# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 3 Results and Discussions
### Immobilization of Levb on Mesoporous Material  
Regarding the obtained results of the LevdC-Syloid system, the highest coverage rate was 24.5% on Syloid. Therefore, in further experiments, the Levb-Syloid system was studied using DCM solvent and increasing the immobilization time from 2 h to 72 h. Since Levb is soluble in DCM and LevdC is almost insoluble [38], only Levb is present in the organic solvent. Based on the nitrogen adsorption isotherms (Figure 5), the coverage of the carrier containing the active ingredient showed a maximum of 65.5% for _sample SyLev8_ (mixing for 48 h), while it decreased for _sample SyLev9_ after 72 h of mixing. The SSA of the silicon dioxide support is the smallest in _sample SyLev8_, which may mean the coverage with the largest active ingredient, while in the case of the other samples, this value is around 33-48% (Table 3). Based on the results, the immobilization of the active ingredient Levo in free base form by mixing in DCM solvent with a Syloid support provided much greater surface coverage than in the case of immobilization in the dihydrochloride form with water or methanol. The 2.5-fold increase in the binding efficiency of Levb compared to LevdC is obviously facilitated by DCM with low permittivity (\(\epsilon_{\rm r}=8.93\)) [34; 35; 36], which is almost 10 times smaller than that of water (\(\epsilon_{\rm r}=80.1\)) and 3.5 times smaller than that of methanol (\(\epsilon_{\rm r}=32.7\)). This low permittivity supports the previous finding that binding efficiency can be increased by using a less polar solvent. Thus, by using DCM with a lower permittivity, which dissolves Levb better than water, a larger coverage can be achieved, in principle. Figure 4: Scanning electron micrographs of Syloid carrier (**A**), levocetriazine dihydrochloride (**B**), and _sample SyLev3_ (**C**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \(\mathbf{V_{\rm tot}}\) & **Covered** & \(\mathbf{V_{\rm m}}\) & **Covered V\({}_{\rm m}\)** & **SSA** & **Covered SSA** \\  & **(cm\({}^{3}\) g\({}^{-1}\))** & **V\({}_{\rm tot}\) (\%)** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(\%)** & **(m\({}^{2}\) g\({}^{-1}\))** & **of Syyloid (\%)** \\ \hline Syloid & 1.1 & & 1.1 & & 368 & \\ \hline _SyLev6_ & 0.76 & 30.9 & 0.75 & 31.8 & 247 & 32.9 \\ _SyLev7_ & 0.74 & 32.7 & 0.70 & 36.4 & 235 & 36.1 \\ _SyLev8_ & 0.44 & 60.0 & 0.39 & 64.5 & 127 & 65.5 \\ _SyLev9_ & 0.62 & 43.6 & 0.60 & 45.5 & 190 & 48.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Nitrogen adsorption results of samples made with levocetriazine base (SSA: specific surface area, \(\mathbf{V_{\rm tot}}\): total pore volume, \(\mathbf{V_{\rm m}}\): mesopore volume). The XPS investigation clearly proved the presence of Levo on the surface of all immobilized samples. Typical X-ray photoelectron spectra of the Levo adsorbed on the Syloid are shown in Figure S5. The chemical states detected on the adsorbed samples are the same as those measured on the pure levocetririazine. Like the pure sample, both kinds of nitrogen and chlorine can be identified. We calculated the surface composition of the samples after immobilization (Table 4) using the \"infinitely thick homogeneous sample\" model. This approach only gives raw information: the samples are not homogeneous, and the presence of silica distorts the results. Raw results can be corrected by splitting the O1s intensity belonging to the adsorbed molecule and silica and fitting data to the theoretical composition of Levo. The corrected compositions are closely aligned with both the expected theoretical values and the measured results. The composition of the silica calculated in this way is also plausible, a typical partially OH-covered SiO\({}_{2}\) surface (Table 4). The following model was supposed to get the adsorbed amount of Levo: the spherical silica is covered by a layer of Levo, as illustrated in the Figure 6 inset. The thickness of Levo layers was calculated by this _Lavers-on-Sphere_ model for all samples.