**3. Films fabricated by monodispersed nanoparticle beams**

#### **3.1 Monodispersed nanoparticle by size classification**

In general, the size distribution of nanoparticles *f*x(*x*) growing in vapor phase is known to be described as the log-geometric distribution as follow [29].

$$f\_{X}\left(\mathbf{x}\right) = \frac{1}{\varkappa\sqrt{2\pi}\ln\sigma\_{\boldsymbol{\xi}}}\exp\left[-\frac{1}{2}\left(\frac{\ln\left(\varkappa/m\_{\boldsymbol{\xi}}\right)}{\ln\sigma\_{\boldsymbol{\xi}}}\right)^{2}\right] \quad \mathbf{x} \ge \mathbf{0} \tag{6}$$

**109**

**Figure 3.**

*Nanoparticle Formation and Deposition by Pulsed Laser Ablation*

While sizes of nanoparticles have a certain distribution, the physical property of the nanoparticle may vary dramatically depending on its size. Thus, the sizes of nanoparticles should be in uniform when we need deal with them as macroscopic materials like nanoparticle films. Hence, in the early 2000s, some kinds of device to make the nanoparticle size in uniform has been actively

In those times, the size selection of aerosol particles is generally done in an electric field by deflecting charged particles generated from a source of the outbreaks such as combustion gas in Diesel engine. Afterward, the Differential Mobility Analyzer (DMA), as shown in **Figure 3** [33], which has been used to measure aerosol particles in the atmosphere, has been improved to the desired extent for applying on nanoparticles [34–36]. It is able to measure the particle size distribution by using the natural law in which a flying charged nanoparticle would land on different locations according to the balance between the electric mobility and fluid resistance, being deflected by an electric field [37]. Camata et al. [38] used a DMA system to estimate the geometric standard deviation of silicon nanoparticles with an average particle diameter of 2.8 nm and got a number of the geometric standard deviation between 1.2 ~ 1.3. Suzuki et al. [39] used DMA system on silicon nanoparticles and obtained a result of an average particle diameter of 2.8 nm and a geometric standard deviation of 1.2. A consistent system from a source of nanoparticles to its sampler is shown in **Figure 4** [34]. The nanoparticles generated by laser ablation pass through the gas phase annealing system, where the particle morphologies are controlled, and move to DMA to be classified by its size, and finally reach the

*Illustration of instrument capable of measuring size distributions of aerosol particles on basis of DMA [33].*

*DOI: http://dx.doi.org/10.5772/intechopen.95299*

particle sampler and the measurement systems.

invented and designed.

Here *m*g is the geometric mean and *σ*g is the geometric standard deviation. A model has been proposed to explain the log-geometric distribution by assuming that the growth formula of a nanoparticle follows a certain Equation [30, 31]. It has also been experimentally confirmed that the size distribution of nanoparticles generated by laser ablation is log-geometric [32].

*Nanoparticle Formation and Deposition by Pulsed Laser Ablation DOI: http://dx.doi.org/10.5772/intechopen.95299*

*Practical Applications of Laser Ablation*

the following issues should be considered.

representative time of the system.

temperature.

non-equilibrium.

nanoparticle is kept or not. If we are assuming that the velocity of vapor atoms is represented in the equilibrium Maxwell distribution, we can estimate the approxi-

> *cr kT p m* = ρ

In this equation, *ρ*c stands for the internal density of the nanoparticle, while *r* the radius of the nanoparticle, *p* the vapor pressure, and *m* the mass of the vapor atoms. In order to obtain an exact solution, Gillespie [27] analyzed the process of nanoparticle formation as a random walk problem. However, no matter how rigorous the probabilistic analysis is, it is still based on classical nucleation theory. To address the problem related to the formation of nanometer-sized particle in non-equilibrium,

a.The nucleation rate equation based on steady-state theory is valid only when the formation time of a critical nucleus is sufficiently short compared to the

b.The classical theory is an available model at the range of relatively low supersaturation. In the case of high supersaturation, the internal degrees of freedom of the nanoparticle must be taken into an account, because differences are created among translational, rotational and vibrational

c.In spite of small systems, macroscopic physical properties and coefficients such as surface free energy, evaporation enthalpy, and condensation coefficient besides macroscopic concepts such as the Thomson–Gibbs formula are used. We have to correct these values and formula in conformity with the extent of

In recent years, with the development of computers, molecular dynamics or Direct Simulation Monte Carlo analyses which take into consideration the internal degrees of freedom of nanoparticles have been made [28], through which the Gibbs free energy and nanoparticle concentrations are elucidated under more realistic conditions.

In general, the size distribution of nanoparticles *f*x(*x*) growing in vapor phase is

*g g*

=− ≥

*x m*

exp 0

σ

2

(6)

**3. Films fabricated by monodispersed nanoparticle beams**

known to be described as the log-geometric distribution as follow [29].

( ) ( *g* ) *X*

*f x x*

1 1 ln /

2 ln

Here *m*g is the geometric mean and *σ*g is the geometric standard deviation. A model has been proposed to explain the log-geometric distribution by assuming that the growth formula of a nanoparticle follows a certain Equation [30, 31]. It has also been experimentally confirmed that the size distribution of nanoparticles

**3.1 Monodispersed nanoparticle by size classification**

*x*

generated by laser ablation is log-geometric [32].

2 ln π σ

 π2

(5)

mate nanoparticle formation time *τ* with the following equation.

τ

**108**

While sizes of nanoparticles have a certain distribution, the physical property of the nanoparticle may vary dramatically depending on its size. Thus, the sizes of nanoparticles should be in uniform when we need deal with them as macroscopic materials like nanoparticle films. Hence, in the early 2000s, some kinds of device to make the nanoparticle size in uniform has been actively invented and designed.

In those times, the size selection of aerosol particles is generally done in an electric field by deflecting charged particles generated from a source of the outbreaks such as combustion gas in Diesel engine. Afterward, the Differential Mobility Analyzer (DMA), as shown in **Figure 3** [33], which has been used to measure aerosol particles in the atmosphere, has been improved to the desired extent for applying on nanoparticles [34–36]. It is able to measure the particle size distribution by using the natural law in which a flying charged nanoparticle would land on different locations according to the balance between the electric mobility and fluid resistance, being deflected by an electric field [37]. Camata et al. [38] used a DMA system to estimate the geometric standard deviation of silicon nanoparticles with an average particle diameter of 2.8 nm and got a number of the geometric standard deviation between 1.2 ~ 1.3. Suzuki et al. [39] used DMA system on silicon nanoparticles and obtained a result of an average particle diameter of 2.8 nm and a geometric standard deviation of 1.2. A consistent system from a source of nanoparticles to its sampler is shown in **Figure 4** [34]. The nanoparticles generated by laser ablation pass through the gas phase annealing system, where the particle morphologies are controlled, and move to DMA to be classified by its size, and finally reach the particle sampler and the measurement systems.

#### **Figure 4.**

*Schematic of nanoparticle synthesis process using laser ablation, which is composed of (a) particle generator, (b) gas phase annealing, (c) particle classifying, and (d) particle measurement and correction [34].*

Although the above methods require nanoparticles being charged in some way to select the size, the flank attack method proposed by Wu et al. [40] can be used for neutral nanoparticles. In this method, the nanoparticle beam is intersected with the atomic beams of inert gas such as argon or neon, and the nanoparticle can be sorted out in accordance with the size following the law in which the smaller the nanoparticle size, the greater the deflection angle of the beam.

## **3.2 The direct fabrication of monodisperse nanoparticles**

In the previous section, the relationship between the growth rate and the size distribution of nanoparticles has been discussed. By understanding the growth process of nanoparticles, it is possible to control their size distribution. Laser Vaporization with Controlled Condensation (LVCC), as shown in **Figure 5** [41], is a method to control the average size of the nanoparticles by adjusting the conditions about nucleation and growth. By raising the temperature of the lower wall in ablation chamber with a heater and cooling down the upper wall with liquid nitrogen, the temperature gradient would cause the convection of ambient gas. When the vapor created by laser ablation drifts upward by the convection, the vapor becomes supersaturated near the upper cold wall, which results in nucleation

#### **Figure 5.**

*(A) Experimental set-up for the synthesis of nanoparticles using the LVCC method. (B) Experimental setup for the LVCC method coupled with a DMA [41].*

**111**

**Figure 6.**

*Nanoparticle Formation and Deposition by Pulsed Laser Ablation*

and condensation followed by nanoparticle forming. It is found that the higher the supersaturation is, that is, the larger the temperature gradient is, the smaller the average size of the nanoparticles becomes. Therefore, by adjusting the temperature gradient proficiently, it is possible to control the nanoparticle size. Furthermore, by combining LVCC and DMA, to control the average diameter and size distribution of

In order to determine the size distribution by directly adjusting the growth process of the nanoparticles, it is necessary to control not only statically the degree of supersaturation but also dynamically its temporal variation of it. That is, it is necessary to rapidly increase the supersaturation level to complete the nucleation within a short period of time and to inhibit the subsequent nanoparticle growth in

As described in Section 2.3, a shock wave is generated in front of the plume by laser ablation. It is possible to use the shock wave to rapidly change the state quantity in the plume and increase the supersaturation at a fast rate. One example is the laser ablation process done in a closed space such as an ellipsoidal cell, in which the collision between the reflected shock wave and the plume front triggers the formation of nanoparticles instantly in a small area [43, 44]. The equipment fabricating monodispersed nanoparticles by using this phenomenon is called Spatiotemporal Confined Nanoparticle Source (SCCS), which is illustrated in **Figure 6** [45], as the

The possibility of developing new functional materials with nanoparticle films

has been pointed out for a long time [46], and many attempts have been made mainly to create light-emitting devices using the visible light emission from silicon

fabrication process is restrained in the confined space and time.

**3.3 Application of self-ordered nanoparticle films on substrate**

*Schematic view of new laser ablation-type silicon cluster beam system named SCCS [45].*

*DOI: http://dx.doi.org/10.5772/intechopen.95299*

nanoparticles would be possible.

some ways [42].

## *Nanoparticle Formation and Deposition by Pulsed Laser Ablation DOI: http://dx.doi.org/10.5772/intechopen.95299*

*Practical Applications of Laser Ablation*

**Figure 4.**

Although the above methods require nanoparticles being charged in some way to select the size, the flank attack method proposed by Wu et al. [40] can be used for neutral nanoparticles. In this method, the nanoparticle beam is intersected with the atomic beams of inert gas such as argon or neon, and the nanoparticle can be sorted out in accordance with the size following the law in which the smaller the nanopar-

*Schematic of nanoparticle synthesis process using laser ablation, which is composed of (a) particle generator, (b) gas phase annealing, (c) particle classifying, and (d) particle measurement and correction [34].*

In the previous section, the relationship between the growth rate and the size distribution of nanoparticles has been discussed. By understanding the growth process of nanoparticles, it is possible to control their size distribution. Laser Vaporization with Controlled Condensation (LVCC), as shown in **Figure 5** [41], is a method to control the average size of the nanoparticles by adjusting the conditions about nucleation and growth. By raising the temperature of the lower wall in ablation chamber with a heater and cooling down the upper wall with liquid nitrogen, the temperature gradient would cause the convection of ambient gas. When the vapor created by laser ablation drifts upward by the convection, the vapor becomes supersaturated near the upper cold wall, which results in nucleation

*(A) Experimental set-up for the synthesis of nanoparticles using the LVCC method. (B) Experimental setup*

ticle size, the greater the deflection angle of the beam.

**3.2 The direct fabrication of monodisperse nanoparticles**

**110**

**Figure 5.**

*for the LVCC method coupled with a DMA [41].*

and condensation followed by nanoparticle forming. It is found that the higher the supersaturation is, that is, the larger the temperature gradient is, the smaller the average size of the nanoparticles becomes. Therefore, by adjusting the temperature gradient proficiently, it is possible to control the nanoparticle size. Furthermore, by combining LVCC and DMA, to control the average diameter and size distribution of nanoparticles would be possible.

In order to determine the size distribution by directly adjusting the growth process of the nanoparticles, it is necessary to control not only statically the degree of supersaturation but also dynamically its temporal variation of it. That is, it is necessary to rapidly increase the supersaturation level to complete the nucleation within a short period of time and to inhibit the subsequent nanoparticle growth in some ways [42].

As described in Section 2.3, a shock wave is generated in front of the plume by laser ablation. It is possible to use the shock wave to rapidly change the state quantity in the plume and increase the supersaturation at a fast rate. One example is the laser ablation process done in a closed space such as an ellipsoidal cell, in which the collision between the reflected shock wave and the plume front triggers the formation of nanoparticles instantly in a small area [43, 44]. The equipment fabricating monodispersed nanoparticles by using this phenomenon is called Spatiotemporal Confined Nanoparticle Source (SCCS), which is illustrated in **Figure 6** [45], as the fabrication process is restrained in the confined space and time.


*4.3.2. Visualization function of statistical information*

As in **Figure 7**, the analytic view can visualize the summary of bibliometric information of the nodes contained in the view. There are several widgets, such as for citation (Impact Factor, SJR, and CiteScore) metrics, publications by year, citations by year, and publications by each country. Moreover, the users can select the nodes in a rectangle area and see the statistical information of the selected nodes. The upper part of the publication by country shows an article count (AC) (https://www.natureindex.com/faq). The AC means the country-level participation in a study, where a country is counted if one or more authors of the article are from the country. For example, if countries of three authors' affiliations in a article are A, B, and B, A is counted as one and B is also counted as one. In contrast, the lower part of the publication by country shows a fractional count (FC) that means the contribution of each country. In the above example, A becomes 1/3, B becomes 2/3.

### *4.3.3. Summarization function of feature phrases*

As in **Figure 8**, the feature phrases of the selected nodes can be summarized in word clouds. At most 10 feature phrases of each node are extracted based on the BM25 method in advance. Then, if the users select the multiple nodes, the feature phrases with higher frequencies are displayed larger and placed closer to the center of the word cloud. This function is useful for understanding specific themes of the selected nodes in a cluster.

**Figure 7.** Statistical information.

**4.3. Analytical functions provided on the map**

**Figure 6.** Comparison of graph layout algorithms.

186 Scientometrics

*4.3.1. Abstract/description translation function*

In addition to the functions described in Section 4.1, the Mapping Science provides the following analytical functions: (1) translation of article abstracts and project descriptions, (2) visualization of statistical information, (3) summarization of feature phrases, (4) querying and exporting using SPARQL, (5) change of layout algorithms, and (6) generation of customized analytic views.

In the analytic views, users can see the detailed information, such as titles, article abstracts/ project descriptions, authors/project members, affiliations, and publication year/proposed

the node is highlighted and the viewpoint is automatically moved to the node. Moreover, the users can store their own SPARQL queries as macros. Therefore, users who are not familiar

Mapping Science Based on Research Content Similarity http://dx.doi.org/10.5772/intechopen.77067 189

In addition, since we received requests for downloading the information displayed on the map, the information of the selected nodes and all nodes in a cluster can be exported in comma-separated values (CSV) format. The result of SPARQL queries can be also exported in CSV format.

As described in the previous section, the layout of the analytic view was calculated by the OpenOrd (edge-cutting value: 0.91). In addition to that, the analytic views can be redrawn by the OpenOrd (edge-cutting value: 0.94 or 0.88), LGL, Fruchterman-Raingold, or Kamada-Kawai [26]. When the users select a layout, the layout algorithm is executed in the background, the resulting layout information is stored and the view is redrawn. If the layout information is stored in advance, the layout is redrawn immediately. The layout calculation time depends on the number of nodes, and the average time is a few seconds to a few minutes.

The analytic views were composed by the info map algorithm, but the users can create the customized Analytic views by keyword search. When the users enter keywords into the widget in the portfolio view, the nodes are extracted by the full-text search for all nodes in five research areas, and then the layout is calculated by the OpenOrd based on the cosine similarities of the extracted nodes. For example, an analytic view for studies related to neural networks and artificial intelligence across multiple research areas can be created by keywords such as "Artificial Intelligence [AND] Neural Network." This function could help find interdisciplinary studies. The calculation time depends on the number of nodes, and the average time is a few seconds to a few minutes. The information on the customized analytic views is stored in the background; the same view is immediately displayed for the second time. The customized analytic view can provide the same analytical functions, such as keyword search, visualization of statistical

information, visualization of the cumulative changes by year, and layout change.

In this map, we try to understand the formation processes of several research areas through chronological changes of network structure. This section describes two cases for the Internet

In **Figure 10** shows the analytic views for an IoT area from 2012 to 2016, which includes 574 nodes as of 2016. The last view is the analytic view in 2016 displaying >0.6 cosine similarities

In 2012, four islands (places, at which nodes are densely located) mainly for IoT frameworks and networks and for IoT system and security are barely found (labels of each island have

**5. Case study for the formation process of research areas**

been extracted by the summarization function of feature phrases).

of Things (IoT) and Brain-Computer Interface (BCI).

as edges.

with SPARQL can simply call the macros and obtain the query results.

*4.3.5. Layout change function*

*4.3.6. Custom analytic view function*

**Figure 8.** Feature phrases in the selected nodes.

#### *4.3.4. Query function and export function*

The background data in the Mapping Science have been converted to RDF data and stored in a graph database. Therefore, the analytic views provide a high-level search using a formal query language, SPARQL, as in **Figure 9**. For example, the users can search for articles, which have >0.8 similarities with articles cited 100+ times from journals with >10 impact factor (such articles might be obscure but important). When the users click a node ID in the result table,

**Figure 9.** SPARQL search widget.

the node is highlighted and the viewpoint is automatically moved to the node. Moreover, the users can store their own SPARQL queries as macros. Therefore, users who are not familiar with SPARQL can simply call the macros and obtain the query results.

In addition, since we received requests for downloading the information displayed on the map, the information of the selected nodes and all nodes in a cluster can be exported in comma-separated values (CSV) format. The result of SPARQL queries can be also exported in CSV format.


**5. Pathophysiology**

6 Neonatal Bacterial Infection

staphylococcus.

neonates to die at home.

**Figure 4.** K. Nemsadze, Neonatology, 2010 [1]

sepsis

**4. Etiology and epidemiology**

**Etiological Structure of Sepsis in Developed Countries**

**Figure 3.** J. Garcia-Prats et al.,SeminPediatrInfect Dis, 2000;11:4 [6]

**Etiological Structure of Sepsis in Tbilisi Children's Central Hospital**

Consequently, the main causative agents of neonatal sepsis are bacteria such as

Klebsiella, acinetobacter and staphylococcus aureus can be the cause of early as well as late

Late sepsis is usually a nosocomial disease although is some cases it may be connected with vertical infection. Pseudomonas, salmonella and serratia can often cause late sepsis. Many factors impact the etiological structure, including the quality of life, cultural traditions, practice of antibiotic therapy and the possibility of distorted results caused by many In the presence of sepsis, the response of infection released anti-inflammatory mediators can't localize anti-inflammatory process. Generalized infection is formed as sepsis. The cause of sepsis (SIRS) is multi-factorial: Activation of anti-inflammatory mediators; complement ischemia of tissues; cytopathology; changes in apoptosis rate. Cellular damage with discharge of anti-inflammators (IL1, IL6 TNF- Tumor Necrosis Factor) and antiinflammatory mediators increase the probability of developing multi-organ failure. The cardiovascular system, pulmonary system, gastro-intestinal tract, kidneys and neurological system are most frequently damaged. Given mediators stimulate production of various proteins called as reagents of acute phase. Any kinds of inflammation stimulus, including infection, trauma and ischemia causes marginal extravasations and activation of granulocytes and monocytes with the simultaneous release of anti-inflammatory cytokines including interleukins IL1, IL6 TNF (Tumor Necrosis Factor) [7]

### **Sepsis is caused by Systemic Inflammatory Response (SIR)**

It is widely known that sepsis educes as a result of Systemic Inflammatory Response (SIR). In the process of opsonization and macrophage phagocytosis pathogens cause the formation of various anti-inflammatory mediators (cytokines) which damage vessel endothelium the result of which is the release of tissue factors. The coagulation system is activated and fibrinolysis inhibitor activity is increased. [8,9]

### **Anti-inflammatory Mediators are Reagents of the Acute Phase**

After the patient is stabilized, normalization and a secondary increase in the levels of CBC indicate the sepsis complication (*subdural empyema and bacterial meningitis* [10]. *S*epsis may change the metabolism of neonates. The change in metabolism may be described in two phases

*Ebb Phase Flow Phase*

#### **EBB Phase**

The initial EBB phase lasts 1-3 days. In this phase the neonate is at the stage of compensation while metabolism is slowed.

#### **EBB Phase Consists of Some Clinical Symptoms:**

#### **Flow Phase**

Flow Phase follows the initial Ebb Phase. In this phase the organism goes into a hyperactive state, which is particularly conditioned by a hyper inflammatory reaction. In many cases flow phase leads to patient mortality.