunosuppressive effects of the macrophages on cell-mediated immune response are increased in smokers (Holt, 1987). The release of cytokines (TNFα, IL-1, IL-2 and IL-6) from macrophages may also be altered in smokers (McCrea et al., 1994; Twigg et al., 1994; Ouyang et al., 2000; Hagiwara et al., 2001). Hydroquinone, the phenolic compound in cigarette tar, had the most potent inhibitory effect of these cytokines, whereas nicotine had little effect. The cytokines IL-1 and IL-6 are important in the host defense against infection (Smith, 1988; Luster et al., 1999). Animal studies have shown that depletion of these cytokines increases susceptibility to bacterial pneumonia. Since PMNs play a significant role in host defense against acute bacterial infections, an impairment of PMN functions by smoke may contribute to the increased susceptibility of smokers to systemic infections, including bacterial pneumonia.
\n\t\t\t\t\t\n\t\t\t\t\t\tLymphocyte functions. Natural killer (NK) cell activity in peripheral blood has been reported to be reduced in smokers compared with nonsmokers (Ferson et al., 1979; Hughes et al., 1985; Tollerud et al., 1989; Nair et al., 1990). These alterations appear to be reversible, since NK activity in ex-smokers was similar to that of a never-smoking group compared with smokers (Silverman et al., 1975; Hersey et al., 1983). The recovery period was relatively short, as little as 6 weeks (Miller et al., 1982; Hughes et al., 1985). Since NK cells are important in the early surveillance response against viral infections and resistance against microbial infections (Herberman & Holden, 1978; Herberman, 1980), impairment of NK cell activity by cigarette smoking is a potential mechanism for the increased incidence of infections among smokers.
\n\t\t\t\t\tMounting evidence suggests that natural killer cells have an important role in innate host defense against microbial agents and in protective antitumour immune surveillance. This is achieved by direct cytotoxicity through perforin and granzymes, CD95 ligand-induced apoptosis and pro-inflammatory cytokine and chemokine release (Tollerud et al., 1989; Hamerman et al, 2005). Several studies have shown that NK cell numbers and activity are decreased in smokers compared with non-smokers (Swann et al., 2007). Exposure to cigarette smoke attenuates the cytotoxic activity and cytokine production of NK cells in humans and mice (Lu et al., 2006; Mian et al., 2008), thereby linking NK cell defects to increased infection risk and cancer.
\n\t\t\t\t\tAnimal studies have shown that nicotine inhibits the antibody-forming cell response through impairment of antigen-mediated signalling in T cells and suppression of intracellular calcium response (Geng et al., 1995; Geng et al., 1996; Sopori et al., 1998). It has been suggested that nicotine through activation of protein tyrosine kinases and depletion of inositol-1,4,5-trisphosphate-sensitive calcium stores in T cells could be a major immunosuppressive component in cigarette smoking (Kalra et al., 2000).
\n\t\t\t\t\n\t\t\t\t\t\tPeripheral blood. The effects of cigarette smoking on humoral immunity have been studied extensively (Sopori et al., 1994; Sopori et al., 1998). Several studies have found that smokers had serum immunoglobulin levels (IgA, IgG, and IgM) 10% to 20% lower than those of nonsmokers (Dales et al., 1974; Ferson et al., 1979; Gerrard et al., 1980; Andersen et al., 1982). It has been shown that IgA, IgG, and IgM levels are higher among former smokers than current smokers and increased with duration of smoking cessation (Mili et al., 1991). This suggests that the effect is reversible, with a return toward the immunoglobulin levels of nonsmokers. It has been reported that three months after subjects stopped smoking, IgG and IgM but not IgA levels have increased compared with levels during smoking (Hersey et al., 1983).
\n\t\t\t\t\t\n\t\t\t\t\t\tLung parenchyma. The IgG content of bronchial fluids was found to be twice higher in smokers than nonsmokers (Onari et al., 1978; Hersey et al., 1983). A selective increase in immunoglobulin levels could be explained either by stimulation of local immunoglobulin production or by exudation of plasma immunoglobulin into alveolar spaces in response to inhaled cigarette smoke (Warr et al., 1977). The availability of opsonic antimicrobial antibodies is essential for optimal function of phagocytes to take up and contain bacteria (Reynolds, 1988). The antibody response to a variety of antigens, such as influenza virus infection and vaccination is depressed in cigarette smokers (Finklea et al., 1971).
\n\t\t\t\t\tAutoimmunity has been proposed as a cause of smoke-induced lung disease. B cells are abundant in smoke-induced lung disease, and their roles, although obscure, are probably greatly underestimated. Cigarette smoke serves as an adjuvant, possibly because it is a potent inducer of granulocyte/macrophage colony-stimulating factor production in the lungs, which enhances the ability of DCs to present antigen and probably to induce TH2 type-biased immune responses (Trimble et al, 2009).
\n\t\t\t\tGiven the complex nature of the immune system, in which unaffected defense mechanisms may compensate for local deficiencies, it is difficult to predict how the impact of cigarette smoke on specific host defense pathways affects the overall responses to microbial agents (Domagala-Kulawik, 2008). Specifically, it is unclear whether the increased risk of infection observed in smokers is due to increased susceptibility to microbial agents, an inability to effectively clear infectious agents or exaggerated pro-inflammatory responses to microbial agents (owing to changes in immune homeostasis), thereby evoking symptoms of infection. Similar considerations apply to acute exacerbations of COPD that are due to bacterial and viral infections (Sethi & Murphy, 2001; Wedzicha, 2004; Papi et al., 2006) and to microbial colonization of the airways, which occurs in approximately one third of patients with COPD (Patel et al., 2002).
\n\t\t\tEnvironmental tobacco smoke exposure increases significantly the risk of lower respiratory tract infections in children, especially maternal smoking (Table 1). Using mouse models, it has been investigated the effects of cigarette smoke on inflammatory processes, viral clearance and secondary immune protection following influenza virus infection (Robbins et al., 2006; Gualano et al., 2008; Kang et al., 2008). Cigarette smoke exposure was found to be associated with exacerbated pro-inflammatory responses to influenza virus, although neither the rate of viral clearance nor the development of influenza virus-specific memory responses were compromised. Hence, cigarette smoke mainly affects primary antiviral inflammatory processes, whereas secondary immune protection remains intact (Robbins et al., 2006). The heightened inflammatory response was associated with increased production of pro-inflammatory mediators and mortality. Furthermore, one study (Kang et al., 2008) showed that increased inflammation led to accelerated emphysema formation and airway fibrosis, providing evidence that altered responsiveness to viral agents may contribute to the pathogenesis of emphysema.
\n\t\t\t\t\n\t\t\t\t\tCommon cold. Several epidemiological studies support the association between smoking and the prevalence of colds and lower respiratory tract symptoms. In a prospective cohort study that examined a large group of US Army recruits found an increased risk of upper respiratory tract infection in smokers (relative risk: 1.5; 95% confidence interval [CI], 1.1-1.8) (Blake et al., 1988). It has been reported that smoking status is predictive of the development of clinical colds when healthy volunteers are exposed intranasally to a low dose of respiratory viruses (Cohen et al., 1993). Viral suspensions were installed into the nares and infections were diagnosed on the basis of viral isolation, virus-specific antibody, and clinical findings. Smokers had a significantly higher incidence of acute infection (clinical cold) than nonsmokers (OR: 2.23; 95% CI, 1.03-4.82). Among virologically confirmed infected individuals, smoking was associated with a higher likelihood of symptoms leading to a clinical diagnosis (OR: 1.83; 95% CI, 1.00-3.36). The relationship between smoking and increased symptoms from viral respiratory infections could be explained by impairment of immune processes that limit viral replication or enhancement of inflammatory processes involved in the production of symptoms.
\n\t\t\t\t\n\t\t\t\t\tInfluenza. Several studies have confirmed the relationship between cigarette smoking and the risk of influenza infections (Finklea et al., 1969). Influenza infections are more severe, with more cough, acute and chronic phlegm production, breathlessness, and wheezing in smokers. Female smokers in the Israeli Army had increased risk of influenza (OR: 1.44; 95% CI, 1.03-2.01) and complications associated to influenza infection compared with nonsmokers (Kark & Lebiush, 1981). In another study, the incidence of influenza in healthy young male recruits was higher among smokers (OR: 2.42; 95% CI, 1.53-3.83) (Kark et al., 1982). Influenza was more severe among smokers, with a dose-related increase in rate: 30% in nonsmokers, 43% in light smokers, and 54% in heavy smokers (p<0.001). Overall, 31.2% (95% CI, 16.5-43.1) of influenza cases were attributed to cigarette smoking.
\n\t\t\t\tEnhanced bacterial adherence has been documented for respiratory cells infected, with influenza A virus being responsible for viral-bacterial combination pneumonia (Hament et al., 1999). Studies have suggested that inflammatory activation of platelet-activating factor is an important factor in the attachment and invasion of cells by pneumococcal strains. Cigarette smoking alters platelet-activating factor metabolism and may contribute to the increased incidence of bacterial superinfection in people who develop influenza (Miyaura et al., 1992; Ichimaru & Tai, 1992).
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\n\t\t\t\t\t\t\t\tReferences: 1) Environ Health Prospect 1997;105:302-6; 2) Pediatr Pulmonol 1999;27:5-13.; 3) Eur J Epidemiol 2000;16:465-8; 4) J Pediatr 2000;137:865-70; 5) An Esp Pediatr 2000;53:339-45; 6) Rev Saude\n\t\t\t\t\t\t\t\tPublica 2003;37:485-93; 7) Acta Paediatr 2003;92:1314-21; 8) Eur J Epidemiol 2005;20:719-27; 9) Tob\n\t\t\t\t\t\t\t\tControl 2006;15:294-301; 10) Environ Health Perspect 2006;114:1126-32; 11) Eur J Pediatr 2007;166:455-9; 12) Indian J Pediatr 2008;75:335-40; 13) Arch Bronconeumol 2009;45:585-90. \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
The environmental tobacco smoke (ETS) exposure increases the risk of lower respiratory tract infections (LRTI) in children.
Although influenza was more severe in smokers, antibody levels to A(H1N1) antigen were not significantly higher than those of nonsmokers. Moreover, influenza antibodies wane more rapidly in smokers than in nonsmokers (Finklea et al., 1971). This finding suggests that smokers are not only at a high risk of influenza, but have an increased susceptibility to new attacks afterward (Kark et al., 1982). Influenza rates are similar in vaccinated smokers and nonsmokers. However, influenza vaccination can be considered to be more efficacious in smokers than nonsmokers because the infection rates are higher in unvaccinated smokers (Cruijff et al., 1999).
\n\t\t\t\t\n\t\t\t\t\tRespiratory syncytial virus bronchiolitis. Respiratory syncytial virus (RSV) infection is very common in early life; over 95% of children have been infected by two years of age. RSV infections are responsible for over 100,000 hospital admissions in the United States annually, mostly affecting infants (Moler & Ohmit, 1999). Of RSV-related admissions, 7% to 21% will require ventilatory support because of respiratory insufficiency (Everard & Milner, 1992). Therefore, RSV infection imposes a significant burden on children early in life. Maternal smoking exposure has been shown to reduce lung function in children, and several studies suggest that this effect on lung function is attributable primarily to exposure during pregnancy (intrauterine cigarette smoke exposure) (Hofhuis et al., 2003). Maternal smoking during pregnancy may impair in utero airway development or alter lung elastic properties. It has been shown that maternal cigarette smoking, especially postnatal, increase the severity of RSV bronchiolitis infection in infants (Gürkan et al., 2000; Lanari et al., 2002; Bradley et al., 2005). As a preventive measure, it has been reported a protective effect of long-term breastfeeding on the risk of lower respiratory tract infection during the first year of life, especially in children exposed to environmental tobacco smoke (Nafstad et al., 1996).
\n\t\t\t\t\n\t\t\t\t\tPassive smoking and respiratory tract infections in childhood. A study in 1974 reported that infants of mothers who smoked had significantly more admissions for bronchitis or pneumonia than infants of non-smoking mothers (Harlap & Davies, 1974). The excess bronchitis and pneumonia increased with increased number of cigarettes smoked by the mother. This excess was mainly seen in infants aged 6 to 9 months. Another study (Colley et al., 1974), also found that the incidence of pneumonia and bronchitis in the first year of life was associated with parents\' smoking habits; the incidence was lowest where both parents were non-smokers, highest where both smoked, and lay between these two levels where only one parent smoked. During the following three decades, a large number of investigations have reported associations between parental smoking and occurrence of lower respiratory tract illness in young children (Table 1). A systematic review (Strachan & Cook, 1997) of around 50 studies in children up to 3 years, including 38 studies used for quantitative analysis, has confirmed these findings. There was consistency in the findings between the community and hospital studies. Pooled ratios were found to be 1.57 for smoking by either parent and 1.72 for maternal smoking. The reviewers also found that there was a significantly increased risk from smoking by other household members in families where the mother did not smoke (odds ratio 1.29). In most of the studies also a dose-response relationship was evident, and the associations with paternal smoking were still present after adjustment for confounding factors. From earlier studies, it appeared that the risk was higher during the first 6 months of life, and gradually decreased to slightly above normal by age 3 years. However, later studies (Taylor & Wadsworth, 1987) have found similar relationships between maternal smoking and lower respiratory illness in children up to 5 years of age. Exposure to passive smoking also seems to increase the risk for acute respiratory tract infections in older children (Jedrychowski & Flak, 1997). A dose-response between the degree of exposure to environmental tobacco smoke and acute respiratory infection was found in a cohort of 9-year-old children. Passive smoking combined with allergy nearly tripled the risk of acute respiratory tract infections (odds ratio 3.39).
\n\t\t\tSimilarly, cigarette smoke exposure was also found to be associated with increased inflammation following challenge with bacterial agents such as Pseudomonas aeruginosa and non-typeable H. influenzae (Drannik et al., 2004; Gaschler et al., 2009), two pathogens that are associated with COPD exacerbations. Cigarette smoke was associated with increased bacterial burden in mice infected with P. aeruginosa, whereas bacterial burden was decreased in smoke-exposed animals following infection with non-typeable H. influenzae. Changes in the bactericidal activity of alveolar macrophages may contribute to increased bacterial burden (Drannik et al., 2004), but it is unclear how infection with non-typeable H. influenzae induces decreased bacterial levels; the mechanisms of this are currently being investigated. Preliminary data suggest that increased levels of immunoglobulins in the bronchoalveolar lavage of smoke-exposed animals might have contributed to this phenomenon. This finding provides evidence that compensatory mechanisms may outweigh certain deficiencies and help to explain why not all smokers suffer from severe chest infections. As discussed above, smoke affects all people who are exposed to it, but the degree and severity is modified by many susceptibility determinants.
\n\t\t\t\t\n\t\t\t\t\tPneumococcal pneumonia. Cigarette smoking is a substantial risk factor for pneumococcal pneumonia, especially in patients with chronic obstructive pulmonary disease. However, even without chronic obstructive pulmonary disease, smoking is a major risk factor. In a population based surveillance study (Pastor et al., 1995), smoking was strongly associated with invasive pneumococcal disease in healthy young and middle aged adults, for whom pneumococcal vaccination is not currently recommended. Among such individuals with invasive pneumococcal disease, 47% were current smokers. The odds ratio (OR) for invasive pneumococcal disease was 2.6 (95% CI, 1.9-3.5) for smokers in the 24-to 64-year age group and 2.2 (95% CI, 1.4-3.4) for smokers 65 years or older. The attributable risk from smoking was 31% and 13% in these 2 groups, respectively.
\n\t\t\t\tA population based case-control study (Nuorti et al., 2000) showed that smoking was the strongest independent risk factor for invasive pneumococcal disease among immunocompetent adults. The OR was 4.1 (95% CI, 2.4-7.3) for active smoking and 2.5 (95% CI, 1.2-5.1) for passive smoke exposure in nonsmokers compared with nonexposed nonsmokers. The attributable risk in this population was 51% for cigarette smoking and 17% for passive smoking. The risk of pneumococcal disease declined to nonsmoker levels 10 years after cessation. In another case-control study, current smoking was associated with a nearly 2-fold risk of community-acquired pneumonia (OR: 1.88; 95% CI, 1.11-3.19), where 32% of the risk was attributable to cigarette smoking (Almirall et al, 1999). There was a trend toward a dose-response relationship: A 50% reduction in the OR was reported 5 years after cessation of smoking.
\n\t\t\t\tIn vitro adherence of Streptococcus pneumoniae to buccal epithelial cells has been shown to be increased in cigarette smokers (Raman et al., 1983). This increased adherence may persist for up to three years after smoking cessation. Since increased adherence of bacteria to surface cells is an established pathogenic step for bacterial colonization and infection in the lung, this may contribute to the increased risk of respiratory infection that exists in cigarette smokers.
\n\t\t\t\t\n\t\t\t\t\tLegionnaires disease. Legionnaires disease is a life-threatening lower respiratory tract infection responsible for 1% to 3% of community-acquired pneumonia. Diverse environmental factors have been identified, and cigarette smoking appears to be an independent risk factor (Doebbeling & Wenzel, 1987; Straus et al., 1996). The risk of legionnaires disease was significantly increased for smoking (OR: 3.48; 95% CI, 2.09-5.79), specially for persons without an underlying disease (OR: 7.49; 95% CI, 3.2-17.1) (Straus et al., 1996).
\n\t\t\t\t\n\t\t\t\t\tOtitis media and exposure to secondhand tobacco smoke. Long term tobacco smoke exposure is a risk factor for otitis media and bronchitis in children (Richardson, 1988). In a case-control study, children with recurrent otitis media more commonly had exposure to secondhand smoke (OR: 1.88; 95% CI, 1.02-3.49; p=0.04). A prospective follow-up of the case group showed no significant difference in the clinical course of the children who were exposed to secondhand smoke (Kitchens, 1995). In other study, passive smoking was a significant risk factor for otitis media with effusion and recurrent otitis media (Ilicali et al, 1999). But only maternal smoking was a significant factor (p<0.001). Moreover, in utero exposure to cigarette smoke was associated with an increased risk of otitis media. In a study (Stathis et al, 1999), acute ear infections were associated with the mother’s consumption of 1 to 9 cigarettes (OR: 1.6; 95% CI, 1.1-2.5), 10 to 19 cigarettes (OR: 2.6, 95% CI, 1.6-4.2), and 20 or more cigarettes (OR: 3.3; 95% CI, 1.9-5.9) per day during pregnancy. For subacute ear infections, an association was present with the mother’s consumption of 10 to 19 cigarettes (OR: 2.6; 95% CI, 1.4-5.0) and 20 or more cigarettes (OR: 2.8; 95% CI, 1.3-6.0). In utero exposure to 20 or more cigarettes per day was also associated with an increased risk of ear surgery by 5 years after delivery (OR: 2.9; 95% CI, 1.3-6.6).
\n\t\t\t\t\n\t\t\t\t\tTuberculosis. Developing tuberculosis disease involves two distinct transitions, with their corresponding risk factors: the transition from being exposed to being infected and the transition from being infected to developing disease. Several studies have shown that smoking is a risk factor for tuberculin skin test reactivity, skin test conversion, and the development of active tuberculosis (Table 2). It has been reported an increased relative risk of development of tuberculosis for heavy smokers compared with nonsmokers (RR: 2.17; 95%CI, 1.29-3.63) (Yu et al., 1988). After adjusting for age and heavy drinking, smokers of 20 years’ or greater duration had 2.6 times (95% CI, 1.1-5.9) the risk of nonsmokers for tuberculosis (Buskin et al., 1994). It has been found a strong association between active smoking and the risk of pulmonary tuberculosis (Alcaide et al., 1996). Both studies showed a dose-response relationship with the number of cigarettes consumed daily. In other study, current smokers had a nearly 2-fold increased risk compared with never-smokers (OR: 1.87; 95% CI, 0.73-4.80) (McCurdy et al., 1997).
\n\t\t\t\tA large case-control study in India examined smoking and tuberculosis in men between 35 and 69 years of age (Gajalakshmi et al., 2003). The tuberculosis prevalence risk ratio was 2.9 (95% CI, 2.6-3.3) for ever-smokers compared with never-smokers, and the prevalence was higher with a higher level of cigarette consumption. The authors found that the smoking attributable fraction of deaths from tuberculosis was 61%, greater than the fraction of smoking-attributable deaths from vascular disease or cancer. In a study among children living with a patient with active pulmonary tuberculosis, passive smoking confirmed by measurement of urinary cotinine levels was a strong risk factor for the development of active tuberculosis (OR: 5.39; 95% CI, 2.44-11.91) (Altet et al., 1996).
\n\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tReferences: 1) Tubercle 1988;69:105-12 ; 2) Am J Pub Health 1994;84:1750-6; 3) Tuber Lung Dis 1996; 77:112-6; 4) Tuber Lung Dis 1996;77:537-44; 5) Eur Respir J 2001;18:959-64; 6) Thorax 2002;57:964-6; 7) Int J Tuberc Lung Dis 2002;6:887-94; 8) Int J Tuberc Lung Dis 2003;7:980-6; 9) Addict Biol 2003;8:39-43; 10) Southeast Asian J Trop Med Public Health 2004;35:219-27; 11) Int J Tuberc Lung Dis 2004;8:194-203; 12) Am J Respir Crit Care Med 2004;170:1027-33; 13) Int J Epidemiol 2005;34:914-23; 14) Southeast Asian J Trop Med Public Health 2005;36:145-50. \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
Tobacco smoking and active tuberculosis (TB).
The biological basis by which tobacco smoking increases tuberculosis risk may be through a decrease in immune response, mechanical disruption of cilia function, defects in macrophage immune responses, and/or CD4+ lymphopenia, increasing the susceptibility to pulmonary tuberculosis (Rich & Ellner, 1994; Onwubalili et al., 1987).
\n\t\t\tSmoking appears to be an important risk factor for the acquisition of a lower respiratory tract infection (bronchitis, influenza, pneumonia, tuberculosis). This link is likely mediated by smoking’s adverse effects on respiratory defenses (structural and immune system changes induced by smoking). Considering the high rates of morbidity and mortality from pneumonia, tuberculosis and influenza, as well as the economic consequences of work days lost from lesser respiratory infections, the merits of smoking cessation are clear. The fact that smokers have been shown to be less likely than nonsmokers to undergo vaccination and yet are probably at higher risk for influenza and pneumococcal infections highlights the importance of targeting this group for vaccination. The available epidemiological evidence, from studies worldwide, indicates a dose-response relationship between smoking and tuberculosis and that the association is likely to be a causal one. This provides a compelling reason for smoking cessation measures to be undertaken to combat the scourge of tuberculosis, particularly in developing countries. Physicians should educate their smoking patients about their increased risk of respiratory infections, the importance of appropriate vaccinations, and the benefits of smoking cessation.
\n\t\tIn statistical physics only a few problems can be solved exactly. For complex problems, numerical methods can give exact results for problems that could only be solved in an approximate way. Numerical simulation can be a way to test the theory. The numerical results can be compared to the experimental results. The numerical simulation is placed between the fundamental and the experimental treatment; it has a quasi-experimental character (numerical experience). For problems of statistical physics, the most widely used simulation methods are the Monte Carlo method and the molecular dynamics method.
The first Monte Carlo simulation (MCS) was proposed by Metropolis et al. in 1953 [1]. The second Monte Carlo simulation was proposed by Wood and Parker in 1957 [2]. The obtained results were in good agreement with the experimental results of Bridgman [3] and those of Michels et al. [4]. In this method we attribute a series of initial positions chosen randomly to a system of N particles interacting through a defined potential. A sequence of particle configurations is generated by giving successive displacements to particles; we only retain configurations to ensure that the probability density is that of the chosen.
Molecular dynamics simulation (MDS) has been first introduced to simulate the behavior of fluids and solids at the molecular or atomic level. MDS was used for the first time by Alder and Wainwright in the late 1950s [5, 6] to study the interactions of hard spheres. The principle is the resolution of equations of motion for a hard sphere system in a simulation cell. The basic algorithm is Verlet’s algorithm [7].
In this chapter, we will present techniques of numerical simulations using the Monte Carlo method. We will present an application on the gas phase during plasma-enhanced chemical vapor deposition (PECVD) of thin films. The application concerns collisions between particles. Particles are in Brownian motion. Collisions, elastic or inelastic, are considered to be binary. Non-elastic collisions result in effective chemical reactions.
In Section 2, we cite some MCS and MDS works on PECVD processes. Section 3 presents general rules on numerical simulation methods. Section 4 presents how to simulate a physical problem using MCS? We present the Metropolis algorithm as a scheme to trait random configurations and different modules related to elaborate an MCS code. In Section 5, we apply the MCS on SiH4/H2 gas mixture during a PECVD process. Finally the conclusion summarizes the contents of the chapter.
The PECVD is the most widely used technique to produce hydrogenated amorphous silicon thin films (a-Si:H) for solar cells and for film transistors and electronic devices [8, 9]. Reactions during plasma deposition are complex and are not understood completely.
Gorbachev et al. [10, 11, 12] have developed a model that is based on chemical reactions and different processes in a PECVD reactor. The model takes into account the formation of SinHm oligomers (n ≤ 5). It presents a simulation of the growth of the films. Gorbachev et al. found that Si2H5 and Si3H7 strongly influence the growth of the film [11].
Valipa et al. [13] calculated the β reactivity of the SiH3 radical on a surface of a silicon lattice plane during the growth of a-Si:H using MDS. The mechanisms of physical and chemical interactions of low temperature plasmas with surfaces can be explored using MDS [14].
For a CH4/H2 mixture, Farouk et al. used the Monte Carlo method (PIC/MC); they calculated the ionization rate of the plasma and the deposition rate of the thin layer [15]. Rodgers et al. [16] have developed three-dimensional Monte Carlo simulations of diamond (100) surface CVD. Other works on MCS are in [17, 18, 19].
In our previous works [20, 21, 22, 23, 24], we were interested in the study of the gas phase and the interaction of plasmas with the surface, for SiH4/H2 and CH4/H2 gas mixtures during PECVD processes. The used numerical simulation techniques were MCS and MDS. To complete the studies, we used the fluid model [25].
The starting point of numerical simulation is a physical phenomenon; its purpose is to obtain useful physical results. Between these two points, several steps can be identified. These steps are general and they are applicable for MCS. The steps can be summarized as follows:
The physical phenomenon must be defined by the description of the dominant domain of physics. The main assumptions and simplifying approximations are necessary to understand the physical phenomenon and the design of the first model.
Mathematical model requires a mathematical formulation of the problem. It may be a problem of elements or discrete object or a problem of a continuous medium; it may be a spatiotemporal problem or frequency problem and may be a deterministic or probabilistic problem.
It would be interesting to know the mathematical equations that govern the phenomenon:
The forces between particles and elements
The potential interaction
The determination of a time scale
The determination of a length scale
Definition of constant magnitudes of motion and equilibrium magnitudes
Continuity equations, balance equations, transfer equations, etc.
The MCS technique has been chosen for this work; knowing its basic algorithm is necessary for elaborating the simulation. This step requires some actions:
Validation of the model on simple cases
Simulation calculation on complex phenomena
The MCS is based on a probabilistic process with a random choice of configurations and samples of the situation of the physical system. The two pedagogical examples most cited in the literature are the integration of a single variable function and Ising’s model of spin. In the following subsection, we define the integration of a single variable function. We introduce the Ising model at the end of Section 4.2.2.
Calculation of the definite integral for a function f(x) of a single variable x on domain {a, b} has been proposed (Figure 1):
The integral of a function f(x).
Let:
Let xi and yi be real random numbers (i = 1, 2,…, N), and let H be a real number greater than the f(x) for x belonging to the domain {a, b} (or x ∈ {a, b}).
Let r1 and r2 be two random numbers belonging to the domain {0, 1} according to a uniform distribution law. Generators (e.g., Ran, RANDOM, RANDUM, or other IMSL mathematical libraries) of random numbers can be used:
where xi and yi are random numbers (xi ∈ {a, b} and yi ∈ {0, H}).
The Monte Carlo (MC) method is based on a probabilistic process. Let N be the total number of cases chosen (possible cases). It is necessary to count the number of favorable cases (or the number of points below the curve y = f(x)); let yi ≤ f(xi)). The number of favorable cases is Nfav. When N➔∞, the value I of the integral is [26]:
An example [26] is the calculation of the value π by calculating the integral I on a quarter circle of unit radius (R = 1.0). The pairs of random numbers (xi, yi) satisfying the condition: xi2 + yi2 ≤ 1. The function f(x) is equal to
We take a = 0.0, b = 1.0, and H = 1.0.
For different values of N, we show that the numerical solution tends to π = 4I.
Although this integral is simple, it shows the strength and simplicity of the method. The technique can be generalized for the integration of multivariate functions.
We note that integration by the MC method is based on:
The choice of random configurations according to a uniform distribution law
Each configuration chosen is either favorable or unfavorable (the “or” is exclusive).
For statistical physics problems, the probabilistic choice of configurations is not always deterministic; the favorable and unfavorable cases are not exclusive. According to the Metropolis algorithm [26, 27], the steps of the simulation are:
Choice of a simulation cell of adequate shape to the studied phenomena. The size of the simulation cell is related to a scale of length characteristic of the forces and interaction potential of the studied phenomenon. This cell may contain Npc particles (and/or elements).
Choice of an initial configuration that responds to some physical and thermodynamic properties. The total or internal energy of the system is Ei.
Infinitesimal random displacement of a particle (or element of the system) and calculation of the new internal energy of the system Ef. This displacement is related to the physical magnitudes: time scale and length scale. The physical system tends toward a minimization of the internal energy of the system with some fluctuation. Let ΔE = Ef-Ei the fluctuation.
If ΔE ≤ 0; the new configuration is retained (favorable) and the different averages can be obtained; go to step (c).
If ΔE > 0; a random number ε is chosen such that 0 < ε < 1. Let the probability Pr equal to: Pr = exp. (−ΔE/kBT) (where kB is the Boltzmann constant and T is the temperature).
If ε < Pr, accept the move and in any case go back to step (c) for a new choice of an infinitesimal displacement (new configuration). Note that if such a trial move is rejected, the old configuration is again counted in the averaging with probability Pr.
Figure 2 shows how to choose between the selected configurations. Let ε be a random number following a uniform law; If ε1 ≤ Pr the configuration is retained, and if ε2 > Pr the configuration is rejected.
Configuration choice according to Metropolis scheme.
Numerical simulation using the MC method is a very important tool for the study of static properties. The basic algorithm is based on probability notions. Understanding of the distribution function and/or interaction potentials is the heart of the calculation.
In equilibrium statistical physics, the system has a certain probability that can be in any states. The probability of being in a state μ with energy H(μ) is given by the Boltzmann distribution P(μ):
where T is the absolute temperature and kB is called Boltzmann’s constant. It is conventional to denote the quantity (kBT)−1 by the symbol β. The normalizing factor Z, or partition function, is given by:
The average of a quantity Q fora system in equilibrium is:
The internal energy U, is given by:
which can be written in terms of a derivative of the partition function:
From thermodynamics we have expressions for the specific heat C, the entropy S, and the Helmholtz free energy F:
or
and
and
We can calculate other parameters affecting the system.
The Monte Carlo method is an excellent technique for estimating probabilities, and we can take advantage of this property in evaluating the results. The simplest and most popular model of a system of interacting variables in statistical physics is the Ising model. It consists of spins σi which are confined to the sites of a lattice and which may have only the values (+1) and (−1). These spins interact with their nearest neighbors on the lattice with interaction constant J; they can interact with an external magnetic field B coupling to the spins. The Hamiltonian H for this model is [26]:
The Ising model has been studied in one and two dimensions to obtain results of thermal properties, phase transition, and magnetic properties [26, 27, 28]. For chosen values of J and/or B, different steps may be taken for the calculations (simulation cell, initialization, configurations, boundary conditions, calculation algorithms). For any configuration, each spin takes the two possible directions. The detail of the calculation procedure is not the purpose of this chapter.
We give a system of N particles (atoms, molecules, ions or particles) placed in a cell of fixed volume, generally of cubic form. The initial positions may, depending on the case, be distributed randomly according to a certain law (uniform or otherwise) or have a given symmetry. In a fluid, a gas, or a plasma, the particles may have random positions in general; in a solid or surface, with a crystal structure, the particles take ordered positions. The choice of random initial positions allows great freedom on the choice of the number of particles in the cell.
At the first step, the particles are given velocities that are generally selected to have a zero total momentum. If the system is in thermodynamic equilibrium, the initial velocities will be randomly chosen according to a Maxwell-Boltzmann law. In the general case, the velocity distribution is according to the problem dealt with. All other phase properties can be initialized to the particles; the main thing is the conservation of the total quantities of the system.
The particles interact with each other according to chosen interaction potentials. Since the interaction potentials are specific for each “numerical experiment,” the main part of the work consists in calculating the interaction energies for each proposed configuration.
The choice of interaction potentials is directly related to the mathematical formulation of the problem according to the state of the medium: fluid, gas, plasma, or solid. It can be Lennard-Jones potential, Coulomb potential, Debye potential, Morse potential, Stillinger-Weber potential, Born-Mayer potential, Moliere potential, or others.
In general, two main boundary conditions are used: periodic boundary conditions (PBC) and minimum image convention (MIC) [29].
To minimize the surface effect, periodic boundary conditions (PBC) [30] are invariably imposed. The simulation cell is reproduced throughout the space to form an infinite mesh. We can simulate the properties of an infinite system. The particles that we follow are in the central cell; if a particle crosses a wall with a certain velocity, its image returns with the same velocity by the opposite wall. Under these conditions, the number of particles in the central cell, and consequently the density, is constant. These conditions also allow the conservation of the energy and the momentum of the system and do not introduce periodic effects (because of the interaction between particles).
According to the hypotheses and according to the geometry of the problem, other boundary conditions are proposed [26]. For example, in order to model thin films, the simulation cells are longitudinal and parallel to the film; one uses PBC in the directions parallel to the film. In the direction normal to the film, free edge boundary conditions can be used. In such cases, it may be appropriate to also include surface fields and surface interactions. In this way, one can study phenomena such as wetting, interface localization-delocalization transitions, surface-induced ordering and disordering, etc.
The core of the program includes calculating the potential energies of particle configuration and particle collisions. The interactions and collisions between particles can be elastic or inelastic; they can be binary or collective. For computation, the interaction energy of a particle with its neighbors is carried out by refocusing a base cell on the particle. This particle only interacts with particles in this region. This is called the “minimal image convention” (MIC) [1].
Generally, a RANDOM generator of real random numbers ri belonging to the domain {0, 1} (or ri ∈ {0, 1} is available. This distribution law is uniform.
To have a real random number xi belonging to the domain {a, b} (or xi
To have a real random number xi belonging to the domain {a, b} (or xi ∈ {a,b}) according to a formula (or law) of nonuniform distribution f(x), a histogram technique is used. Let Nm be the number of intervals. If the mesh is regular (Figure 3):
Random number selection according to f (x) distribution.
We define:
We define the sequence:
and the sequence:
Hence each real random number ri belongs to the domain {0, 1} (where ri ∈ {0, 1}) (according to the uniform law); this number belongs to the domain {rxj-1, rxj}. It corresponds to a random value xran of the domain {xj-1, xj}; this number satisfies the formula (or the law) of nonuniform distribution f(x).
This technique can be generalized for a nonuniform distribution law f(x) with an irregular mesh Δxi, or with tabular data f(xi) with i = 1,…, m.
The technique can be generalized, too, for a discrete distribution law f(i) with i = 1,…, m.
In the literature, the reader can find simple algorithms for the choice of random numbers of some simple functions (Gaussian, etc.).
It is necessary to find some parameters allowing the control of the smooth course of the evolution of the system. We must look for the constants of movement. For example for an isolated system, we have the conservation of the total energy and the quantity of matter.
By using the numerical simulation, it is possible to calculate many spatiotemporal quantities F(r,t). These quantities can be positions, speeds, kinetic moments, particle energies, concentrations, transport coefficients, etc. It would then be possible to calculate all other quantities related to F(r,t).
For the calculation of the averages, one can note the quantities on the space, on the time or on both. The histogram methods can be used. Static or dynamic distribution functions and spatial or temporal correlation functions can be calculated. It should be noted that the SMC is much more adequate for static properties because of the probabilistic choice of configurations.
Any calculated function or parameter F(r,t) can be used for another application in another calculation program.
In the MCS model discussed extensively in this chapter, it’s more about collisions between particles. It’s particle-particle MCS or PP-MCS. In many problems of physics, the general idea is the same, but the applications and proposed models are numerous.
Other MCS models, named particle-in-cell MCS (PIC-MCS), are based on particle-cell interactions. In these last models, we also use a probabilistic choice of configurations and small variations in the state of the system (following the Metropolis algorithm); the interaction is between the particle with a cell, a mesh, or a drop. The parameters and variables of the cell, although local and instantaneous, are macroscopic. These parameters and variables can be thermodynamic, fluid, or electromagnetic. An example of the model based on PIC-MCS is described by Mattei et al. [31] for simulation of electromagnetic particle-in-cell collision in inductively coupled plasmas. Several works can be found in the literature on this same line of work. Other MCS models using particles may be considered. [32].
For statistical physics problem solving (such as thin film deposition problems), MCS models use experimental, numerical, or theoretical data from other methods and models. Models can be improved to hybrid models. In the hybrid models, connections between two modules can be realized. The first module is MCS; the second module is fluid, electromagnetic, or other. An example of a three-module hybrid model is presented by Mao and Bogaerts [33] to study gas mixtures in PECVD system. The three modules are MCS, fluid, and electromagnetic. The first module EM calculates the electromagnetic fields by solving Maxwell equations. These fields are used as inputs in the module MCS, where the electron density, electron temperature, electron energy distribution function, and electron impact reaction rates can be computed with a Monte Carlo procedure. Subsequently, the module fluid calculates densities and fluxes of the various plasma species (i.e., heavy particles and electrons) with continuity equations and the electrostatic field with Poisson’s equation. This electrostatic field is used as input again in the EM. This cycle is iterated until convergence. The schematic of the hybrid model is given in Figure 4.
Schematic of a hybrid model of three modules used to study gas mixtures in the PECVD [33].
To solve statistical physics problems with evolutions as a function of time, kinetic models of MCS (kMCS) are used. Using kMCS, Battaile and Srolovitz [17] described kinetic phenomena of the diffusive motion of a single interstitial atom in a close-packed metal crystal. The motion of the interstitial atom is usually limited to two types: vibration of the atom around the center of the interstitial hole in which it resides and hops to nearest-neighbor interstitial sites. The atom can hop into any of the nearest-neighbor interstitial sites; it executes a random walk. In an MC simulation of this diffusion process, the new position of the interstitial atom is chosen at random from a list of the adjacent interstitial sites.
Other CVD and PECVD works on MCS are presented in Ref.s [15, 34, 35, 36, 37, 38]. They show how MCS methods can study properties of gas mixtures and properties of the growth of thin films.
In this section, we present an example of PP-MCS of collisions and reactions in gas phase of SiH4/H2 mixture used in PECVD process. Some paragraphs have been treated in previous works [21, 24].
We use a MCS to study collisions and chemical reactions in gas phase of SiH4/H2 mixture used in the PECVD process. In this phase, important reactions have been identified that contribute to the production and the consumption of hydrogen (H), silylene (SiH2), and silyl (SiH3). The hydrogen consumption reactions SiH4 + H → SiH3 + H2 and SiH3 + H → SiH2 + H2 are found to play a central role in deciding the distribution of hydrogen [39].The plasma chemistry indicates that H atoms and SiH3 radicals play an important role in the a-Si:H deposition process [40]. Experimentally, it is generally accepted that SiH3 radicals dominate a-Si:H and μc-Si film growth from SiH4 plasmas in the PECVD; it is the key precursor of a-Si:H deposition [41]. The proposed MCS allowed to get the ratio SiH2/SiH3 and mean value of densities of species. It provides information on SiH4 dissociation and on the production of SiH3, H, SiH2, and Si2H6 and other important parameters.
The plasma in the PECVD reactor is weakly ionized. For our study, the mixture gas contains 22% of SiH4 and 78% of H2; the pressure is 100 mtorr, the temperature of the gas ranges from 373 to 723 K, the electron temperature is about 2.5 eV, and the electron density is 3. 108 cm−3. The process is considered to be stationary. We take into account electrons and eight neutral species (SiH4, SiH3, SiH2, H, H2, Si2H6, Si2H5, SiH). Reactions taken into account include seven electron-neutral and 14 neutral-neutral reactions. Table 1 shows the 21 reactions and rate constants Kreac. At low temperature, the neutrals interact occasionally with each other and move under the effect of thermal agitation; their velocity distribution function is Maxwell-Boltzmann distribution. Electrons have the mean velocity with kinetic energy Te.
Symbol | Reactions | Kreac (cm3/s) |
---|---|---|
R1 | SiH4 + e→SiH3 + H+e | k1 = 3 × 10−11 [42] |
R2 | SiH4 + e→SiH2 + 2H + e | K2 = 1.5 × 10−10 [42] |
R3 | SiH4 + e→SiH + H + H2 + e | K3 = 9.34 × 10−12 [42] |
R4 | SiH4 + e→SiH2 + H2 + e | K4 = 7.19 × 10−12 [42] |
R5 | H2 + e→2H + e | K5 = 4.49 × 10−12 [42] |
R6 | Si2H6 + e→SiH3 + SiH2 + H + e | K6 = 3.72 × 10−10 [42] |
R7 | Si2H6 + e→SiH4 + SiH2 +e | K7 = 1.1 × 1010× (1.(1./(1. + (0.63 × P)))) [43] |
R8 | SiH4 + H→SiH3 + H2 | K8 = 2.8 × 10−11 × exp.(−1250/T) [44] |
R9 | SiH4 + SiH2→Si2H6 | K9 = 1.1 × 1010 × (1.−(1./(1. + (0.63 × P)))) [43] |
R10 | SiH3 + SiH3→SiH4 + SiH2 | K10 = 0.45 × 1.5 × 10−10 [44] |
R11 | SiH4 + Si2H5→SiH3 + Si2H6 | K11 = 5 × 10−13 [42] |
R12 | SiH3 + H→SiH2 + H2 | K12 = 2 × 10−11 [44] |
R13 | SiH3 + Si2H6→SiH4 + Si2H5 | K13 = 4 × 10−10 × exp. (−2500/T) [44] |
R14 | SiH2 + H→SiH + H2 | k14 = 2 × 10−11 [44] |
R15 | Si2H6 + H→Si2H5 + H2 | K15 = 0.66 × 2.4 × 10−10 × exp. (−1250/T) [43] |
R16 | Si2H6 + H→SiH4 + SiH3 | K16 = 0.34 × 2.4 × 10−10 × exp. (−1250/T) [44] |
R17 | SiH + H2→SiH3 | K17 = 2 × 10−12 [43] |
R18 | SiH2 + SiH3→Si2H5 | K18 = 3.77 × 10−13 [43] |
R19 | SiH2 + H2→SiH4 | K19 = 3 × 10−12 × (1. + (1./1. + (0.03 × P))) [43] |
R20 | 2SiH3→Si2H6 | K20 = 0.1 × 1.5 × 10−10 [43] |
R21 | SiH4 + SiH→Si2H5 | K21 = (1.−(1./(1. + (0.33 × P)))) × (6.9 × 10−10) [43] |
List of gas phase reactions and corresponding rate constants [24].
Let
And chemical reaction for the production of A is as:
Rate production and consumption for any species A are taken as:
The MCS is based on binary collisions at the microscopic level. Elastic collisions are between all particles, and inelastic collisions (or effective collisions) are those that result in a chemical reaction. A chemical reaction needs a collision involving at least two particles (atoms, ions, electrons, or molecules). According to kinetic theory, gases consist of particles in random motion. These particles are uniformly distributed in a cell which has a parallelepiped form of sizes Lx, Ly, and Lz (Figure 5). These particles move in a straight line until they collide with other particles or the walls of their container. Dimensions and volume of Monte Carlo cell must take into consideration the mean free path of species.
Form of the simulation cell.
Let ni be the density of neutral spice i (i = 1,…, 8). The first particle i is randomly chosen according to a probability of neutral species Prsp,I (nonuniform discrete distribution) given by:
The chosen particle takes randomly three components of space in cell ri(xi, yi, zi) according to the normal distribution (nonuniform distribution). It takes also randomly three components of velocity vi (vxi, vyi, vzi) according to Maxwell-Boltzmann distribution.
Let ni and nj be the densities of species i and j in the gas and Vij the relative velocity between the two species i and j.
According to the kinetic theory of gases, we have for an incident particle i on a target particle j the average collision frequency νij as:
where <sij> is the cross section of the particle j.
The mean free path <λι> of species i is:
The time between two collisions τij is then:
For chemical effective reactions (inelastic collisions) between two reactive species i and j giving products i’ and j’, the rate constant reaction verifies [45]:
General rules of collision theory are applied:
The new velocities of the colliding particles are calculated using conservation of energy and momentum for elastic collisions.
Conservation of total energy as isolated system.
Movement of the center of mass and relative motion around the center of mass.
The reader can refer to some fundamental physics books that deal with general notions of collisions and corresponding parameters [45, 46, 47, 48].
The plasma in the PECVD reactor is weakly ionized. At low temperature, particles interact occasionally with each other and move under the effect of thermal agitation. In reality, only a small fraction of collisions are effective (result in a chemical reaction) [21].
In our MCS, after traveling a random walk given by a Gaussian distribution, the first chosen particle collides with a second particle (molecule, atom, radical, or electron). The last particle j is randomly chosen according to a (i-j) collision probability Prcol,j (nonuniform discrete distribution) given by:
where
The activation energy is given by:
where the pre-exponential factor is assumed to be the collision frequency factor and Kreac is the rate constant of the gas phase reaction.
The two colliding particles (e.g., the electron and SiH4 molecule) can interact by several reactions (R1, R2, R3, and R4 in Table 1); we choose randomly one of gas phase reactions occurring according to a, nonuniform discrete distribution reaction probability Prreac (i,j):
where
All chemical systems go naturally toward states of minimum Gibbs free energy [21, 24]. A chemical reaction tends to occur in the direction of lower Gibbs free energy. To determine the direction of the reaction that is taking place, we use the old and new values of Kreac and the equilibrium constant with reactants and product concentrations. Each set of binary collisions can be related or converted into time. As cited in section (a), Table 1 gives gas phase reactions and corresponding rate constants used in this MCS.
To continue the simulation, after the elastic collision, particle i takes new values of components velocity and new mean free path; mean free path is taken from a normal (nonuniform) distribution (Gaussian distribution). If the collision is inelastic, we have to take a new particle.
From Metropolis algorithm, the scheme of this MCS is as follows:
Choices of particle of spice i with random position, velocity, and mean free path; periodic boundary conditions are used to keep particles in the elementary cell.
Choices of random collision with a spice j.
Study of collision type (elastic, inelastic). If the collision is elastic the particle i move with a new velocity and mean free path, and we return to step (b). If the collision is inelastic particles i and j give new particles i’ and j’, according to Metropolis scheme, and we return to step (a) or (b). Periodic boundary conditions are used to keep particles in the elementary cell.
At each step, we can note the different statistics.
Once the species are selected for the simulation model, an estimate of species densities should be made. Following the model of interaction and collisions between particles (binary, collective, etc.), a first choice of the minimum number Ni of particles of each species is made. A first estimate of the sizes (Lx, Ly, Lz) of the elementary cell is made.
The study of the types of interaction potentials and the calculation of the approximate values of the force ranges, the kinetic energies, the internal energies, and the energies of activation make it possible to correct the minimal numbers Ni of particles and the sizes (Lx, Ly, Lz) of the elementary cell.
Let kp be the number of a species, kp = 1,…, 9. The minimal numbers Qnp(kp) and the sizes (Lx, Ly, Lz) have to be discussed for statistical calculations.
For numerical programming, according to the programming language used and according to the size (or the computational capacity) of the computer, it is necessary to find a judicious choice of the tables of integer or real values and which values would be useful to save all during simulation. Let Ncol,m be the maximum number of elastic collisions per particle, and let Ncycle be the number of cycles to average the simulation calculations.
For this MCS, the numerical chosen values are in Table 2.
Cell dimensions and steps for collisions | Number of species Kp | Initial number of particles in cell | ||
---|---|---|---|---|
Lx (m) | 4.68 10−6 | 1 | Qnp(SiH4) | Qnp1 |
Ly (m) | 4.68 10−6 | 2 | Qnp(SiH3) | 10 |
Lz (m) | 20.0 10−3 | 3 | Qnp(SiH2) | 10 |
4 | Qnp(H) | 10 | ||
Ncol,m | 500 | 5 | Qnp(H2) | Qnp5 |
Ncycle internal cycle | 2000 | 6 | Qnp(Si2H6) | 10 |
Ncycle external cycle | 200,000 | 7 | Qnp(SiH) | 10 |
8 | Qnp(Si2H5) | 10 | ||
9 | Qnp(e) | Qnp9 |
Used quantities and parameters in calculations for the gas temperature Tg = 520 K.
For radicals (e.g., SiH3), particle numbers Qnp(k) are very small; we take Qnp(k) = 10. These numbers cannot take value 1 or 0, even if a species k is in trace form in the gas. The value 0 for a species k means that any other species k’ does not make a collision with the species k; and the value 1 means that we have no collisions between particles of the same species in the cell.
Qnp1, Qnp5, and Qnp9 are calculated from the volume of cell, the pressure, the temperature, and the total number of particles in the cell (Qnp1 = 0.81187824 * 109; Qnp5 = 0.20296956 * 109; Qnp9 = 131).
As we have chosen a stationary regime, we must reach the values and properties at equilibrium. The results of the simulation show this trend. In MCS, averaged values, distribution functions, autocorrelation functions, and correlation functions can be calculated. To ensure rapid convergence of calculations, it would be useful to look for statistically symmetric (or stationary or unsteady) parameters [26, 50].
As an example for our MCS calculation, we have:
The number of Si2H6, SiH, and Si2H5 particles reaching the surface is negligible.
Let Ns,i and Ns, H2 be the densities of a species i and H2 reaching the surface. The ratios Ns,i/Ns, H2 are too small (Table 3).
Let Ns,i be the density of a species i reaching the surface and Nv,i the density of same species i in volume. The ratios Ns,i/Nv,i are too small (Table 4); the surface effect is negligible.
The reactions begin with the dissociation (consumption) of H2 and SiH4 by R5, R1, and R2 reactions.
The production of SiH3 is done by R8, and then there is production of SiH2 by R12.
The reaction R2: SiH4 + e → SiH2 + 2H + e plays the central role in SiH4 dissociation by electron impact [24]. This result is compatible with [39].
The second important chemical reaction in the SiH4 dissociation is R1: SiH4 + e → SiH3 + H + e [24]. This result is compatible with that of Perkins et al. [51] and that of Doyle et al. [52].
Type | H2 | SiH4 | H | SiH3 | SiH2 |
---|---|---|---|---|---|
Ns,i/Ns, H2 | 1 | 0.23 | 1.67 10−4 | 8.60 10−5 | 9.86 10−6 |
Ratios Ns,i/Ns, H2 of particles reaching the surface compared to H2.
Type | SiH4 | SiH3 | SiH2 |
---|---|---|---|
v, j | 6.695 10−6 | 7.965 10−6 | 775 10−6 |
Ratios Ns,i/Nv,i of particles reaching the surface compared to volume.
MCS is a widely used method in statistical physics to study thermodynamic, structural, or phase properties. It is based on random and probabilistic processes. The purpose of this chapter is to present the technique for general use in physics for the study of thin film deposition problems. The technique can be generalized to other fields of science: biology, economics, transportation, and social sciences.
We started by presenting general rules for numerical simulation methods. Metropolis algorithm has been considered as the basic algorithm. After, we presented the different steps for the realization of a MCS code. We chose the particle-particle model MCS (PP-MCS) to explain the different steps and procedures to be applied in the deposition of thin layers by PECVD processes. We have shown that this technique can be generalized to the particle-in-cell MCS (PIC-MCS) case or kinetic MCS (kMCS), as it can be joined with other modules to give hybrid models. It is important to know how to choose random configurations from the laws or probability distributions in the system.
A numerical application is presented for collisions in a SiH4/H2 gas mixture in the PECVD process. A preliminary work of determination of the chemical reactions between molecules and radicals is made. A choice of the simulation cell is made, and the definition of the probabilities of the collisions between peers is made. The Metropolis algorithm makes it possible to follow the various elastic and inelastic collisions; it also makes it possible to make the statistics of the interactions with the surface. The results are compatible with [39, 51, 52].
Other questions may be asked to account for molecular ions, surface and volume kinetics, or thin film formation. The techniques and different models of the MCS (PP-MCS, MCS-PIC, kMCS) allow taking care of these questions.
The interconnection of the MCS with other models (MDS, hybrid model, fluid model, electromagnetic model, etc.) would allow answering more questions. The methods can be applied to other specialties than the physical sciences.
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