Some arsenic compounds can show extraordinary polymorphism. Realgar (As4S4) is among several minerals with various crystal forms and is one of the most important sources of arsenic for pharmaceutical use. Currently, realgar is used as an arsenic source in many industries, such as weaponry, publishing, textiles, cosmetics, and health products. In this paper, we used and reported new methods for the purification, nanonization, and structural morphological investigations of As4S4 by using planetary ball mills process for nanonization of the compound. The product was characterized using X-ray powder diffraction analysis, Fourier transform infrared spectrometry spectra, and field emission scanning electron microscope (FESEM) imaging. We investigated the morphological properties of FESEM-imaged realgar nanoparticles by an image-processing technique that calculates fractal dimensions using values on a computer with MATLAB software. We applied the Statistical Package for the Social Sciences software for statistics data extracted from the FESEM image and obtained the statistics results of the fractal dimension and histogram plot for the FESEM image.
The anticancer action of arsenic compounds was reportedly discovered in Boston Hospital in 1878, where the influence of Fowler’s solution (1% As2O3 in K2CO3) in reducing the number of white blood cells in leukemia patients was clinically defined [
Realgar is among the numerous minerals used for thousands of years in China as a medicine for treating anthrax, abdominal pains, children’s convulsions, burning, and drying warts and treating abscesses and insect bites [
In this paper,
Mechanochemistry can also be used to synthesize realgar because it can produce nanosized particles with improved solubility by a simple solid-state method [
Image processing of nanostructures involves several preparation steps, separation, and postprocessing that finally lead to the extraction of quantitative data. Recently, computer-based image-processing methods have advanced rapidly, allowing quantification of complex colors, shapes, texture properties, and sizes. Image-processing methods are regularly used in tandem with mechanical and instrumental devices to change human manipulation in the display of an assumed process [
Fractals are irregular forms with equal irregularities across all scales. The fractal dimension is a parameter for investigating the degree of data complexity and, in contrast to the naturally numbered Euclidian dimension, can be a real number [
Various methods are used for estimating the fractal dimension. The box-counting method [
Our study presents descriptive data for the fractal dimension of As4S4 nanoparticles. This method is basically a function in MATLAB (MathWorks, Natick, Massachusetts, USA), which is a powerful software package for drawing and analyzing data, programming, and performing engineering and research calculations [
The starting materials were obtained from Merck (Berlin, Germany) and were used without further purification. Nanomaterials underwent spectrometry using a Fourier transform infrared (FT-IR) Bruker-Tensor 27 in the 400–4000 cm−1 range. The surface morphology of product was characterized by a field emission scanning electron microscope (FESEM) (Hitachi S 4160, Japan) with an accelerating voltage of 20 kV. X-ray powder diffraction (XRPD) measurements were performed using a Philips X’pert diffractometer (PANalytical, Almelo, the Netherlands) with monochromatized CuK
First, 15 mg of powdered realgar (As4S4) was added to 100 mL distilled water and the solution was boiled for 3 hours to remove soluble impurities. After three stages of filtration, and reduction of the pH to 5 by the addition of a few drops of 2 M HCl, the remaining solid realgar was put in an oven for 10 hours at 80 degrees centigrade to dry. The dried compound was then transferred to one of the polymer jars of the planetary ball mills that contained 15 balls with different sizes for 5 hours of nanotreatment at room temperature (ball mill process).
Using MATLAB, 20 sections of the FESEM images of As4S4 were selected and their fractal dimensions were calculated. The obtained fractal dimensions were fed into SPSS software to analyze the normal distribution, correlations, standard deviation, mean, cumulative frequency, and variance.
Considering the resulting numbers, we drew a logarithmic diagram in which the vertical and horizontal axes were
We used the self-similar method for determining the fractal dimension from the FESEM image, which employed the box-counting method for the fractal dimension. Using the box-counting dimension method, we calculated the fractal’s dimension theoretically by applying the following formula:
For the box-counting fractal dimension, we assumed that
The circle of cubes in
The FT-IR spectrum was taken within the 400–4000 cm−1 range to evaluate the purity of the synthesized realgar nanoparticles. Realgar is an inorganic compound and so lacks the spectral complexity of many organic materials and is without obvious factor groups. Upon adjusting the pH, some acid and alkali were added to the solution, which caused absorption peaks; however, there were some impurities, and the widened peaks at 3500 cm−1 clearly show the included water (Figure
FT-IR spectrum of As4S4 realgar compound measured by KBr technique.
The main spectrum at 481 cm−1 shows the As-S vibration, which confirms the realgar compound; many special vibrations for realgar are below 400 cm−1 and these vibrations do not appear in the spectra [
To obtain the crystallite size distribution of the particles by the half-height method, an FWHM XRPD pattern was used (Figure
XRPD pattern of a realgar nanoparticle.
The image of the surface and morphological structure of the sample indicates that the realgar nanopowder obtained was condensed and highly porous and was comprised of 20 nm spherical particles; larger particles observed in the surface of the image were formed as a result of the aggregation of the smaller particles, which manifested as irregular spherical shapes. FESEM images of the realgar nanoparticles are shown in Figure
FESEM images of realgar nanoparticles.
For analyzing fractals in MATLAB, FESEM images are required. In the present research, FESEM images were used for imaging particle surfaces to determine the fractal dimension. Having used MATLAB for determining the fractal dimension of realgar, SPSS software was used to analyze the MATLAB data. SPSS can determine the frequency of groups in one variable, calculate a simple mean for data, prepare data for testing the relationships between variables, calculate correlations, perform multivariate regressions, and analyze diagnostic functions, linear logarithms, and other multivariate logarithms. In the present research, other characteristics, such as statistical analyses, normality tests, correlation of variables, cumulative frequency, standard deviation, variance, mean, and harmonic mean, were extracted in SPSS.
The descriptive statistics of the fractal dimension are listed in Table
Statistics of As4S4 nanoparticles in the fractal dimension.
Valid | Frequency | Percent | Valid percent | Cumulative percent |
---|---|---|---|---|
1.32 | 1 | 5.0 | 5.0 | 5.0 |
1.35 | 1 | 5.0 | 5.0 | 10.0 |
1.39 | 1 | 5.0 | 5.0 | 15.0 |
1.43 | 1 | 5.0 | 5.0 | 20.0 |
1.48 | 1 | 5.0 | 5.0 | 25.0 |
1.49 | 1 | 5.0 | 5.0 | 30.0 |
1.51 | 2 | 10.0 | 10.0 | 40.0 |
1.54 | 1 | 5.0 | 5.0 | 45.0 |
1.57 | 2 | 10.0 | 10.0 | 55.0 |
1.58 | 2 | 10.0 | 10.0 | 65.0 |
1.62 | 2 | 10.0 | 10.0 | 75.0 |
1.69 | 1 | 5.0 | 5.0 | 80.0 |
1.73 | 2 | 10.0 | 10.0 | 90.0 |
1.74 | 1 | 5.0 | 5.0 | 95.0 |
1.78 | 1 | 5.0 | 5.0 | 100.0 |
|
||||
Total | 20 | 100.0 | 100.0 | — |
In statistics and probability theory, skewness reflects the degree of asymmetry of the probability distribution. Skewness is a measure of the presence or absence of distribution function symmetry: for a perfectly symmetrical distribution, the skewness is zero; for an asymmetric distribution with stretching toward higher quantities, the skewness is positive; and for an asymmetric distribution with stretching toward smaller quantities, the skewness is negative. Considering Table
Statistical values of the selected As4S4 nanoparticles.
Total counts | Range | Minimum | Maximum | Mean | Variance | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|
20 | 0.94 | 0.84 | 1.78 | 1.5375 | 0.041 |
|
7.090 |
A grayscale digital image is collected from separate points of gray tones, or brightness, before incessantly variable tones. A normal image is separated into a number of specific points of brightness, and each of those points is defined via a digital data value. A pixel is the greatest elementary element of a digital image; each brightness point is a pixel of the digital image. Essentially, the image histogram shows the distribution of the pixel intensities in the image and those continuously used as a reference. Interactive thresholding can be very effective and afford fast, precise information [
Histogram plot of selected As4S4 nanoparticles.
In the obtained sections of As4S4 nanoparticles, the minimum and maximum fractal dimensions were 0.84 and 1.78, respectively; the mean fractal dimension was 1.53 (in statistics, mean signifies central tendency). The normal and detrended normal P-P and Q-Q correlations are plotted in Figure
Normal and detrended normal P-P and Q-Q plots.
Correlations are associated with value and direction, like a vector: its value indicates the degree of relationship between two variables, and its direction indicates the direction of behavior (either same direction or the opposite) of the variables under investigation. It is a statistic for measuring the power of degree of the linear relationship between several variables and can range from +1 to −1. Values close to +1 and −1 show strong correlation (either positive or negative); however, the more they approach zero, the less powerful the correlation will be. In SPSS, one of the correlation coefficients can be used to determine the degree and type of relationships, based on the type and nature of variables. In the present paper, we used the Pearson correlation coefficient, which is applicable to quantitative variables.
Where continuous variables are considered in normal distributions, this equation is strongly affected by sample size. Therefore, we investigated more points to ensure that we applied a powerful test. The Pearson correlation coefficient is based on the covariance and deviations of the variables, where their estimations can be used for calculating the Pearson correlation coefficient. Also, as Table
Correlation of the fractal dimension of nanoparticles.
Correlations | |||
---|---|---|---|
Fractal | Dimension | ||
Fractal | Pearson correlation | 1 | 0.994 |
Sig. (2-tailed) | — | 0.457 | |
Sum of squares and cross products | 0.392 | 0.047 | |
Covariance | 0.049 | 0.006 | |
|
9 | 9 | |
|
|||
Dimension | Pearson correlation | 0.994 | 1 |
Sig. (2-tailed) | 0.457 | — | |
Sum of squares and cross products | 0.047 | 0.070 | |
Covariance | 0.006 | 0.008 | |
|
9 | 10 |
New methods for the purification, nanonization, and structural and morphological investigations of As4S4, using a planetary ball mills for nanonization of the realgar compound, were reported. The realgar nanoparticles experimentally characterized by using XRPD analysis, FT-IR spectra and FESEM image analysis, and the obtained FESEM images were used for investigate the phase composition, morphological, and fractal dimension calculations by MATLAB and SPSS software. The image of the surface and morphological structure of the sample indicates that the obtained nanopowder was condensed. We conclude that the calculation of the fractal dimensions of the FESEM images of 20 randomly selected particles of the nanomaterial and calculation of the Max, Min, and many other data can be used for the analysis of nanoparticles; therefore, homogeneity and uniformity were considered. The correlation of the fractal dimension of the nanoparticles of As4S4 gave a value of 0.994, which indicates a strong positive correlation between the variables.
The realgar nanoparticles are planned for use in patients with gastric cancer, since it can alleviate pain for a while; it will not be used in trials with healthy individuals because of the highly toxic nature of inorganic arsenic compounds. Finally, investigating the use of this compound for therapeutic purposes will be considered by oncologists.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors gratefully acknowledge the financial support from the Research Council of Imam Khomeini International University. Daniel Glossman-Mitnik is a researcher of CONACYT and CIMAV and acknowledges partial support from both institutions.