# The toxicity of SiO2 NPs on cell proliferation and cellular uptake of human lung fibroblastic cell line during the variation of calcination temperature and its modeling by artificial neural network

Fariba Abbasi, Mohammad Reza Samaei, Hassan Hashemi, Amir Savardashtaki, Abooalfazl Azhdarpoor, Mohammad Javad Fallahi, Mahrokh Jalili, Sylvain Billet

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Fariba Abbasi, Mohammad Reza Samaei, Hassan Hashemi, Amir Savardashtaki, Abooalfazl Azhdarpoor, et al.. The toxicity of SiO2 NPs on cell proliferation and cellular uptake of human lung fibroblastic cell line during the variation of calcination temperature and its modeling by artificial neural network. Iranian Journal of Environmental Health Science and Engineering, 2021, 19 (1), pp.985-995. 10.1007/s40201-021-00663-4. hal-04211628

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# The toxicity of SiO₂ NPs on cell proliferation and cellular uptake of human lung fibroblastic cell line during the variation of calcination temperature and its modeling by artificial neural network

Fariba Abbasi<fnref n="1" /> · Mohammad Reza Samaei<fnref n="1" /> ID · Hassan Hashemi<fnref n="1" /> · Amir Savardashtaki<fnref n="2" /> · Abooalfazl Azhdarpoor<fnref n="1" /> · Mohammad Javad Fallahi<fnref n="3" /> · Mahrokh Jalili<fnref n="4" /> · Sylvain Billet<fnref n="5" /> ID

Received: 12 November 2020 / Accepted: 5 April 2021
© Springer Nature Switzerland AG 2021

## Abstract

Less attention had been paid to cell toxicity of the various synthesis methods of nanoparticles, this study investigated the effect of the calcination temperature(CT) on the crystallization of SiO₂ nanoparticles(NPs), cell proliferation(CP), and cellular uptake(CU) in MRC-5. In this study, parameters were adjusted as CT(70–1000 °C), calcination time(2, 12, and 24 h), and catalyst feed rate(0.01, 0.05, and 0.1 mL.min⁻<fnref n="1" />). CP was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT) test after a 24-h exposure. The CU was achieved using ICP-MS. Results were analyzed using MATLAB2018. Results revealed that the size of synthesized particles was lower than 50 nm and, the XRD peak varied from 21 to 30° during the increase in CT. FTIR spectra confirmed the existence of Si-O and Si-Cl bonds. The maximum level of crystallization was at 1000 °C. CP decreased with the rise in the concentration of NPs(*p* < 0.05), as well as an increase in feed rate. A positive relationship between increased crystallization and decreased CP(R = 0.78) was seen, while such a trend was not observed in calcination time. The suggested structure in this study was 4:10:1 with R<sub>all</sub> = 0.97, R<sub>test</sub> = 0.97, RMSE = 0.25, and MSE = 0.003. Furthermore, the CU rate increased with the rise in CT and calcination time. The maximum and minimum CU levels were related to NPs calcinated in 1000 °C-24 h and 350 °C-2 h, respectively. As a consequence, the most toxicity of SiO₂ NPs was related to the crystalline NP. Therefore, the increase in CT and the calcination time were significant factors affecting on crystallization of SiO₂ NPs, CP of lung cell, as well as CU of SiO₂.

**Keywords** SiO₂ NPs · Calcination temperature · Cell proliferation · Cellular uptake · ANN

## Introduction

Silica nanoparticles (SiO₂ NPs) are one of the most common NPs [1]. So, it is used widely in industrial and medical fields such as drug delivery, biosensors, cancer treatment, environmental sensors, and solvent dispersion, including toluene in industries [2, 3]. As a consequence of vast applications and even dust storms [4], occupational and environmental exposure increased. SiO₂ NPs are usually available in either amorphous or crystalline forms. Also, SiO₂ NPs were classified as industrial environmental products and, as well as natural environmental pollutants [5]. They are initially colloidal, then become amorphous during synthesis. Finally, it was converted to crystal NPs with a regular structure using calcination [6]. During this process, the amorphous SiO₂ NPs are irregular compare to tetra and hexagonal crystals. This structure is determined by X-ray dispersion (XRD) analysis. The amorphous silica has a wide peak and lower intensity in the XRD

<figure><img src="image_3.png" /></figure>

<footnote>Mohammad Reza Samaei
mrsamaei@gmail.com</footnote>

<footnote n="1">¹ Department of Environmental Health Engineering, Shiraz University of medical science, Shiraz, Iran</footnote>

<footnote n="2">² Department of Medical Biotechnology, Shiraz University of medical science, Shiraz, Iran</footnote>

<footnote n="3">³ Department of Internal medicine, Shiraz University of medical science, Shiraz, Iran</footnote>

<footnote n="4">⁴ Environmental science and technology research center, Department of environmental health engineering, school of public health, Shahid sadoughi University of medical science, Yazd, Iran</footnote>

<footnote n="5">⁵ UR4492, Unité de Chimie Environnementale et Interactions sur le Vivant, SFR Condorcet FR CNRS 3417, Université du Littoral Côte d'Opale, Dunkerque, France</footnote>
plot, while the crystal silica has a sharper peak and a higher level of intensity. The significant nucleation function of silica is a critical factor during the silica crystallization process [7, 8]. Several factors are involved in the SiO₂ NPs crystallization, such as temperature, time, pressure, size of NPs, water content, percentage of elements (NaCl), the addition of calcifying agents, silicate filaments [9–12]. But temperature is one of the most efficient and practical factors in the crystallization of many silica because it has been shown the hydrothermal synthesis increases the crystallinity of silica [13]. So, in many glass and ceramic industries, the crystallization of silica compounds is also carried out by temperature [14]. Silica is a toxic pollutant for human cells [15], but if it is nano-sized, the toxicity effect increases synergistically [16–18]. Previous studies have shown the toxicity of deposited, colloidal, pyrogenic, and crystalline silica on human and mouse cells [19–22]. Although the toxicity mechanisms of amorphous and crystalline NPs are similar [23]. Changes in the structure of the material occur during the crystallization process of silica. This process could affect many of its properties, such as physicochemical ones, biological behavior, and cytotoxicity [24]. SiO₂ NPs penetrate the plasma membrane at a small size (> 10 nm). Because of their smaller size than the cellular organs and deposits in the mitochondria, or even on the nucleus can induce cell death [25]. Therefore, cellular uptake can be associated with the physiochemical and physiology of NPs and biological media. Hence, reactive NPs with small size diffuse to the cell. Diffusion is a non-active that is attributed to the concentration gradient. Therefore, it is expected that the NPs penetrate to cell easily. Moreover, 24-h exposure of lung fibroblast cells to SiO₂ NPs can induce genotoxicity and cell death [19, 21, 26–28]. As a consequence, the cytotoxicity of SiO₂ NPs depends on their crystallinity, physic-chemical, and surface characterization. Exposure to crystalline silica can cause silicosis and lung disease in occupational studies [29]. However, insufficient attention has been paid to the effect of calcination temperature (CT) on the cell proliferation (CP) and cellular uptake (CU) of SiO₂ NPs on fibroblastic cell lines, especially MRC-5. Besides, the prediction of these effects can be investigated by non-linear modeling. Among the models, in the case of a multi-agent intervention, the artificial neural network (ANN) has high power in predicting effects. As 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) can be used as a CP index, this study aimed to investigate the effect of calcination temperature of SiO₂ NPs on cell proliferation and cellular uptake of the MRC-5 cell line and its modeling by ANN.

# Material and methods

## Materials

Used materials were included tetraethoxysilane (TEOS 98%, Merck Germany), pure ethyl alcohol (ethanol, Merck

Germany), ammonia 25% (Merck Germany), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT, Sigma USA), dimethyl sulfoxide (DMSO, Merck Germany), dulbecco modified Eagle's minimal essential medium (DMEM, Gibco England) + Glutamax (high glucose), Fetal Bovine Serum (FBS, Merck Germany), Penicillin Streptomycin Solution (pen-strep, Merck Germany) trypsin 0.025% - EDTA (1X, Merck Germany), and deionized water (Merck Germany).

## Synthesis of SiO₂ NPs

Due to the high homogeneity and purity of the sol-gel method, SiO₂ NPs have been synthesized based on this way by attending to the Stober method [30]. According to this method, under ultrasonic conditions (40 kHz), 5 ml of tetraethyl orthosilicate (TEOS) was dissolved in 30 ml of ethanol for 10 min. Then, 2 mL of distilled water was added to the solution to facilitate ionization. The feed rate of water addition was 0.2 mL.min⁻¹ under ultrasonic conditions. After 1.5 h, ammonia was added as a catalyst at feed rate = 0.01 mL.min⁻¹. The solution was sonicated for 3 h. to obtain a gelatin solution [31].

For the Aging process, the sample was kept under ambient conditions (1 h.). Finally, it was washed with ethanol and distilled water, 3 × 7000 rpm (g-force = 5478.2). The product was dried for 24 h at 70 °C and then crystallized at different temperatures (350, 600, 800, and 1000 °C).

## The characterization of SiO₂ NPs

The crystallization level of the NPs was evaluated by XRD (XRD Philips PW1730, USA). Then, field emission scanning electron microscopy (FESEM, FE-SEM TESCAN MIRA3, Czech Republic) coupled with energy dispersive spectroscopy (EDS, FESEM-FEI Nanosem 450, USA) was used to determine the size and purity of NPs. Moreover, the distribution of NPs in solution was determined by dynamic light scattering (DLS, CORDOUAN TECHNOLOGIES, France). Finally, the functional group and reactivity of NPs were determined by the Fourier-transform infrared (FTIR, Thermo AVATAR, USA) spectroscopy.

## Cell culture

Human fibroblastic lung cells (MRC-5) were purchased from Iran Genetics Resource Center. First, the cell line was taken from a nitrogen tank, and it was cultured under standard conditions in a sterile cabin. The used medium culture was DMEM high glucose (Biosera, France). This media was based on a pilot study enriched by 15% of fetal bovine serum (FBS, Sigma Aldrich USA) and 1% Penicillin-Streptomycin (pen-strep, Thermo Fisher USA). Then the sufficient cell was

<figure><img src="image_4.png" /></figure>
seeded in a T-75 flask (2 × 10⁶ cells per T-75 flask approximately), and it incubated at the temperature of 37 °C, 5% of CO₂, and 90% humidity. Moreover, cell line in any experiment was exposed to SiO₂ NPs after three times of cell passaging for the generation similar culture condition [32, 33].

## MTT assay

Cell proliferation was evaluated by studying the mitochondrial dehydrogenase activity in MRC-5 cells. A 15,000 cells/well were cultured in a 96-well plate. They were grown for 48 h. before exposure. Then, the cells were exposed to the logarithmical range of SiO₂ NPs 0.06, 0.1, 0.6, 6, or 60 mg of SiO₂ NPs/ml during 24 h. Because of the logarithmical range of concentration, they cover a vast range of exposure. Unexposed cells were used as negative controls (100% viability). The MTT assay was performed according to the manufacturer's instructions. Briefly, 10 µl of MTT was added to each well. After four hr., the light absorption was measured at 570 nm wavelengths [34].

## Cellular uptake of SiO₂ NPs

The cells were seeded on a 12-well plate with a density of 100,000 cells/well. It was allowed to grow at a confluence of 80–90%. The cells were then exposed to SiO₂ NPs (diameter < 50 nm) with a concentration of 100 µg.MI⁻¹ in a serum-free environment. After 72 h. of incubation, the cells were washed with PBS, triple times. Then, the cell mass was digested to extract SiO₂ NPs. Due to the stability of SiO₂ NPs to acid digestion, the alkaline digestion method was used in this study. Finally, the cellular uptake rate was measured by inductively coupled plasma mass spectrometry (ICP-MS).

## Statistical analysis

Descriptive statistics were used to determine cellular viability. The cellular viability at each exposure concentration and each NPs were calculated using the one-way Analysis of Variance (ANOVA) test. The correlation between cell proliferation and cellular uptake was analyzed by Spearman the significance level = 0.05. Then linear and nonlinear regression models were applied to improve its mathematical model to determine the effect of calcination temperature on the NPs. Statistical analyses were done using matrix laboratory (MATLAB) 2018.

## ANN modelling

In previous studies, machine learning methods including, deep learning, random forests, k-nearest neighbors, and support vector machines have been applied to toxicity prediction in biology media [35]. Among them, ANN is one of the

modeling methods for determining the non-linear relationship between the variables [36], which was carried out in this study by feed-forward backpropagation and Matlab 2018 [37]. In the neural network, there are several inputs, the hidden layer, and the output, in which the number of neurons in each hidden layer has an important responsibility. Besides, the relationship between the layers was determined by the assigned weight. Initially, the network was trained with derived laboratory data from cell viability, of which 70, 15, and 15% of the data were used for training, validation, and testing, respectively. Subsequently, the training was used to determine the weight and bias, thus controlling the validation error rate. Then, the training was stopped to avoid an over-fitting increase when the validation error was increased by specific iterations [38]. Given the significance and accuracy of the mean square error (MSE) and correlation coefficient (R), the best ANN structure for predicting cell proliferation in MRC-5 exposed to SiO₂ NPs, based on MSE and R from the number of neurons, in the hidden layer recommended. The number of neurons in the hidden layer is determined by two eqs. 1 and 2 [36]:

$$ \frac{2(i + o)}{3} < n < i + o - 1 \quad (1) $$

$$ 0.5i - 2 < n < 2i + 2 \quad (2) $$

i = number of inputs, o = the number of outputs and n = number of hidden layer neuron.

# Result and discussion

## Characterization of SiO₂ NPs

Due to the effect of CT on the crystallization of NPs [39], the effect of CT (70–1000 °C) was investigated on the properties of SiO₂ NPs. As the CT increased, the maximum angle in the XRD diagram varied from 23° in sample A (dried at 70 °C) to nearly 30° at 1000 °C because amorphous silica was produced at a low temperature of drying [40]. In previous studies, the XRD peak for amorphous and crystalline SiO₂ NPs was 14–21° and 26–27°, respectively [12, 41, 42]. The characteristic of synthesized SiO₂ NPs during CT increase, as shown in Table 1.

Table 1. Crystalline degree based on CT. Calcination time = 2 h. A: 70 °C; B: 350 °C; C: 600 °C; D: 800 °C and E: 1000 °C.

Moreover, FTIR spectroscopy of the SiO₂ NPs during variation temperature of calcination express (fig. S1) shows that there were two peaks in the FTIR spectra in the range of 1070–1084 and 450–461 cm⁻¹. Both peaks confirm the presence of Si-O bonds and various functional groups, such as active hydrogenated groups. Also, as the CT increased, the transmittance percent increased. The highest amount of functional groups has been identified in E NPs.
<table><caption>**Table 1** The crystalline degree based on CT. Calcination time = 2 h. Calcination temperature; A: 70 °C; B: 350 °C; C: 600 °C; D: 800 °C and E: 1000 °C</caption><thead><tr><th></th><th></th><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th></tr></thead><tbody><tr><td>The ratio of crystallization to amorphous</td><td></td><td>1</td><td>1.07</td><td>1.17</td><td>1.22</td><td>1.38</td></tr><tr><td>Diameter in DLS</td><td></td><td>&gt;1000 nm</td><td>47.4 nm</td><td>40.2 nm</td><td>35.3 nm</td><td>32 nm</td></tr><tr><td rowspan="3">A percent of Si, O and C from EDS</td><td>Si</td><td>22.72</td><td>24.35</td><td>25.57</td><td>29.12</td><td>35.55</td></tr><tr><td>O</td><td>76.2</td><td>75.65</td><td>74.43</td><td>70.88</td><td>64.45</td></tr><tr><td>C</td><td>1.8</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

According to Table 1, the highest crystallization rate was in E NPs (calcinated at 1000 °C). The crystallization rate was 38% compared to the amorphous sample. In another study, crystallization has been observed at different temperatures according to the synthesis conditions. Moreover, in the presence of calcinates, the temperature required for the conversion of silica to cristobalite was 850 °C [12]. The EDS results show that only the amorphous silica NPs have a carbon peak (Table 1) because many mineral carbon compounds are not volatile at 70 °C at 24 h [43]. Because the crystallization of B-E NPs was well performed, the crystals contain only silica and oxygen [44]. Thus, the presence of only Si and O peaks at temperatures of 350, 600, 800, and 1000 °C indicates the appropriate crystallization of the NPs. On the other hand, the FESEM images of all the NPs have shown the size <50 nm of all the NPs, but DLS results indicated that the mean diameter of amorphous NPs (A) was more than other SiO₂ NPs. This result could be because of rapid agglomeration due to their high surface area and reactivity. The result of this agglomeration can be expressed the differences in primary and secondary size [45, 46]. So, the particle size reached above 1000 nm in A NPs. For B-E samples, the mean particle size was <50 nm. Although the results of Table 1 were shown the process of crystallization of NPs, the increase in temperature did not have a significant effect on the crystallization of silica. Thus, the increase in calcination time was used as a variable to increase the silica crystallization. Hence, the degree of XRD peak was increased when increased calcination time from 2 to 24 h. Thus, the crystallization of NPs increased as calcination time increased. That, the crystallization of SiO₂ NPs at 1000 °C for 24 h. was longer because there was sufficient opportunity for the development of regular SiO₂ crystals with increasing time.

## The effect of the feed rate of catalyst on the crystallinity of SiO₂ NPs

In this study, used silica NPs were < 50 nm due to the effect of the feed rate of catalyst on nanoparticle size [47], crystallization level, purity percentage, size in aqueous media, and XRD peak degree at feed rates of 0.01, 0.05, and 0.1 mL.min⁻¹ were shown in Table 2.

**Table 2.** The level of Crystallization SiO₂ NPs based on the feed rate of catalyst. Calcination time = 2 h and calcination temperature = 1000 °C.

According to Table 2, the rate of crystallization of NPs decreased with increasing feed rate. The maximum crystallization was at feed rate = 0.01 mL.min⁻¹ because higher feed rates do not provide sufficient time and surface for the catalytic reaction. As the lower surface-area-to-volume ratio of catalyst droplets for higher feed rates, as well as the variation of functional group in FTIR (fig. S2), many TEOS molecules may remain unchanged. FESEM and DLS also showed particle size <50 nm under these conditions because these NPs were calcinated at high temperatures, which made them crystallize. As amorphous silica is a reactive nanoparticle, their agglomeration has decreased as crystallinity properties increased.

## Toxicity of SiO₂ NPs on MRC-5

### Toxicity of SiO₂ NPs based on CT

In this study, cellular viability due to exposure of MRC-5 to synthesized SiO₂ NPs at various temperatures, times of calcination, and different feed rates, were determined by the MTT test, as shown in Figs. 1, 2, and 3, respectively. The toxicity of SiO₂ NPs was compared to amorphous silica. Besides, it was expressed NPs disperse in their stock dispersions up to 2.56 mg.mL⁻¹, but the diameter of the NPs in this study was lower than 50 nm. Therefore, it was suspended in DMEM media easily up to 1000 mM in stock based on the pilot study.

As shown in Fig. 1, the CP of MRC-5 rate decreased with increasing concentration of NPs. The minimum CP rate was

<table><caption>**Table 2** The level of Crystallization SiO₂ NPs based on the feed rate of catalyst. Calcination time = 2 h and calcination temperature = 1000 °C</caption><thead><tr><th>Nanoparticles</th><th>F<br>(0.01 ml.min<sup>-1</sup>)</th><th>G<br>(0.05 ml.min<sup>-1</sup>)</th><th>H<br>(0.1 ml.min<sup>-1</sup>)</th></tr></thead><tbody><tr><td>The ratio of crystallization to amorphous</td><td>1.55</td><td>1.115</td><td>1.0127</td></tr><tr><td>Diameter in DLS</td><td>&lt;50 nm</td><td>&lt;50 nm</td><td>&lt;50 nm</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 1** CP of MRC-5 due to SiO₂
NPs: A: amorphous SiO₂, B: SiO₂
NPs calcinated at 350 °C, C: SiO₂
NPs calcinated at 600 °C, D: SiO₂
NPs calcinated at 800 °C, E: SiO₂
NPs calcinated at 1000 °C.
Calcination time = 2 h. exposure
time = 24 h</figcaption></figure>

observed in all NPs at the concentration of 60 mg.mL⁻¹ (*p* < 0.05) because the toxicity of in vitro NPs depends on the concentration dose, size of NPs, and cell type [23, 48, 49]. In general, at concentrations above 100 µg.mL⁻¹, it had toxic effects on MRC-5. Previous studies report that there were toxic effects on HL-7702 at concentrations >25 µg.mL⁻¹ of silica NPs. Moreover, HFL-1 cell viability decreased with an increasing concentration of 250–1500 µg.mL⁻¹ [50]. Also, the lethality at 600 µg.mL⁻¹ concentration was higher than 60 µg.mL⁻¹ (*p* = 0.01). In the study by Petrache Voicu et al., 2015, cell viability decreased dramatically with increasing nanoparticle concentrations, from 12 to 62 µg.mL⁻¹. Besides, cytoplasmic vacuolization was observed at concentrations above 60 µg.mL⁻¹ [27]. According to Fig. 2, cell survival was increased during the calcination temperature up to 600 °C (at concentrations of 60–600 µg.mL⁻¹) but then was decreased. The maximum and minimum relative viability were observed at 600 °C and amorphous. The difference between relative viability in A and E was statistically significant only at 60 µg.mL⁻¹ (*p* < 0.05). Moreover, the kinetic of variation OD in 600 µg.mL⁻¹ v.s. calcination temperature fitted with a 2-order polynomial regression model (R² = 0.999). Whereas, this trend for 600 µg.mL⁻¹ fitted to a 3-order polynomial regression model (R² = 0.995) because A NPs are amorphous, they have a

greater reactivity with MRC-5 due to the high surface area to volume ratio. At higher temperatures, their reaction capacity and catalytic activity have decreased because its structure converts to regular form, and the surface area of NPs reduces. That is effective in reducing their toxicity [23, 51]. Nevertheless, the results indicated that the increase in calcination temperature did not significantly change the crystallization degree. So, the maximum toxicity of MRC-5 at concentrations of 600 and 6000 µg. mL⁻¹ was due to the formation of regular crystals at 1000 °C. Besides, the difference between OD in D and E NPs was significant only at 6000 µg.mL⁻¹ (*P* < 0.05). Pearson correlation showed that there was a positive correlation with CP at 600 µg.mL⁻¹ during calcination temperature (R = 0.78). However, this correlation was very weak at concentrations of 60 and 6000 µg.mL⁻¹ (R<sub>6000µg/mL</sub> = -0.2; R<sub>60µg/mL</sub> = 0.09). Thus, in addition to the toxicity of crystalline NPs, amorphous SiO₂ also has high toxicity to MRC-5. In the previous study, there was also a negative correlation between high concentrations of 100 µg.mL⁻¹ SiO₂ NPs and the cell proliferation of the RD cell line in the 24 h. exposure [27]. Furthermore, during the CT increased, the trend of cell survival changed at a concentration of 60 µg.mL⁻¹. This was positively correlated with concentration of 600 µg.mL⁻¹ (R = 0.5), while correlation was negative with concentration of 6000 µg.mL⁻¹ (R = -0.79 and R =

<figure><img src="image_7.png" /><figcaption>**Fig. 2** Toxicity of SiO₂ NPs on CP of MRC-5 at 1000 °C during D₁) 2 h, D₂) 12 h and D₃) 24 h. exposure time of MRC-5 = 24 h</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 3** Toxicity of SiO₂ NPs on cell proliferation of MRC-5 at 1000 °C during various feed rate of catalyst. A) Feed rate = 0.01 mL.min⁻¹, B) feed rate = 0.05 mL.min⁻¹ and C) feed rate = 0.1 mL.min⁻¹. Exposure time of MRC-5 = 24 h</figcaption></figure>

−0.54). According to these results, the concentration of SiO₂ NPs increased logarithmically, whereas OD was not increased logarithmically. Therefore, it is necessary to determine the relationship between cellular viability and exposure to different concentrations of SiO₂, as described in “Prediction of CP using ANN” section. Besides, with increasing crystallization, the difference between OD at different concentrations increased. Concentration is an influential factor in cellular viability. Finally, exposure to low concentrations of amorphous NPs has been shown to affect cellular viability. As silica NPs are stable, they have a high potential for the production of radicals and oxidizing agents, but there was no linear pattern for OD changes in NPs with different crystallization times. Thus, considering the similarity of toxicity rates of 600 and 6000 µg.mL⁻¹ of E NPs in MRC-5 is essential. Moreover, there was a limited regular structure for A-D NPs. Therefore, E nanoparticle was selected among them. Ultimately, the effect of other variables such as calcination time and feed rate of catalyst were investigated on E NPs.

## Toxicity of SiO₂ NPs based on calcination time at 1000 °C

The effect of increasing calcination time on cell proliferation due to exposure of E NPs at 1000 °C was shown in Fig. 2.

There was no linear pattern for OD changes in NPs with different calcination times (Fig. 2). With increasing time, OD increased at 6000 µg.mL⁻¹ concentration. However, the minimum OD was related to concentrations of 60 and 6000 µg.mL⁻¹ (D₂). At a concentration = 100 µg.mL⁻¹, there was no difference between cell proliferations (*p* > 0.05). As the crystallization of silica NPs has increased, the relationship between concentration variables and the degree of crystallization has changed with the cell proliferation degree, which needs further evaluation.

## Toxicity of SiO₂ NPs based on catalyst feed rate

Concerning the effect of the synthesis method of SiO₂ NPs on cellular toxicity [36], the effect of the catalyst feed rate on CP

was investigated. Based on the used method in the synthesis of SiO₂ NPs, details of the CP of MRC-5 at three concentrations of 42, 420, and 4200 µg.mL⁻¹ were shown in Fig. 3.

CP differed in various feed rates (Fig. 3) so the minimum CP was at feed rate = 0.05 mL.min⁻¹ (*p* < 0.05). Pearson correlation also showed that there was a negative correlation between cell proliferation and feed rate increase at the concentration of 42 µg.mL⁻¹ (R = −0.97). Also, at 0.01 mL.min⁻¹, the maximum toxicity was at 4200 µg.mL⁻¹ (*p* > 0.05), while at B and C NPs and the minimum CP was at 42 µg.mL⁻¹. Only in C NPs, there was a significant difference between the CP of different concentrations (*p* < 0.05). Thus, increasing the catalyst feed rate during the nanoparticle synthesis steps was an efficacious factor in decreasing cell proliferation. Besides, it was suggested that the feed rate decrease. According to SEM results, the change in feed rate did not affect the physicochemical properties of the NPs, such as size, but it affects the toxicity of MRC-5.

## Prediction of CP using ANN

In this study, CP in MRC-5 exposed to different SiO₂ NPs concentrations was predicted and modeled by the ANN method. The Levenberg-Marquardt algorithm for weight and bias was used to determine the best network structure. Based on the number of input and output variables, the number of neurons in the hidden layer = 3–10 was used to predict CP by using ANN, which the best of it was related to 10 neurons (Table 4), because it had the highest correlation coefficient, the lowest MSE for all datasets (0.97) and test datasets (0.98). Figure 4 shows the structure of the best predictive model and the relationship between input and output by the hidden layer.

The minimum MSE = 0.003 (Fig. 4a) was also at Epoch = 1, which after this point, overfitting data and validation set error began to increase. When the CP error was increased for seven iterations, the training was stopped based on the stop algorithm therefore, it prevents network overfitting. The regression coefficient in Fig. 4b shows that the correlation in the test dataset = 0.98 and all test = 0.97. The prediction rate using ANN compared with
<figure><img src="image_9.png" /><figcaption>**Fig. 4** The structure of best model for prediction of CP related to CT</figcaption></figure>

<figure><img src="image_10.png" /></figure>

the experimental value was shown in Table 4. According to Table 3, the maximum and minimum CP in real samples was 0.1 and 0.001 M, respectively, which was at the CT of 1000 °C with feed rate = 0.01 mL.min-1 and HRT = 2 h. (runs 20 and 13).

The results of the ANN prediction were similar, too. In Fig. 5, the correlation between the predicted values by the ANN model versus the actual value obtained from the laboratory tests is 0.94, which indicating a high correlation between the network
<table><caption>**Table 3** The design matrix of four independent variables for CP of MRC-5 (the laboratory results and predicted ANN)</caption><thead><tr><th>Run number</th><th>CT (°C)</th><th>Concentration of SiO<sub>2</sub> NP (µg.ml<sup>-1</sup>)</th><th>Feed rate of catalyst</th><th>HRT (h)</th><th>CP of MRC-5 at OD 570 nm</th><th>ANN Predicted values</th></tr></thead><tbody><tr><td>1</td><td>70</td><td>60</td><td>0.01</td><td>2</td><td>0.736</td><td>0.732</td></tr><tr><td>2</td><td>70</td><td>100</td><td>0.01</td><td>2</td><td>0.659</td><td>0.721</td></tr><tr><td>3</td><td>70</td><td>600</td><td>0.01</td><td>2</td><td>0.535</td><td>0.518</td></tr><tr><td>4</td><td>70</td><td>6000</td><td>0.01</td><td>2</td><td>0.174</td><td>0.139</td></tr><tr><td>5</td><td>350</td><td>60</td><td>0.01</td><td>2</td><td>0.781</td><td>0.776</td></tr><tr><td>6</td><td>350</td><td>100</td><td>0.01</td><td>2</td><td>0.768</td><td>0.769</td></tr><tr><td>7</td><td>350</td><td>600</td><td>0.01</td><td>2</td><td>0.626</td><td>0.611</td></tr><tr><td>8</td><td>350</td><td>6000</td><td>0.01</td><td>2</td><td>0.056</td><td>0.086</td></tr><tr><td>9</td><td>600</td><td>60</td><td>0.01</td><td>2</td><td>0.803</td><td>0.803</td></tr><tr><td>10</td><td>600</td><td>100</td><td>0.01</td><td>2</td><td>0.731</td><td>0.797</td></tr><tr><td>11</td><td>600</td><td>600</td><td>0.01</td><td>2</td><td>0.671</td><td>0.675</td></tr><tr><td>12</td><td>600</td><td>6000</td><td>0.01</td><td>2</td><td>0.140</td><td>0.058</td></tr><tr><td><b>13</b></td><td><b>800</b></td><td><b>60</b></td><td><b>0.01</b></td><td><b>2</b></td><td><b>0.943</b></td><td><b>0.821</b></td></tr><tr><td>14</td><td>800</td><td>100</td><td>0.01</td><td>2</td><td>0.790</td><td>0.817</td></tr><tr><td>15</td><td>800</td><td>600</td><td>0.01</td><td>2</td><td>0.677</td><td>0.701</td></tr><tr><td>16</td><td>800</td><td>6000</td><td>0.01</td><td>2</td><td>0.154</td><td>0.092</td></tr><tr><td>17</td><td>1000</td><td>60</td><td>0.01</td><td>2</td><td>0.73</td><td>0.757</td></tr><tr><td>18</td><td>1000</td><td>100</td><td>0.01</td><td>2</td><td>0.774</td><td>0.752</td></tr><tr><td>19</td><td>1000</td><td>600</td><td>0.01</td><td>2</td><td>0.642</td><td>0.621</td></tr><tr><td><b>20</b></td><td><b>1000</b></td><td><b>6000</b></td><td><b>0.01</b></td><td><b>2</b></td><td><b>0.025</b></td><td><b>0.063</b></td></tr><tr><td>21</td><td>1000</td><td>60</td><td>0.01</td><td>12</td><td>0.802</td><td>0.78</td></tr><tr><td>22</td><td>1000</td><td>100</td><td>0.01</td><td>12</td><td>0.794</td><td>0.777</td></tr><tr><td>23</td><td>1000</td><td>600</td><td>0.01</td><td>12</td><td>0.714</td><td>0.686</td></tr><tr><td>24</td><td>1000</td><td>6000</td><td>0.01</td><td>12</td><td>0.544</td><td>0.524</td></tr><tr><td>25</td><td>1000</td><td>60</td><td>0.01</td><td>24</td><td>0.875</td><td>0.798</td></tr><tr><td>26</td><td>1000</td><td>100</td><td>0.01</td><td>24</td><td>0.734</td><td>0.794</td></tr><tr><td>27</td><td>1000</td><td>600</td><td>0.01</td><td>24</td><td>0.714</td><td>0.704</td></tr><tr><td>28</td><td>1000</td><td>6000</td><td>0.01</td><td>24</td><td>0.622</td><td>0.626</td></tr><tr><td>29</td><td>1000</td><td>60</td><td>0.01</td><td>5</td><td>0.731</td><td>0.638</td></tr><tr><td>30</td><td>1000</td><td>100</td><td>0.01</td><td>5</td><td>0.641</td><td>0.634</td></tr><tr><td>31</td><td>1000</td><td>600</td><td>0.01</td><td>5</td><td>0.473</td><td>0.515</td></tr><tr><td>32</td><td>1000</td><td>60</td><td>0.05</td><td>5</td><td>0.138</td><td>0.176</td></tr><tr><td>33</td><td>1000</td><td>100</td><td>0.05</td><td>5</td><td>0.211</td><td>0.177</td></tr><tr><td>34</td><td>1000</td><td>600</td><td>0.05</td><td>5</td><td>0.18</td><td>0.187</td></tr><tr><td>35</td><td>1000</td><td>60</td><td>0.1</td><td>5</td><td>0.231</td><td>0.384</td></tr><tr><td>36</td><td>1000</td><td>100</td><td>0.1</td><td>5</td><td>0.541</td><td>0.381</td></tr><tr><td>37</td><td>1000</td><td>600</td><td>0.1</td><td>5</td><td>0.469</td><td>0.337</td></tr></tbody></table>

The bold runs implies the similarity of ANN prediction and experimental data

output and the CP. Thus, the ANN has the potential to predict CP in MRC-5 cells in the face of different types of amorphous and crystalline NPs at different concentrations. The prediction of toxicity in some cell lines using Deep neural networks had validation ranging from 0.56 to 0.89 [52]. In the study of Rezai et al., 2016,

<figure><img src="image_11.png" /><figcaption>**Fig. 5** Cell proliferation of MRC-5 exposed to SiO₂ NPs for the actual laboratory results and the predicted by ANN model</figcaption></figure>

the use of ANN has also been introduced as a useful, reliable, cheap, and fast tool for predicting lethality in human cancer cells [53]. Moreover, the neural network model using the k-nearest neighbors' algorithm was identified as the model with the best predictive performance for the nanomaterials toxicity prediction [54]. Besides, the sensitivity analysis of this database was performed to determine the most effectiveness variables on the CP of lung cell line exposed to SiO₂ NPs. According to the linear regression model, the beta coefficient was - 0.52, - 0.429, 0.241, and 0.18 for calcination temperature of SiO₂ NPs, the concentration of SiO₂ NPs, the feed rate of catalyst, and calcination temperature of SiO₂ NPs, respectively. Thus, among the studied variables, the most efficient factor was the calcination temperature of SiO₂ NPs, which was negatively related to the CP of MRC-5.

Table 3. The design matrix of four independent variables for CP of MRC-5 (the laboratory results and predicted ANN).

Table 4. Number of neurons in hidden layer (3–10 neurons).

## The cellular uptake of SiO₂ NPs on MRC-5

MRC-5 cells were exposed to A, B, C, D, and E NPs in the serum-free media to investigate the effect of SiO₂ NPs on cellular uptake. The cellular uptake rate of silica NPs is shown in Fig. 6.

<table><caption>**Table 4** Number of neurons in hidden layer (3–10 neurons)</caption><thead><tr><th>Number of neuron in hidden layer</th><th>R(all data)</th><th>R (test data)</th><th>MSE</th></tr></thead><tbody><tr><td>4:10:1</td><td>0.97</td><td>0.98</td><td>0.003</td></tr><tr><td>4:9:1</td><td>0.958</td><td>0.98</td><td>0.0031</td></tr><tr><td>4:8:1</td><td>0.959</td><td>0.96</td><td>0.0021</td></tr><tr><td>4:7:1</td><td>0.915</td><td>0.92</td><td>0.0015</td></tr><tr><td>4:6:1</td><td>0.92</td><td>0.96</td><td>0.021</td></tr><tr><td>4:5:1</td><td>0.9</td><td>0.89</td><td>0.015</td></tr><tr><td>4:4:1</td><td>0.959</td><td>0.96</td><td>0.027</td></tr><tr><td>4:3:1</td><td>0.91</td><td>0.81</td><td>0.0014</td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>**Fig. 6** The CU of SiO₂ NPs during variation of calcination temperature and calcination time (SiO₂ NPs concentration = 100 µg.mL⁻¹ and exposure time of MRC-5 = 72 h.). **a:** calcination time = 2 h. **A)** Drying at 70 °C, **B)** calcination temperature = 350 °C, **C)** calcination temperature = 600 °C, **D)** calcination temperature = 800 °C and **E)** calcination temperature = 1000 °C. **b:** calcination temperature = 1000 °C. **A)** Calcination time = 2 h., **B)** calcination time = 12 h. and **C)** calcination time = 24 h.</figcaption></figure>

According to Fig. 6, during the CT increases, the maximum and minimum CU were related to E and B NPs that calcinated at 1000 °C and 350 °C, respectively (p < 0.05). As the calcination temperature increases, the CU has been increased. The cellular uptake of A NPs was higher than B and C NPs because amorphous silica has a large surface area and high biocompatibility [16]. In addition, processes such as dissolution, aggregation, and sedimentation effect the reaction of NPs with the cell. Therefore, it may leak from the cell using passive methods due to the small size and spherical form of the SiO₂ NPs, because silica NPs are stable and less metabolized in the cell. Also, NPs dispersion of all samples was performed in standard conditions. The leakage due to the passive transfer of A NPs from the cell was reduced due to the aggregation and size of the secondary particles in the biological environment (Table 1). Moreover, the CU rate of SiO₂ NPs increased with increasing calcination time of NPs at 1000 °C (p < 0.05). Hence, they were 6.46, 6.58, and 6.82 ppm during 2, 12, and 24 h respectively (fig. S2).

## Conclusion

In this study, the effects of temperature, time of calcination, and feed rate of catalyst on the crystallization rate of SiO₂ NPs, cell proliferation, and the cellular uptake of them were

investigated in MRC-5 during exposure of 24 h. the structural analysis of SiO₂ NPs was shown that the size of the synthesized NPs was less than 50 nm, but the amorphous SiO₂ NPs rapidly agglomerated, which can affect their properties. The maximum crystallization rate was observed at 1000 °C (58% < amorphous). The peak of the XRD diagram changed from 21 to 30° during the increase of calcination temperature. The EDAX results also confirmed that with increasing calcinated temperature, the purity of the NPs increased. Thus, increasing CT has been a significant factor in the crystallization of SiO₂ NPs. Moreover, increasing the concentration of NPs and the crystallinity of amorphous cells decreased the CP. Thus, the crystallization of SiO₂ NPs was an efficient factor in MRC-5 cell death. Besides, increase in CT was highly correlated (R = 0.78) with CP at 600 µg.mL⁻¹ concentration. On the other hand, when increased the concentration of SiO₂ NPs logarithmically, OD was not decreased logarithmically. The crystallization of the NPs, the concentration was also an efficient factor in MRC-5 cell death. CP modeling by using ANN showed that the best structure for this study was 4:10:1 with R<sub>all</sub> = 0.97, R<sub>test</sub> = 0.97, RMSE = 0.25 and MSE = 0.003. The correlation between the actual data and the ANN prediction was 0.94. Finally, the maximum CU rates were related to crystal-line NPs. The CU was increased when the CT increased, and the rate of CU has gradually increased with increasing
calcination time of NPs at 1000 °C (p < 0.05). As a consequence, an increase in CT and calcination time were significant factors in the crystallization of SiO₂ NPs, CP of lung cells. So, the most toxicity of SiO₂ NPs was related to the crystalline nanoparticle that calcinated to 1000 °C-24 h. Moreover, increase calcination temperature raises the CU of SiO₂ NPs in the fibroblastic lung cell lines.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s40201-021-00663-4.

**Acknowledgments** This project was financially supported by Shiraz University of Medical Sciences and is Ph.D thesis of Fariba Abbasi (No. 19159).

**Author contribution** FA participated in the design of study, performing experimental, data collection and analysis and write the article. MRS participated the design of study and the main edit of article. HH, AA and MJF participated in the design of study. ASD participated in the design of study and data collection. MJ & SB participated in edit of article.

**Funding** This study was funded by Shiraz University of medical sciences.

**Data availability** The supporting data are available from the corresponding author on reasonable request.

**Declarations**

**Ethics approval and consent to participate** All ethical aspects of this study were approved by Shiraz University of medical science' Ethics Committee (IR.SUMS.REC.1398.1226).

**Consent for publication** Not applicable.

**Competing interests** There is no competing interest.

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