**4. Viability assay in normal cells**

Attempts are pursued to develop drugs that are nontoxic to normal cells; meanwhile toxic to cancer cells can be considered as good anticancer agent.

### **4.1 Isolation of lymphocytes from whole blood**

In 1968, Boyum [21] described methods for the isolation of mononuclear cells from circulating blood and bone marrow. The solution contains Ficoll and sodium diatrizoate, adjusted to a density of 1.077 ± 0.001. This medium facilitates rapid recovery of viable lymphocytes from small volumes of blood on centrifugation. It is usually employed as the initial isolation step prior to enumeration of T, B, and null lymphocytes. In brief, 3 ml of blood will be taken in heparinized test tube. About 5 ml of PBS will be added and mixed well by inversion. About 3 ml of Ficoll Hypaque solution was added in a conical centrifuge tube. Carefully layered 8 ml of blood-PBS mixture on to the Ficoll Hypaque, keeping the tube in a slanting position. Centrifuged at 2000 rpm for 30 min. The opaque interface containing mononuclear cells was aspirated and transferred into a clean conical centrifuge tube with a Pasteur pipette and discard the upper layer. About 10 ml of PBS solution was added and mixed by inversion. Centrifuged at 1500 rpm for 10 min and the supernatant was discarded. Resuspend the pellet with 5 ml PBS and centrifuge at 1500 rpm for 10 min. Repeat the step thrice and resuspend the lymphocyte pellet in 500 μl PBS.

#### **4.2 Lymphocyte viability assay**

In vitro experiments with compounds can be tested in lymphocyte using lymphocyte viability assay. The lymphocytes were aspirated from the gradient plasma interfaces and washed twice in PBS, and then the final cell pellets will be resuspended in RPMI-1640 medium containing 10% FCS and 100 μl streptomycin, and 100 μl fungicide can be added to avoid contamination; pH 7.4 is ideal. Cells can be harvested, counted, and seeded (5 × 103 cells/well in 100 μl) in 96 well titer plates (Axygen). PBS will be added to the outer wells (200 μl/well). After 24 h of incubation at 37°C in 5% CO2 to allow cell attachment, the media will be

removed. Cells will be treated with varying concentration of compounds diluted with medium. The plates can be incubated for 72 h. After incubation, the media will be removed without disturbing the cells, and to each well, 100 μl of 5 mg/ml stock solution of MTT will be added, and the plates can be incubated for 2 h in the dark at 37°C in a CO2 incubator. About 100 μl of lysis buffer will be added to each well, and the plates can be further incubated for 4 h in the dark in a CO2 incubator, and absorbance can be read using ELISA plate. Three replicates will set up for each concentration.


**3. Results**

#### *3.1. SSA-Denoised Waveform Series*

We experimented with the 20-Hz waveforms data of Jason-1 cycle 340 pass 153 from 13.80◦ N to 21.75◦ N (pass with purple color in Figure 1, hereafter called c340-p153 track). This track had a total of 3118 waveforms and contained the altimeter data moving from both land to ocean and ocean to land. The deepest part of the waters that this track moved was about 1730 m, and the shallowest part was about 7 m. The c340-p153 track had su fficient data volume and a certain representativeness. The waveforms of the c340-p153 track were connected end-to-end to construct a waveform series with length of *N* = 3118 × 104. Then, this waveform series was denoised with SSA.

The main purpose of the waveform retracker is to find the position of the leading-edge component with respect to the fixed nominal tracking point [6,8]. Therefore, it is critical to retain the slope information of the leading-edge component when performing noise reduction of the waveform series with SSA. It was assumed that a component with a ratio lower than 0.01% was considered as the noise information. That is, the ratio of 0.01% was used as a boundary value to distinguish the main waveform information and the noise information. The components with a ratio higher than 0.01% were classified as the main waveform information, and those lower than 0.01% were classified as the noise information. This means we can reconstruct the main waveform information from the components with a ratio higher than 0.01%.

Figure 3 presents the contribution ratio of the eigenvalues {λ*i*} *M i* corresponding to the raw waveform series. As can be seen from Figure 3, except for the ratio of the first eigenvalue reaching about 70.5%, the others were less than 10%, and the ratio of each eigenvalue starting from the 49th eigenvalue was within 0.01%. The ratio of the first 48 eigenvalues was more than 99%. Therefore, it was preferable to choose *l* = 48 in reconstruction to obtain the SSA-denoised waveform series. Thus, the main waveform information can be well preserved.

**Figure 3.** Ratio of the eigenvalues.

Figure 4a is the beginning part of the raw waveform series and the SSA-denoised waveform series, the latter corresponding to the main waveform information. Figure 4b is the beginning part of the residual series (i.e., noise information of the waveform series), defined as the difference between the raw and SSA-denoised waveform series. As can be seen from Figure 4, by comparison, the raw waveforms and the SSA-denoised waveforms mainly differed in the thermal noise component and amplitude, while the slope information of leading-edge component was well retained. This indicates that SSA allowed a noise reduction on Jason-1 waveforms. The larger amplitude of the residual series in Figure 4b was mainly concentrated on the junction of different waveforms. The reason is that the trailing edge component of the waveform was affected by the low-frequency signal of the thermal noise component of the following waveform. However, the leading edge was less affected.

**Figure 4.** Beginning part of the rawwaveform series, the SSA-denoisedwaveform series, and corresponding residual series defined as the difference between the raw and SSA-denoised waveform series. (**a**) Raw waveform series and SSA-denoised waveform series; (**b**) residual series.

#### *3.2. Comparison of Retracked SSHs*

The 20-Hz waveforms data of the c340-p153 track were denoised using the SSA algorithm described earlier to obtain the SSA-denoised waveforms. Both the raw and SSA-denoised waveforms were retracked by the 50% threshold retracker, respectively. Figure 5 compares the raw SSH, the retracked SSHs from the 50% threshold, and SSA + 50% threshold retracker, respectively, with referenced geoidal heights calculated by the EGM2008 model. Here, the SSA + 50% threshold retracker refers to using the 50% threshold retracker for the SSA-denoised waveforms. As can be seen from Figure 5, the deviation of SSH from geoidal height increased as the tracks approached the land. In the coastal region, the retracked SSH profile was smoother than the raw SSH profile, and the former was more similar to the geoidal height than the latter.

The success of the retracker in producing a better SSH estimate value was identified by computing the standard deviations of the difference between SSHs and geoid heights, and the improvement percentage (IMP). The calculation formula of IMP is as follows [18].

$$\text{IMPP} = \frac{\delta\_{\text{raw}} - \delta\_{\text{retracked}}}{\delta\_{\text{raw}}} \times 100\% \tag{8}$$

where, δraw and δretracked are the standard deviations of the differences between raw SSHs and geoidal heights, and retracked SSHs and geoidal heights, respectively. The geoidal heights are calculated by EGM2008 model [24] in the present study.

**Figure 5.** Comparison of raw SSH, retracked SSH, and referenced geoid height (calculated by EGM2008 model) along the track of Jason-1 cycle340 pass153.

The IMP was compared in three cases: (1) The entire c340-p153 track; (2) part of the c340-p153 track from land to ocean within 10 km from the coastline; and (3) part of the c340-p153 track from ocean to land within 10 km from the coastline. Table 2 shows the standard deviations (STDs) of the differences between raw SSH, retracked SSH, and geoidal heights and the IMP in these three cases. In Table 2, the STDs of differences between raw SSH and geoidal heights were smaller than that between the retracked SSH and geoidal heights. This means that waveform retracking iswas successful in improving the quality of altimeter data, especially in the coastal region. Whether in the open ocean or coastal region, the IMP values from the SSA + 50% threshold retracker were larger than those from the 50% threshold retracker. This indicates that SSA successfully improved the retracked SSHs estimate both in the open ocean and coastal region.

**Table 2.** Standard deviations of differences between raw SSH, retracked SSH, and geoidal heights and improvement percentage (IMP).

#### *3.3. Comparison of Retracked SSHs Discrepancies at Crossover Points*

SSA was used to reduce the noise information contained in the 20-Hz waveforms data of Jason-1 GM from cycle 500 to cycle 537 in the South China Sea to obtain the SSA-denoised waveforms, which was the same process as using SSA to denoise the waveforms of the c340-p153 track. Then, the 50% threshold retracker was used to retrack the raw and SSA-denoised waveforms.

There was an SSH difference at the crossover point between the ascending and descending tracks, e.g., Jason-1 GM ground tracks in Figure 1b. The crossover differences can be used to evaluate the quality of the retracked SSHs [10]. In order to compare the retracked SSHs from the 50% threshold retracker and that from the SSA + 50% threshold retracker quantitatively, the mean, STD, and root mean square (RMS) of the crossover differences were calculated (Table 3). The statistical results were categorized into two classes, according to the nearest distances from the crossovers to land: Distances less than 10 km and greater than 10 km. The number in the brackets in column one indicates the number of crossovers in these two classes.

**Table 3.** Statistical results of crossover differences between the retracked SSHs from the 50% threshold and SSA + 50% threshold retracker.

As can be seen from Table 3, the accuracy of the retracked SSHs in the coastal region (distances less than 10 km from the land) was lower than that in the open ocean (distances more than 10 km from the land). Regardless of whether the distance was more than 10 km or less than 10 km, both the STDs and RMSs from the SSA + 50% threshold retracker were smaller than those from the 50% threshold retracker, which shows that the retracked result of the SSA + 50% threshold retracker was better than that of the 50% threshold retracker. This indicates SSA can effectively improve the precision of the retracked SSHs, whether in the open ocean or coastal region. This conclusion is consistent with Section 3.2.

#### **4. MSSH Model and Validation**

#### *4.1. MSSH Model from SSA-Denoised Waveform Retracked SSHs*

All the 20-Hz waveforms data from Jason-1 SGDR products, including ERM1, ERM2, and GM in Table 1, were denoised with the SSA described earlier to obtain SSA-denoised waveforms, and the 50% threshold retracker was performed for these SSA-denoised waveforms to obtain the 20-Hz retracked SSHs. These 20-Hz retracked SSHs were compressed by linear regression to obtain 1-Hz retracked SSHs. In this linear regression, the SSHs over three-times larger than the STD were eliminated by an iterative outlier detection, and data with less than 10 points were also not considered. Then, these 1-Hz SSHs were used to establish an MSSH model over the South China Sea with grid of 2 × 2 (shows in Figure 6). The process of establishing MSSH model mainly includes data preprocessing, the removal of the temporal oceanic variability, crossover adjustment, and gridding, which has been detailed by Yuan et al. [25].


**1. Introduction**

In Computational Fluid Dynamics (CFD), many simplifications and assumptions are made to the mathematical models to make them computationally affordable. The rapid development and progress in the field of computer processors and enhancement of computer memory has enabled scientists and engineers to review and revisit some of the assumptions. This has helped to improve and enhance the predictive capabilities of computer modeling. This approach has also been implemented in engine simulation codes and tools. The objective of this study and research is to identify the reactions that govern the chemical kinetics of fuel oxidization and move from multi-step global reactions to a reduced number of elementary reactions that can better model the combustion and engine performance parameters [1].

For the development of future fuels and the optimization of automotive engines, computer modeling and simulation has proved itself as an inseparable tool alongside experimental study. Due to computational limitations, the traditional approach has been to utilize simplified global reactions to simulate and evaluate the combustion and performance parameters in internal combustion engines. Due to increased interest in various advanced engine configurations with various combustion modes and injection strategies, this approach could reduce credibility of the predictions for advanced concepts since it depends on arbitrary adjustment of model parameters.

Oxidation of hydrocarbons is shown by detailed chemical kinetic mechanisms. These detailed mechanisms are very large and comprised of a large number of species and reactions. As the size of the hydrocarbon increases, the length (number of species and reactions) of the mechanism increases along with it. Due to the construction from smaller hydrocarbons (HC) progressing to larger HC and intermediate radicals, detailed mechanisms of large hydrocarbons consist of many species and reactions that are redundant. Those species do not have significant impact on simulating ignition and combustion phenomenon and needlessly raise the computational and memory requirements. These reactions and species can be identified and eliminated from the detailed reaction mechanism without compromising the accuracy and integrity of the detailed reaction mechanism. The detailed and large mechanisms cannot be employed in present solvers because they are time expensive. For example, gasoline and diesel fuels consist of thousands of reactions and hundreds of hydrocarbons [2].

Fuels, such has gasoline and diesel, are composed of hundreds of hydrocarbons. These hydrocarbons include alkanes, alkenes, aromatics and naphthenes. Surrogate fuel mechanisms that contain limited number of hydrocarbons from all the abovementioned hydrocarbon types are important in this regard. There is a necessity to use reduction techniques to produce reduced order mechanisms that can replicate the predictions of detailed mechanisms [3]. The reduction techniques decrease the number of species and reactions.

Redundant reactions were identified by two methods [1–3]:

In a reaction mechanism, there are two subsets of reactions; slow reactions and fast reactions. The reaction rates analysis divides a reaction mechanism into abovementioned two subsets of slow and fast reactions. The sensitivity analysis is performed to divide the reaction mechanism into two subsets of rate limiting and non-rate limiting reactions. When combined, both analyses identify redundant reactions. The redundant reactions identified by this method are non-rate limiting slow reactions. As the redundant reactions are eliminated, the species taking part in those reactions gets automatically eliminated, hence, a reduced mechanism is obtained [1–3]. Sensitivity analysis is discussed further in detail in the next section with detailed references.

*Numerical Simulations and Validation of Engine Performance Parameters Using Chemical… DOI: http://dx.doi.org/10.5772/intechopen.106536*

Using the computational singular perturbation (CSP) technique [4, 5], reduction of iso-octane/n-heptane reaction mechanism by Soyhan et al. [4] and Valorani et al. [5] has been performed that has resulted in reduced and skeletal mechanisms.

Using various computational codes [6–15] and experimental tools, various researchers performed multiple studies to review surrogate fuel mixtures [8], reduced PRF mechanisms [9], with variable intake parameters including an operating range of equivalence ratios, intake pressures and temperatures while considering various engine performance parameters such as heat release rate (HRR) analysis, in-cylinder pressure data [10] and emissions on various engine geometries operating at various operational ranges [11, 12]. Using a mixture of iso-octane, n-heptane and toluene as gasoline surrogate fuel predicts engine performance parameters correctly, especially in HCCI and SI engines. Further addition of di-isobutylene and methylcyclohexane is also recommended. Under stoichiometric and lean conditions, significant number of small particulate formation occurs while large particulate formation shows existence with increasing equivalence ratio. Decrease in the peak in-cylinder pressure can be achieved by mass and temperature reduction. This phenomenon occurs due to heat loss to the chamber walls [13].

A reaction mechanism reduction through sensitivity analysis of a skeletal reaction mechanism for the compression and power strokes by utilizing computational singular perturbation (CSP) method and using the low temperature reaction pathway analysis leads to a reaction mechanism that predicts accurate results for computational studies. Detailed chemistry in conjunction with fluid dynamics enhances the ability of a computational code to correctly predict the engine performance parameters. This is proven in benchmarking the global and quasi-global mechanisms [1] which provided necessary data and confidence in the use of detailed chemistry to correctly predict the engine performance parameters. Also, using 90% iso-octane and 10% n-heptane as surrogate fuel for gasoline helps in correct prediction and best modeling the engine performance parameters. Along with a correct reduced mechanism, mesh independent study is a key to correctly predict and validate the engine performance parameters against the experimental data for a range of equivalence ratios in premixed spark ignition engines.


**4. Predictive models based on LSER model coupled to a group contribution method**

Solvation model may be also used to set up correlation between thermodynamic properties and LSER parameters. Abraham et al. [41,42] reported mathematical correlations based on the general Abraham solvation parameter model for the gas-to-solvent, *KL*, and water-tosolvent, *P*, partition coefficients. Recently, [43-46] modified the Abraham solvation parameter model:

$$\begin{aligned} \text{Log}K\_{L} &= \mathbf{c}\_{\text{cation}} + \mathbf{c}\_{\text{anion}} + (\mathbf{e}\_{\text{cation}} + \mathbf{e}\_{\text{anion}}) \cdot \mathbf{E} + (\mathbf{s}\_{\text{cation}} + \mathbf{s}\_{\text{anion}}) \cdot \mathbf{S} + \\ &+ (\mathbf{a}\_{\text{cation}} + \mathbf{a}\_{\text{anion}}) \cdot \mathbf{A} + (\mathbf{b}\_{\text{cation}} + \mathbf{b}\_{\text{anion}}) \cdot \mathbf{B} + (\mathbf{l}\_{\text{cation}} + \mathbf{l}\_{\text{anion}}) \cdot \mathbf{L} \end{aligned} \tag{18}$$

$$\begin{aligned} \text{LogP} &= \mathbf{c}'\_{\text{cation}} + \mathbf{c}'\_{\text{anion}} + \left(\mathbf{e}'\_{\text{cation}} + \mathbf{e}'\_{\text{anion}}\right) \cdot \mathbf{E} + \left(\mathbf{s}'\_{\text{cation}} + \mathbf{s}'\_{\text{anion}}\right) \cdot \mathbf{S} \\ &+ \left(\mathbf{a}'\_{\text{cation}} + \mathbf{a}'\_{\text{anion}}\right) \cdot \mathbf{A} + \left(\mathbf{b}'\_{\text{cation}} + \mathbf{b}'\_{\text{anion}}\right) \cdot \mathbf{B} + \left(\mathbf{v}\_{\text{cation}} + \mathbf{v}\_{\text{anion}}\right) \cdot \mathbf{V} \end{aligned} \tag{19}$$

by rewriting each of the six solvent equation coefficients as a summation of their respective cation and anion contribution. The dependent variables in equations (18) and (19) are solutes descriptors as follows: **E** and **S** refer to the excess molar refraction in units of (cm3.mol-1)/10 and dipolarity/polarizability descriptors of the solute, respectively, **A** and **B**

**Table 3.** LSER descriptors of ionic liquids determined at 313.15 K.

376 Chromatography – The Most Versatile Method of Chemical Analysis

scales developed for polar molecular solvents.

dialkylimidazolium salts.

**contribution method**

parameter model:

bromide [36], 1-butyl-3-methylimidazolium octyl sulfate and 1-ethyl-3-methylimidazolium tosylate [37], Triethylsulphonium bis(trifluoromethylsulfonyl)imide [38], 1-Methyl-3 ethylimidazolium bis(trifluorosulfonyl)-amide and 1.2-Dimethyl-3-ethylimidazolium bis(trifluorosulfonyl)-amide [39] were taken from the sources indicated. Poole & Poole [40] found that the system constants of LSER model for the room temperature ionic liquids fall into the range *e* = −0.62 to 0.86, *s* = 1.7–2.8, *a* = 2.1–7.3, *b* = 0–1.07, and *l* = 0.35–0.96. Compared with the scale of the polar organic solvents *e* = −0.60 to 0.82, *s* = 0.54–2.8, *a* = 0.28–5.50, *b* = 0– 4.8, and *l* = −0.21 to 0.98, we can see that both scales are similar indicating that the solvation properties for the room temperature ionic liquids are classical and fit quite well into the

The (c + lL) term gives information on the effect of cohesion of the ionic liquids on solute transfer from the gas phase. In general, the ionic liquids are cohesive solvents; they interact weakly via nonbonding and π-electrons (*r* system constant is zero) and are not much different to other polar non-ionic liquids. The ionic liquids are roughly as dipolar/polarizable as classical solvents. The hydrogen-bond basicity of the ionic liquid (*a*  system constants) are considerably larger than values obtained for non phases (0-2.1) [1]. The hydrogen-bond basicity of Ils depends on the anion grafted on the cation. ionic liquids can be slightly more hydrogen-bond basic than dimethyl sulfoxide and *N*methylpyrrolidinone, and are weak to moderate hydrogen-bond acids, similar to the aliphatic alcohols. From Table 3 and data collected in the reference [40], we can see that the hydrogen-bond acidity of the ionic liquids depends largely on the cation and is lower for the 1,3-dialkylimidazolium salts with an alkyl group at C-2 position than 1,3-

**4. Predictive models based on LSER model coupled to a group**

Solvation model may be also used to set up correlation between thermodynamic properties and LSER parameters. Abraham et al. [41,42] reported mathematical correlations based on the general Abraham solvation parameter model for the gas-to-solvent, *KL*, and water-tosolvent, *P*, partition coefficients. Recently, [43-46] modified the Abraham solvation

cation anion cation anion cation anion

= ++ + + + + + + + ++

 + a a b b l l *KL ( )· ( )·*

cation anion cation anion cation anion

by rewriting each of the six solvent equation coefficients as a summation of their respective cation and anion contribution. The dependent variables in equations (18) and (19) are solutes descriptors as follows: **E** and **S** refer to the excess molar refraction in units of (cm3.mol-1)/10 and dipolarity/polarizability descriptors of the solute, respectively, **A** and **B**

*P ' ' ( ' ' )· ( ' ' )·*

Log c c e e s s

Log c c e e s s

cation anion cation anion cation anion

cation anion cation anion cation anion

a a b b v v

*( ' ' )· ( ' ' )· ( )·*= ++ + + + + + + + ++

*( )· ( )· ( )·*

**E S A BL** (18)

**E S A BV** (19)

are measures of the solute hydrogen-bond acidity and basicity, **V** is the McGowan volume in units of (cm3.mol-1)/100 and **L** is the logarithm of the gas-to-hexadecane partition coefficient at 298 K. Sprunger et al. calculated equation coefficients for 8 cations and 4 anions using a database that contained 584 experimental log *KL* and 571 experimental log *P* values. No loss in predictive accuracy was observed by separating the equation coefficients into individual cation-specific and anion-specific values. The major advantage of splitting the equation coefficients into individual cation-specific and anion-specific contributions is that one can make predictions for more ILs. In Sprunger's approach, the major advantage of splitting the equation coefficients into individual cation-specific and anion-specific contributions is that one can make predictions for more Ils. Most of the cations are alkylimidazolium. The use of this model is somewhat limited since it can not be extrapolated to alkylimidazolium ionic liquids not initially defined by the method (e.g. with long alkyl chains).

The Use of Solvation Models in Gas Chromatography 379

− − N . Nine groups are used for

6 PF ,

2 CN N .

2 TF N , hexafluorophosphate: −

imidazolium based ionic liquids, 3 ammonium, 3 pyridinium and 4 pyrolidinium based ionic liquids. The authors also add sulphonium and phosphonium ionic liquids although only one set of *KL* (or *P*) data may be found for these families. The twenty one groups which are defined in this method are listed in Table 4. The decomposition into groups of the ionic liquids is very easy, that is as simple as possible. No substitution effects are considered. No exceptions are defined. In Figure 3 are represented all ionic liquids studied in this work. Five groups are defined to describe the chains R1, R2, R3 and R4 grafted on the cation: CH3, CH2, -O-, -O-Ncycl and –OH. These groups allow the calculation of partition coefficients of alkyl based ionic liquids but also functionalized ionic liquids such as ether, alcohols. The remaining seven groups are: CH2cyclic, CHcyclic, Ccyclic, Ncyclic, N+ (ammonium cation), P+

> ⏐ − = N and

trifluoromethylsulfonate : − CF SO3 3 , trifluoroacetate : − ACF3 and dicyanamide: ( ) −

As an example, let's have a look at the decomposition of 1-butyl-3-methylimidazolium hexafluorophosphate. In this case, the decomposition of the molecule into elementary groups is: 2 group 1 (-CH3) + 3 group 2 (-CH2) + 3 group 7 ( Ccyclic) + 2 group 9 (Ncyclic) + 1

> N+ R1

R2

Imidazolium Pyridinium Pyrolidinium

P + R4 R1 R3 R2

Ammonium Phosphonium Sulphonium

Group contribution model coupled to LSER (GC-LSER) for estimating the gas-to-ionic liquids partition coefficients and water-to-ionic liquids partition coefficients allows to predict with good accuracy Log *KL* and Log *P* at 298 K of not only alkyl based ionic liquids but also functionalized ionic liquids. The parameters of the group contribution methods were determined for imidazolium, pyridinium, pyrrolidinium, phosphonium, ammonium and sulphonium based ionic liquids containing several different anions. A comparison between the experimental and calculated values showed that the proposed models describe

⏐

4 EtSO , octylsulfate: − OcSO4 , thiocyanate: − SCN ,

S + R1

N+ R1 R2

R2 R3

(phosphonium cation) and S+ (sulphonium cation).

More precisely, Ncyclic represents two structures: +

tetrafluoroborate: −

).

**Figure 3.** Cation of six families of ionic liquids.

N+ R4 R1 R3 R2

N N+ R1 R2 R3

group 14 (PF6-

anions: bis(trifluoromethylsulfonyl)imide :( ) −

4 BF , ethylsulfate: −

In the development of Mutelet et al. [47], the cation with its alkyl chains is splitted in different contributions: (CH3, CH2, N, CHcyclic…). The approach allows to have a predictive model. The aim of this work was to develop a group contribution method allowing to estimate the log *KL* and Log *P* of organic compounds in ionic liquids at 298 K. Using the LSER model proposed by Abraham, the group contribution method expresses LSER coefficients ci, ei, si, ai, bi and li of equation (19). or ' i c , ' i e , ' i s , ' i a , ' i b and i v of equation (20) by:

$$\begin{aligned} \text{LogK}\_{\text{L}} &= \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{c}\_{\text{i}} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{e}\_{\text{i}} \cdot \mathbf{E} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{s}\_{\text{i}} \cdot \mathbf{S} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{a}\_{\text{i}} \cdot \mathbf{A} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{b}\_{\text{i}} \cdot \mathbf{B} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{l}\_{\text{i}} \cdot \mathbf{L} \\ \text{LogP} &= \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{c}\_{\text{i}} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{e}\_{\text{i}} \cdot \mathbf{E} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{s}\_{\text{i}} \cdot \mathbf{S} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{a}\_{\text{i}} \cdot \mathbf{A} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{b}\_{\text{i}} \cdot \mathbf{B} + \sum\_{\text{i}}^{21} \mathbf{n}\_{\text{i}} \times \mathbf{v}\_{\text{i}} \cdot \mathbf{V} \end{aligned} \tag{20}$$

Where ni is the number of group i present in the ionic liquid.

Mutelet et al. [47] proposed to extend the temperature dependent GC-LSER in view of determining the partition coefficient of organic compounds in ionic liquids. The GC-LSER can be rewriting as followed:

$$\text{LogK}\_{\text{L}} = \text{const} + \frac{\sum\_{i}^{21} \mathbf{n}\_{i} \times \mathbf{c}\_{i} + \sum\_{i}^{21} \mathbf{n}\_{i} \times \mathbf{e}\_{i} \cdot \mathbf{E} + \sum\_{i}^{21} \mathbf{n}\_{i} \times \mathbf{a}\_{i} \cdot \mathbf{A} + \sum\_{i}^{21} \mathbf{n}\_{i} \times \mathbf{b}\_{i} \cdot \mathbf{B} + \sum\_{i}^{21} \mathbf{n}\_{i} \times \mathbf{l}\_{i} \cdot \mathbf{L}}{\text{T}} \tag{21}$$

The experimental data used to calculate Abraham's model ion-specific equation coefficients were taken from the collection of [43-46] and were updated with recent data. A total of 1450 gas-liquid partition coefficients and 1410 water-to-liquid partition coefficients were used for the calculation. Solutes were mainly n-alkanes, cycloalkanes, alkenes, alkynes, aromatics, alcohols, ethers, aldehydes, ketones, chloroalkanes. The *E*-scale varies from 0 to 1.5, the *S*scale from 0 to 1.72, the *A*-scale from 0 to 1.04, the *B*-scale from 0 to 1.28, the *L*-scale from – 1.200 to 7.833 and the *V*-scale from 0.109 to 1.799. The dataset is composed of 27 imidazolium based ionic liquids, 3 ammonium, 3 pyridinium and 4 pyrolidinium based ionic liquids. The authors also add sulphonium and phosphonium ionic liquids although only one set of *KL* (or *P*) data may be found for these families. The twenty one groups which are defined in this method are listed in Table 4. The decomposition into groups of the ionic liquids is very easy, that is as simple as possible. No substitution effects are considered. No exceptions are defined. In Figure 3 are represented all ionic liquids studied in this work. Five groups are defined to describe the chains R1, R2, R3 and R4 grafted on the cation: CH3, CH2, -O-, -O-Ncycl and –OH. These groups allow the calculation of partition coefficients of alkyl based ionic liquids but also functionalized ionic liquids such as ether, alcohols. The remaining seven groups are: CH2cyclic, CHcyclic, Ccyclic, Ncyclic, N+ (ammonium cation), P+ (phosphonium cation) and S+ (sulphonium cation).

More precisely, Ncyclic represents two structures: + ⏐ − = N and ⏐ − − N . Nine groups are used for anions: bis(trifluoromethylsulfonyl)imide :( ) − 2 TF N , hexafluorophosphate: − 6 PF , tetrafluoroborate: − 4 BF , ethylsulfate: − 4 EtSO , octylsulfate: − OcSO4 , thiocyanate: − SCN , trifluoromethylsulfonate : − CF SO3 3 , trifluoroacetate : − ACF3 and dicyanamide: ( ) − 2 CN N . As an example, let's have a look at the decomposition of 1-butyl-3-methylimidazolium hexafluorophosphate. In this case, the decomposition of the molecule into elementary groups is: 2 group 1 (-CH3) + 3 group 2 (-CH2) + 3 group 7 ( Ccyclic) + 2 group 9 (Ncyclic) + 1 group 14 (PF6- ).

**Figure 3.** Cation of six families of ionic liquids.

378 Chromatography – The Most Versatile Method of Chemical Analysis

coefficients ci, ei, si, ai, bi and li of equation (19). or '

Where ni is the number of group i present in the ionic liquid.

long alkyl chains).

can be rewriting as followed:

L

= +

by:

are measures of the solute hydrogen-bond acidity and basicity, **V** is the McGowan volume in units of (cm3.mol-1)/100 and **L** is the logarithm of the gas-to-hexadecane partition coefficient at 298 K. Sprunger et al. calculated equation coefficients for 8 cations and 4 anions using a database that contained 584 experimental log *KL* and 571 experimental log *P* values. No loss in predictive accuracy was observed by separating the equation coefficients into individual cation-specific and anion-specific values. The major advantage of splitting the equation coefficients into individual cation-specific and anion-specific contributions is that one can make predictions for more ILs. In Sprunger's approach, the major advantage of splitting the equation coefficients into individual cation-specific and anion-specific contributions is that one can make predictions for more Ils. Most of the cations are alkylimidazolium. The use of this model is somewhat limited since it can not be extrapolated to alkylimidazolium ionic liquids not initially defined by the method (e.g. with

In the development of Mutelet et al. [47], the cation with its alkyl chains is splitted in different contributions: (CH3, CH2, N, CHcyclic…). The approach allows to have a predictive model. The aim of this work was to develop a group contribution method allowing to estimate the log *KL* and Log *P* of organic compounds in ionic liquids at 298 K. Using the LSER model proposed by Abraham, the group contribution method expresses LSER

= ×+ × + × + × + × + ×

ii ii ii ii i i i i

Mutelet et al. [47] proposed to extend the temperature dependent GC-LSER in view of determining the partition coefficient of organic compounds in ionic liquids. The GC-LSER

LogK n c n e · n s · n a · n b · n l ·

21 21 21 21 21 21 L i i i i i i i i i i ii ii i i i i 21 21 21 21 21 21 '' ' ' '

= ×+ × + × + × + × + ×

LogP n c n e · n s · n a · n b · n v ·

ii i i i i

i c , ' i e , ' i s , ' i a , '

**ESA B L**

**ESA B V**

× + × ⋅+ × ⋅ + × ⋅+ ×⋅

n c n eE n aA n bB n lL

i i i i i i i i ii

 21 21 21 21 21

ii i i i

LogK const T (21)

The experimental data used to calculate Abraham's model ion-specific equation coefficients were taken from the collection of [43-46] and were updated with recent data. A total of 1450 gas-liquid partition coefficients and 1410 water-to-liquid partition coefficients were used for the calculation. Solutes were mainly n-alkanes, cycloalkanes, alkenes, alkynes, aromatics, alcohols, ethers, aldehydes, ketones, chloroalkanes. The *E*-scale varies from 0 to 1.5, the *S*scale from 0 to 1.72, the *A*-scale from 0 to 1.04, the *B*-scale from 0 to 1.28, the *L*-scale from – 1.200 to 7.833 and the *V*-scale from 0.109 to 1.799. The dataset is composed of 27

i b and i v of equation (20)

(20)

Group contribution model coupled to LSER (GC-LSER) for estimating the gas-to-ionic liquids partition coefficients and water-to-ionic liquids partition coefficients allows to predict with good accuracy Log *KL* and Log *P* at 298 K of not only alkyl based ionic liquids but also functionalized ionic liquids. The parameters of the group contribution methods were determined for imidazolium, pyridinium, pyrrolidinium, phosphonium, ammonium and sulphonium based ionic liquids containing several different anions. A comparison between the experimental and calculated values showed that the proposed models describe the experimental data available with a mean absolute error of about 0.15 log unit. While the model is probably somewhat limited in prediction for pyridinium and pyrrolidinium based ionic liquids because of the poor dataset for these cations, results obtained are satisfactory.

The Use of Solvation Models in Gas Chromatography 381

The solvation parameter model is suitable for describing the retention properties of molecules in chromatographic systems. To establish the system properties requires identification of a group of compounds with well known descriptor values. We have shown that all LSER parameters of solutes may be determined using gas chromatography or experimental techniques. The solvation model may be used either for the physico-chemical characterization of the stationary phases or for the establishment of a suitable quantitative structure–property relationship to facilitate the prediction of further system properties for

*Laboratoire Réactions et Génie des Procédés (CNRS, UPR 3349), Ecole Nationale Supérieure des*

[1] Poole, C.F. Chromatographic and spectroscopic methods for the determination of solvent properties of room temperature ionic liquids. Journal of Chromatography A

[2] Mutelet, F. & Rogalski, M. Experimental determination and prediction of the gas-liquid

[3] Russom, C.L.; Breton, R.L.; Walker, J.D.; Bradury, S.P. An overview of the use of quantitative structure-activity relationships for ranking and prioritizing large chemical inventories for environmental risk assessments Environ. Toxicol. Chem. 2003; 22 1810-

[4] Katritzky, A.R.; Maran, U.; Lobonov, V.S.; Karelson, M. Structurally Diverse Quantitative Structure-Property Relationship Correlations of Technologically Relevant

[5] Abraham, M. H.; Grellier, P. L. & Mc Gill R.A. Determination of Olive Oil-Gas and Hexadecane-Gas Partition Coefficients, and calculation of the corresponding Olive Oil-Water and Hexadecane-Water Partition Coefficients. J. Chem. Soc. PERKIN TRANS II

[6] Vitha, M. & Carr, P.W. (2006). The chemical interpretation and practice of linear solvation energy relationships in chromatography. Journal of Chromatography A 2006;

[7] Abraham, M.H., Poole, C.F. & Poole, S.K. Classification of stationary phases and other materials by gas chromatography Journal of Chromatography A, 1999; 842 79-114. [8] Kamlet, M.J.; Abboud, J.L. & Taft, R.W. (1977). The solvatochromic comparison method. 6. The π scale of solvent polarities. Journal of the American Chemical Society 1977; 18

n-hexadecane partition coefficients J. Chromatogr. A 2001; 923 153-163.

Physical Properties J. Chem. Inf. Comput. Sci. 2000; 40 1-18.

**5. Conclusion**

**Author details**

Fabrice Mutelet

**6. References**

1821.

1987; 797-803.

1126 143-194.

(99) 6027-6038.

2004; 1037 49-82.

compounds lacking experimental values.

*Industries Chimiques, Nancy CEDEX, France*

**Table 4.** Description of the 21 groups used for the estimation of LogKL and LogP.


**Author details**

optimal anodizing parameters, independent of the electrolyte used. The porous oxide layer thickness and hence pore height can grow to many times the height of the barrier layer. It is usually during this oxide growth that electrolyte anions can integrate into the forming porous structure near the oxide/electrolyte interface, while pure alumina is predominantly found in layers close to the metal/oxide interface. It is also possible for voids to form in the pore walls during the growth of the oxide layer. Possible causes of these voids range from oxygen evolution during oxide formation to localized defects in the barrier layer. These defects produce a condensation effect that involves cations and/or metal vacancies at the metal/oxide

Diggle et al. [86].discussed the contradictory research results of the period. For example, the non- porous barrier layer was regarded as amorphous and anhydrous, while the porous layer had been found to be both amorphous and crystalline. In the case of the barrier layer, under normal anodization conditions the layer will be amorphous. However, studies by Uchi et al. [87] have shown that with the right growth conditions it was possible to have amorphous or crystalline Al oxide being produced during anodization. To form a crystalline oxide structure; an Al substrate was first immersed in boiling water to form a hydrous oxide layer [oxyhydroxide with excess water (AlOOH.H2O)]. The substrate was then anodized in a neutral borate solution at high temperatures, during which Al3+ ions move from the metal substrate to the hydrous oxide interface, where they combine and transform the hydrous oxide to crystalline Al2O3. Some of the contradictory evidence of the earlier works discussed by Diggle et al. [86] of the porous layer could be explained by the work of De Azevedo et al. [88]. In this study the structural characteristics of doped and un-doped porous Al oxide, anodized in oxalic acid was investigated using X-ray diffraction (XRD). The XRD patterns for the un-doped samples revealed several peaks associated with Al and Al2O3 crystalline phases on top of a broad peak that was approximately centered on the 2θ angle of 25°. This broad peak indicated that the synthesized layer was a highly disordered and/or amorphous Al oxide compound.

**•** One of the most important aspects of self-assembly lies in the capability of producing uniform structures over a large area using conventional electrodepsotion and anodizing

**•** Nanostructured Ni-Fe alloys, produced by electro-deposition technique provide material with significant improved strength and good magnetic properties, without compromising

**•** Due to better anticorrosive in several aggressive environments, mechanical and thermal stability characteristics of Ni-Mo alloys, the electro deposition of these alloys plays an important role. Broad application of nickel-based composite coatings in electrochemistry is due to the highly catalytic activity in electrocatalytic hydrogen evolution (HER) and electrocatalytic oxygen evolution (OER) as well as good corrosion resistance of nickel in

interface, which subsequently become detached, and form the void.

**6. Conclusions**

150 Modern Surface Engineering Treatments

processes.

the coefficient of thermal expansion.

aggressive environments.

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**Chapter 7**

**Coating Technology of Nuclear Fuel Kernels:**

The coating technology of nuclear fuel kernels (NFKs) is an important part of the nuclear safety research. Now there are many new designs of nuclear reactor which based on the coated fuel particles. Among them, high temperature gas-cooled reactor (HTGR) is one of the Gen-IV reactors and has a bright future in the electricity and hydrogen production because of its

The inherent safety characteristics of HTGR have been paid more attention among many nuclear reactors in the nuclear renaissance, even more adequately after the Fukushima nuclear accident. The first security assurance is the HTGR nuclear fuel element based on the coated fuel particles, so the coating process of nuclear fuel particles is one of the most important key technologies in the research on HTGR. The tristructural-isotropic (TRISO) type coated fuel particle, which has been commonly used in the current HTGR consists of a microspheric UO2 fuel kernel surrounded by four coated layers: a porous buffer pyrolysis carbon layer (buffer PyC), an inner dense pyrolysis carbon layer (IPyC), a silicon carbide layer (SiC) and an outer dense pyrolysis carbon layer (OPyC), as shown in Fig.1. All coating layers are prepared in the spouted fluidized bed by chemical vapour deposition (CVD) method in different

Now, HTR-PM (high-temperature-reactor pebble-bed module), a Chinese 2×250 MWth HTR demonstration plant, is under construction in Weihai City, Shandong Province, PRC. A pilot fuel production line will be built to fabricate 300,000 pebble fuel elements per year, and each pebble fuel element contains about 15000 coated fuel particles, so the higher requirements for mass production of coated fuel particles for fuel elements are put forward. In order to optimize and scale up the coating process of fuel kernels from the lab to the factory, the multiscale study

> © 2013 Liu; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Liu; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

research groups in Germany, USA, South Korea, Japan and China [1-5].

Additional information is available at the end of the chapter

**A Multiscale View**

http://dx.doi.org/10.5772/55651

**1. Introduction**

superior characteristics.

Malin Liu


**4.3 Stability of nanocapsule suspensions**

An important property of colloidal systems is that they remain stable over time and under certain conditions of interest. Some physicochemical instability


**8. Conclusions and Challenges for the Future**

HCC primarily develops from cirrhosis, and these two diseases cause more than two million fatalities annually around the world. Since liver cirrhosis and HCC have limited available treatments, it is crucial to stop the spread of these diseases as early as possible. In recent years, it has become evident that the gut microbiota, via the gut–liver axis, significantly contributes to the development of HCC. Dysbiosis can be caused by a variety of lifestyle variables, and it has been shown in numerous human trials and animal studies that short-term treatment with probiotics or synbiotics can reverse dysbiosis and consequently enhance liver health. Fatty liver disease is one instance where this is especially accurate. As a means to interfere with the effects of dysbiosis, TLR4 inhibitors are expected to enter clinical development. A possible method of lowering the occurrence of HCC is to prevent the early onset of progressive liver disease through the microbiome. Probiotics and synbiotics, as well as dietary changes, are possible methods for achieving this. As addressed above, the technological advances in liver cancer biomarker profiling have provided a breakthrough in liver metabolomics research.

In addition, challenges connected to HCC metabolism, cellular interfaces, metabolomics, and metabolic changes have been addressed. Future review research initiatives are proposed for improving HCC performances with clinical metabolomics:

**Author Contributions:** Conceptualization, R.G.; writing—original draft preparation, R.G.; writing review and editing, R.G., S.J.Y., and K.T.S.; supervision, K.T.S.; project administration, K.T.S.; funding acquisition, K.T.S. All authors have read and agreed to the final version of the manuscript.

**Funding:** This research was funded by the Hallym University Research Fund and the Basic Science Research Program through the National Research Foundation (NRF) of Korea, funded by the Ministry of Education, Science, and Technology (NRF-2018M3A9F3020956, NRF-2019R1I1A3A01060447, NRF-2020R1I1A3073530, and NRF-2020R1A6A1A03043026). This work was also supported by the Promotion of Innovative Businesses for Regulation-Free Special Zones, funded by the Ministry of SMEs and Startups (MSS, Korea) (P0020622).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are contained within the article.

**Acknowledgments:** Ki Tae Suk would like to thank the NRF of Korea, the Ministry of Education, Science, and Technology, and the Ministry of SMEs and Startups (MSS) for the funding support. R.G. specially thanks Ki Tae Suk for his support.

**Conflicts of Interest:** The authors declare no conflict of interest.


**Acknowledgements**

We would like to thank to the generous support of European Research Council's Horizon 2020 programme under grant agreement 760331 (Newcotiana) and 774078 (Pharma Factory), as well as the Sir Joseph Hotung Charitable Settlement. We also would like to thank colleagues in the Hotung Molecular Immunology Unit at St. George's, University of London for their support and constructive discussion.

*Immunosuppression and Immunomodulation*


**Author details**

Connor Wright, Christine W. Chan\* and Paul Laforge

\*Address all correspondence to<EMAIL_ADDRESS>
Energy Informatics Laboratory, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan, Canada


*2.2. Shell-Side Pressure Drop*

Similarly to heat transfer coefficients, pressure drop in the shell side cross-flow tube bundle is calculated via well-known empirical equations from, e.g., [37]. The actual formula to be used depends on the bundle geometry, possible presence of fins, etc.


**Abstract**

Septoplasty is one of the most commonly performed procedures by rhinologists. This article will provide a brief review of the surgical anatomy and the operative techniques of this procedure. Both endoscopic and open septoplasty procedures will be addressed. However, more than 15% of patients undergoing septoplasty fail to achieve symptomatic relief. Incomplete separation of the bony-cartilaginous junction and inadequate correction of the caudal septal deviation are the main reasons for persistent septal deviation after primary septoplasty. In revised septoplasty, correction of the caudal septal deviation can be done by proper correction of the cartilaginous curvature and strengthening of the structure using a batten graft.

**Keywords:** nasal septum, septoplasty, endoscope, caudal septal deviation


*4.3. Sensitivity Analysis of the Recycling Process*

Although battery recycling activities are still in their infancy and huge uncertainties are related to the recycling techniques, it is widely believed that traction batteries are worthy of recycling and reusing, both from the environmental perspective and the cost-benefit view. Based on previous studies, the NMC batteries, which contain high cobalt and nickel, are expected to be recycled and about 50% energy for the battery's primary production is reported to be saved. However, the LFP batteries are hardly reused since the lithium metal is relatively abundant and cheap [37–41].

In this sense, we explore whether the consideration of recycling of batteries would considerably change the results. Here, we assume 30% energy saving for NMC batteries in the battery production process and 10% for LFP, as part of lithium, nickel, and aluminum are also recyclable. Considering the battery recycling, the life cycle energy consumption and GHG emissions of four vehicle types are shown in Figure 5:

**Figure 5.** Life cycle GHG emissions with battery recycling.

Notably, battery recycling offers some environmental benefits to electric vehicles, but such a contribution accounts for little in the life cycle. Since heavier batteries are installed in BEVs, relative to PHEVs, more environmental benefits could be achieved through battery recycling. Therefore, the superiority of BEVs is further confirmed. Besides, although NMC-powered vehicles have greater emission reduction, they are still not able to exceed the LFP-powered ones in terms of GHG emission performance.

In general, sensitivity analyses have been performed concerning future electricity generation pathways (2020 and 2030), lifetime mileage, travel distance and UF, and expended system boundary of the recycling stage. The main conclusions can be summarized as follows:

(1) As long as the emission intensity of the power generation is less than 815.00 g CO2-eq/kWh, BEVs are more competitive than PHEVs for both batteries in terms of GHG emissions.

(2) When the lifetime mileage is within 120,000 km to 160,000 km, which is reasonable for vehicles, BEVs emit less GHG emissions than PHEVs. In terms of the travel distance at each time, LFP-powered BEVs are superior to PHEVs, as long as the distance is below the range limitation, while NMC-powered PHEVs are better if the driven distance during single travel is under 96.23 km; at this point, the UF is 0.83.

(3) The impacts of battery recycling are found to be small from the life cycle perspective.

## **5. Conclusions**

In this work, a comprehensive life cycle analysis is conducted to compare BEVs and PHEVs. This analysis is divided into two parts: fuel cycle and vehicle cycle, performed with two different battery chemistries cases: LFP and NMC, and framed to China. The main conclusions are drawn as follows:

(1) BEVs are currently better choices than PHEVs, in terms of energy consumption and GHG emissions. Specifically, BEVs have 3.04% (NMC) to 9.57% (LFP) energy mitigation benefits and 15.95% (NMC) to 26.32% (LFP) emission reduction benefits compared to PHEVs.

(2) The fuel cycle and vehicle cycle have similar contributions to the life cycle emissions for BEVs while the fuel cycle is the dominant emission stage for PHEVs.

(3) Through sensitivity analyses, the superiority of BEVs is further confirmed as BEVs have lower GHG emissions than PHEVs in the vast majority of cases. In this study, NMC-powered PHEVs might be preferable if the GHG emission intensity is higher than 815.00 g CO2-eq/kWh, or when the driven distance at a single travel is over 96.23 km.

While this study provides a comprehensive life cycle environmental performance comparison, some limitations remain.

(1) Although the selected vehicles are believed to be representative, a larger number of vehicles should be considered to confirm the robustness of the results.

(2) Another source of variability in the results relates to battery lifetime assumptions. Since there is no practical evidence regarding the lifetime of batteries and the uncertainty relates to use patterns, future research should pay more attention to these aspects.

(3) Since GHG emission reduction is the main purpose of developing electric vehicles, other potential environmental impacts are disregarded in this study. If a more comprehensive comparison is desired, other impacts should be included.

**Author Contributions:** S.X. conceived and designed the work, analyzed the data and prepared the original draft; J.J. mentored the use of the LCA software and reviewed the writing; X.M. supervised all work. Conceptualization, S.X.; Data curation, S.X.; Formal analysis, S.X.; Methodology, J.J.; Software, J.J.; Supervision, J.J. and X.M.; Validation, X.M.; Writing—original draft, S.X.; Writing—review & editing, J.J.

**Funding:** This research received no external funding.

**Acknowledgments:** We wish to thank Master Ying Duan for helping us with the GaBi software.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


**5. Conclusion**

Note in conclusion that the specific features of agonist responses, including kinetics and magnitude, all-or-nothing behavior and gradual dose-response delay were correctly reproduced by Ca2+ signals elicited by Ca2+ uncaging (**Figure 5**). This supports the idea that agonist-evoked Ca2+ signaling in MSCs includes two different but coupled stages. Initially, agonists stimulate coupling of suitable GPCRs via appropriate G-proteins to PLC, thus triggering IP3 production, activation of IP3 receptors (IP3 Rgrad) followed by the release of Ca2+ ions from Ca2+ store. This machinery generates an initial, presumably local and gradual Ca2+ signal (**Figure 11**). When exceeding the threshold, this local Ca2+ signal stimulates CICR that is mediated by IP3 receptors (IP3 RCICR) presumably located in another, spatially separated Ca2+ store. By involving the trigger-like mechanism CICR, a cell generates Ca2+ responses of virtually universal shape and magnitude at different agonist concentrations above the

**Figure 11.** Working model of agonist transduction in MSCs. The binding of agonists to GPCRs stimulates PLC-dependent hydrolysis of PIP2 to DAG and IP3 . The consequent activation of IP3 receptors (IP3 Rgrad) mediates Ca2+ release from related Ca2+ store, producing an initial Ca2+ signal that gradually rises with agonist concentration (red curve). As soon as this signal reaches the threshold level (dotted line), the process determining agonist-dependent delay of a cellular response, one stimulates IP3 receptors of another type (IP3 RCICR) in separated Ca2+ store and triggers CICR. This provides a significant amplification mechanism that finalizes transduction with a large and global Ca2+ signal (blue curve).

cut-off dose. Of course, the presented model is a simplification of the actual transduction process, and roles for other common contributors to intracellular Ca2+ signaling, including Ca2+ pumps, mitochondria, Ca2+ buffer as well as Ca2+-dependent enzymes and ion channels, remain to be elucidated.