INTRODUCTION:

Starch, one of the naturally occurring polysaccharides, consists of two glucosidic amylose and amylopectin macromolecules. Being nontoxic, biodegradable, renewable, and biocompatible makes starch widely used in the pharmaceutical industry. It may be used in the form of a plant extract “native starch” or chemically processed “modified starch”¹.

Starch nanoparticle (SNPs) have gained great attention recently as it used in industries related to nutrition, composite biomaterial, papers and as drug delivery carriers^2-10 . It has been reported that SNPs are used for the encapsulation of chemically different drugs and enhance the transdermal activity of flufenamic acid, caffeine, and testosterone across human skin¹¹. SNPs are formulated using several physical and chemical methods, including high-pressure homogenization^12-14, ultra-sonication^15-17, extrusion¹⁸, gamma radiation¹⁹, dialysis technique²⁰, acid hydrolysis^21-23, and nanoprecipitation^24-30. Among the previously reported methods, ultra-sonication and nanoprecipitation are commonly used.

SNPs preparation by nanoprecipitation process involves the gradual addition of a non-solvent to a dilute starch solution or vice versa to obtain particles in the nano-range. Nanoprecipitation is based on the chemical opening of the starch structure and formation of hydrogen bonds through which SNPs form. Chin et al. synthesized SNPs with an average diameter between 300 nm and 400nm by precipitation of starch solution in ethanol. They used a mixture containing sodium hydroxide and urea solution as a solvent system²⁹. Hebeish et al. produced SNPs with an average PS of 103 nm by modifying the nanoprecipitation method using a solution of sodium hydroxide as a solvent medium, tween 80 as a surfactant and ethanol as a non-solvent medium³¹. Recently, SNPs from different botanical origins were prepared by nanoprecipitation and the particle size of SNPs was between 30 and 75 nm²⁷. In spite the advantages of the nanoprecipitation process, it has disadvantages such as time-consuming, the mass of solvent used, low yield and large uncontrolled particle size (PS), which form aggregates. These drawbacks hinder their industrial application.

Ultra-sonication or the sonoprocess method is a rapid, simple physical method, which has become an intensified method in various processes³². It is based on producing ultrasonic waves that cause modifications of the structural arrangement of starch through acoustic cavitation^33-35. Ultrasonication is particularly effective in breaking up the aggregates of nanoparticles formed through hydrogen bonds, thereby reducing the size and polydispersity of nanoparticles³⁴. Izidoro et al. investigated the impact of ultrasonication on the physical properties of starch suspensions. The results of that investigation showed an improvement in starch solubility, swelling power and water absorption capacity after ultrasound treatment (24 W power with 40% amplitude at a frequency of 20 kHz) for1 h³⁶. Zhu et al. reported that ultrasonic treatment of starch suspension for 30 min at temperatures ranging from 20 to 30 °C resulted in a reduction in the crystallinity of the starch without any reduction in PS³⁷.

The reported data about using sonoprocess in SNPs preparation used temperatures between 8 and 10°C^17,35. However, a study conducted by Zuo, Hébraud et al.³⁸ concluded that the high-intensity ultrasonication treatment of potato starch at 5°C for 30 min resulted in damage to the starch surface and shattering of the granules. Recently, a study conducted by Minakawa et al.³⁹ stated that SNPs can be obtained at room temperature. They obtained a mixture containing 88% starch micro particles and 12% SNPs when the temperature was maintained at 25°C. Boufi et al.⁴⁰ obtained SNPs after 75 min of ultrasonication to maize starch suspended in a mixture of equal volumes of water and isopropanol at 25°C.

Recently, it was reported that the combination of the physical and chemical processes produced nanoparticles with more desirable properties³⁵. Chang et al.⁴¹ obtained potato SNPs by combining sonication and nanoprecipitation at ambient temperature. They stated that this process produced SNPs with high efficiency and low cost. However, the advantages of this method, it takes a long time to prepare as it takes 60 min under continuous stirring at 160rpm to gelatinize the starch at 100°C. Then, the starch paste was treated by ultra-sound for a certain time. After that, the starch aqueous paste was dropwise into absolute ethanol, which was continually agitated with ultrasound.

To the best of our knowledge, there is no publication study the in situ combination of ultrasound treatment and nanoprecipitation with sodium hydroxide simultaneously for the production of maize SNPs. It has been reported that sodium hydroxide breaks the interaction bonds between starch molecules and has an impact on the swelling and gelatinization of starch^31,42. The current work discussed the effect of in situ combination of ultrasound treatment and nanoprecipitation with alkali hydrolysis on obtaining SNPs with a high yield under ambient temperature and short preparation time.

The current study was investigated based on statistical modeling analysis to obtain a statistically optimized formulation with an appropriate controlled PS and polydispersity index (PDI) rather than trial and error reported from the literature review^17,23,43-45. The SNPs optimized formulation was further characterized for zeta potential, transmission electron microscopy (TEM), Fourier transmission infrared (FT-IR), differential thermal analysis, and X-ray to validate the efficiency of the adopted technique.

The in situ combination of sono-precipitation and alkali hydrolysis technique overcomes the time-consuming, severe conditions, large uncontrolled PS, poor polydispersity index (PDI), excessive solvent used, and aggregates reported using each method alone. The coupling of ultrasound treatment and nanoprecipitation was referred to sono-precipitation technique throughout the manuscript.

MATERIAL AND METHODS:

Materials:

Native maize starch was purchased from Biotech for Laboratory Chemicals, Cairo, Egypt. Tween 80 was purchased from Sigma Chemical Co., USA. Absolute ethanol and sodium hydroxide were of analytical grade.

Experimental Design:

A Box-Behnken design (Table 1) was established by Design Expert ® (Version 7.0.0, Stat-Ease Inc.)^46-48 to investigate the effects of the independent variables: starch concentration (mg) (X₁), tween 80 concentration (µL) (X₂), and ethanol concentration (ml) (X₃) on two dependent variables PS (nm) (Y₁) and PDI (Y₂). The concentration range and the portion of independent variables were chosen after preliminary research trials and data gathered from the literature review.

Table 1: Variables in Box-Behnken design

Factor	Levels		Dependent variables
Independent variables	Low	High	Y₁= Particle size (PS) (nm)
Starch concentration (mg) (X₁)	50	150	Y₁= Particle size (PS) (nm)
Tween concentration (µl) (X₂)	200	600	Y₂=Polydispersity index (PDI)
Ethanol concentration (ml) (X₃)	5	10	Y₂=Polydispersity index (PDI)

An experimental design matrix containing 13 runs (Table 2) was constructed and a quadratic nonlinear computer model was provided as follows:

(1)

Where Y is the measured response for each variable dependent; the intercept is b₀; the regression coefficients of the observed experimental Y values are b₁-b₃₃, and the individual coded variables are X₁, X₂, and X₃. The terms X₁X₂ and (i = 1, 2 or 3) represent the terms interaction and quadratics, respectively.

Table 2: Box–Behnken Design for SNPs Formulations and their responses

F	Starch (mg)	Tween (µl)	Ethanol (ml)	PS (nm)	PDI	% Yield
1	150	400	5	79.33 ± 8.57	0.166 ± 0.019	96.68 ± 1.76
2	100	200	10	56.33 ± 6.07	0.147 ± 0.018	97.99 ± 0.30
3	100	400	7.5	55.56 ± 7.26	0.136 ± 0.013	98.30 ± 0.43
4	100	200	5	59.33 ± 8.21	0.150 ± 0.014	97.49 ± 0.99
5	150	200	7.5	83.93 ± 8.53	0.179 ± 0.018	98.15 ± 0.89
6	100	600	5	54.53 ± 4.88	0.133 ± 0.018	98.24 ± 0.23
7	50	600	7.5	44.82 ± 3.31	0.106 ± 0.012	97.34 ± 0.79
8	150	400	10	75.09 ± 6.10	0.165 ± 0.020	98.01 ± 0.58
9	100	600	10	52.33 ± 4.09	0.123 ± 0.023	98.12 ± 0.33
10	150	600	7.5	73.69 ± 5.82	0.157 ± 0.021	97.26 ± 0.56
11	50	200	7.5	46.98 ± 4.25	0.120 ± 0.014	97.30 ± 1.67
12	50	400	10	46.07 ± 4.05	0.115 ± 0.018	97.14 ± 0.69
13	50	400	5	46.52 ± 3.12	0.119 ± 0.015	97.24 ± 0.44
14^a	50	600	10	40.23 ± 4.05	0.114 ± 0.019	98.72 ± 0.89

Where: PS: Particle size, PDI: Polydispersity index, ^a: optimized formulation and the presented data are means of three replications ± standard deviations.

Starch nanoparticles preparation:

Starch nanoparticles (SNPs) were formulated using the in situ combination of Sono-precipitation and alkali hydrolysis technique. Briefly, at ambient temperature, different amounts of native maize starch were dissolved in 10ml 0.1 N sodium hydroxide as the dissolving medium, and sonicated for 10 min at (AMP 80% - Pulse 02/02) using an ultrasonicator (Q125 Sonicator, Qsonica, USA). A clear solution was obtained. After that, different amounts of Tween were added and sonicated for 10 min, and then ethanol was added dropwise under sonication for another 10 min. The preparation was then left in a closed glass tube until complete precipitation of the powder was then filtered through Whatman 1 filter paper to obtain SNPs. The obtained SNPs were left to dry at room temperature before further characterization. The 80% power was chosen because it was reported that above 80% of power, the strong cavitation increases the coalescence among the bubbles, lowering the efficiency of chemical and physical effects¹⁷. The samples used for PS and PDI were kept in a liquid medium under refrigeration (10°C) to avoid agglomeration.

Yields of SNPs:

The yields of the SNPs samples obtained were calculated by dividing the weight of the dried SNPs by the weight of native starch. All samples were measured in triplicate and mean values ± SD were determined.

Particle size and polydispersity index determination:

The SNPs were diluted with deionized water (0.01%, w/v) and the PS and PDI of SNPs were estimated using a Malvern size distribution analyzer (Zeta-sizer, Malvern Instruments, UK) at 25°C. The refractive index and viscosity of water were 1.330 and 0.89 cp, respectively⁴⁹. All samples were measured in triplicate and the mean values ± SD were determined.

Regression modeling:

Using multiple linear regression, data collected from 13 runs for the PS and PDI were analyzed. The effect of each variable formulation and terms of the two-way interaction were estimated. A backward stepwise regression approach was utilized to prevent over-parameterization, and the reduced model was fitted.

Model validation:

The validity of the model was based on the analysis of variance (ANOVA), the adjusted correlation coefficient (Adj R²), the predictive correlation coefficient (Pred R²) and the actual versus expected values plot. To study the interaction between independent variables and the effect of interaction on each response, the response surface plots were developed by a design expert^®software.

Optimization:

A desirability index approach was used for optimization. The desirability index is a 0-1 value reflecting the degree of satisfaction with a set of independent variables. The desirability index of 0 corresponds to the completely undesirable form, while 1 means a completely desirable one. The optimal formulation in this study had a minimum PS and a minimum PDI. After optimization, the optimized formulation was further characterized.

Characterization of the optimized formulation

Zeta potential:

The SNPs were diluted with deionized water (0.01%, w/v) and the electrical charge of SNPs was measured for the optimized formulation using a Malvern size distribution analyzer (Zeta-sizer, Malvern Instruments, UK) at 25°C. The refractive index and viscosity of water were 1.330 and 0.89 cp, respectively (20). The sample was measured in triplicate and the mean value ± SD was determined.

Transmission electron microscopy:

The morphology of the SNPs was visualized by transmission electron microscopy (TEM) (JEM-2100, Jeol, Japan)^48,50,51. A dry carbon-coated copper grid with a droplet of SNPs suspension was stained with 1% phosphotungstic acid and allowed to dry again. After that, it was examined and photographed by TEM⁴⁹.

Fourier Transmission Infrared:

The chemical modifications of the native starch and SNPs were investigated using Fourier Transmission Infra-Red (FT-IR) spectroscopy (BRUKER, I FS 66, Germany). Samples (1–2mg) of starch and SNPs were ground, mixed with potassium bromide, compressed into disks and the FT-IR spectra were recorded. The scanning range was 4000 – 400cm⁻¹ ⁵².

Differential Thermal Analysis:

Thermal analysis of native starch and SNPs was measured using (Shimadzu DTA-50, Japan). The instrument was calibrated using indium then samples (1–1.5mg) were accurately measured, put in an aluminum pan, heated at a temperature of 10°C/min from 25 to 300°C, and nitrogen was used as a purging gas at a flow rate of 15ml/min¹⁶.

X-Ray Diffraction:

The crystalline patterns of native maize starch and SNPs were detected using a powder X-ray diffractometer (Shimadzu XRD 6100, Japan) under this conditions: X-ray tube target, Cu ka radiation; voltage, 40.0 (kV); current, 30.0 (mA). Divergence slit, 1.00°; scatter slit, 1.00°; receiving slit, 0.300 (mm). Scanning drive axis, Theta-2 Theta, scan range, 5.00°- 80.00°; scan mode, continuous scan; scan speed, 8.0000 (deg/min); sampling pitch, 0.020°¹⁷. The relative crystallinity of the samples was determined as the ratio of the crystalline area to the total area calculated using Origin Pro 2020^® software.

RESULTS AND DISCUSSION:

The SNPs were formulated by in situ combination of sono-precipitation and alkali hydrolysis, which provides the advantages of rapid preparation, high yield % and nanoparticles with uniform distribution under ambient conditions without consuming many solvents. The results obtained confirmed the efficiency of the adopted technique. The yields % of SNPs (Table 2) were between 96.68% and 98.30% and were statistically non-significant.

SNPs were obtained after a short preparation time (30 min), which could be explained by two factors: the presence of sodium hydroxide and the acoustic cavitation of ultrasound. A study conducted by Roberts and Cameron⁵³ concluded that the addition of sodium hydroxide solution to potato starch dispersion at room temperature resulted in immediate and rapid swelling of starch granules. Also, gelatinization of starch occurred at a lower temperature. These findings were in agreement with those reported by El-Sheikh⁴². The effect of ultrasonication on breaking the intermolecular bonds between starch molecules has been studied by several researchers^36,54. It was reported that starch solubility, swelling power and water absorption were improved after ultrasonic treatment with 40% amplitude for 1 h^17,36,54.

Regression analysis and interaction study:

The results of PS and PDI showed that the PS was between 44.82nm and 83.93nm, while the PDI was between 0.106 and 0.179 (Table 2). The responses of PS and PDI were fitted individually to linear, 2-factor interaction and quadratic models using linear regression to obtain the model of choice with the highest p-value, Adj R² value, and Pred R² value. The model reduction was performed by removing the non-significant model terms to improve the chosen model and achieve a higher Pred R².

For the PS, the reduced quadratic model was chosen because it has a significant p-value (< 0.0001) and the Pred R² (0.9874) is in reasonable agreement with the Adj R² (0.9960). The results of ANOVA for the reduced quadratic model of the PS are listed in Table 3. The model F-value (497.01) implies that the model is significant. Additionally, the high value of the determination coefficient (0.9980) endorsed that the statistical model was adjusted correctly. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The Adeq Precision ratio was 58.347, indicating an appropriate signal.

For model evaluation, experimental values were plotted versus predicted values (Fig 1b) and a good correlation between variables was observed, indicating that the model is valid. After that, the coded PDI final equation was obtained and was as follows:

PDI = + 0.14 + 0.026 X₁- 0.009625 X₂- 0.00225 X₃(3)

From the PS and PDI polynomial equations and response surface plots (Fig 2), it was detected that the PS and PDI were significantly increased by increasing the starch concentration and decreasing with increasing concentrations of Tween and ethanol.

Fig. 2: 3D-Response surface plots showing the effect of independent variables (starch concentration, tween 80 concentration and ethanol concentration) on the dependent variables (particle size (P.S) (nm) and polydispersity index (PDI).

The increase in PS and PDI associated with increasing starch concentration could be depicted by increasing polymer concentration, which increases the viscosity of the formulation. The high viscosity of the formulation hinders both the dispersion of starch toward ethanol and sonoprocess action, leading to the formation of a large PS⁵⁵. Another explanation could be due to the increasing number of nanoparticles that result in an overlapping and cross-linking reaction between SNPs^14,15,43.

The observed decrease in PS and PDI with increasing tween concentration could be attributed to the well-known role of tween as a surfactant in decreasing surface tension and increasing the solubility of the solution, which enhances the sonoprocess action. Also, It was reported that the presence of tween during precipitation limits the growth of SNPs³¹. While, the marked decrease in PS and PDI with increasing ethanol concentration could be explained by steric stabilization gained by using ethanol, which increases the repulsive force between the SNPs and prevents their aggregation. Also, the increase in the number of hydrogen bonds between ethanol and starch that increases the number of SNPs and decreases their size^31,56,57.

It was noticed that the obtained PS and PDI were smaller than those reported for nanoprecipitation alone. Tan et al.³⁰ obtained SNPs from waxy maize starch acetate by dropwise addition of 50ml distilled water to a solution of starch in 20ml acetone. Although they obtained PDI from 0.005 to 0.038 by increasing the starch acetate concentration from 1 to 8mg/ml, the mean diameter increased from 249 to 720nm as the starch acetate concentration increased from 1 to 20mg/ml. It was reported by Chin, Yazid et al. that dropwise addition of 1 ml of starch solution (1%w/v) to 15ml of ethanol solution containing different concentrations of Tween 80 resulted in SNPs with mean PS between 83 and 137nm. It was also observed that the PS obtained from the adopted method was smaller than that obtained from ultrasonication alone at the same ultrasonication power (80%). Bel Haaj, Magnin et al.¹⁷ obtained SNPs between 100 and 200nm in diameter after 75 min of 80% ultrasonication to 1.5% starch suspension. Hedayati, Niakousari et al. reported SNPs with a diameter of 163±5.5nm diameter when coupled ultrasonication and nanoprecipitation with acetone compared with 219±6.8 nm by nanoprecipitation alone.

The observed decrease in PS and PDI results could be attributed to the synergetic effect of the adopted technique in reducing aggregates between particles by ultrasonic energy that increase electrostatic repulsion between the SNPs surface, the presence of tween during precipitation that limits the growth of SNPs, and increases the number of hydroxyl groups by using ethanol, which increases the hydrogen bonding and SNPs number, consequently decreasing the size and PDI.

Optimization:

The optimized formulation was chosen by the numerical optimization of the design expert^® software. Variable parameters were set in range and the weights were all equal to one. The optimized formulation in this study had a minimum PS and a minimum PDI. F14 (Table 2) was chosen as the optimized formulation with a desirability of 0.995. The predicted PS and PDI values of F14 were generated using the model equations. The experimental versus predicted values of F14 showed a percentage prediction error of less than 12%, indicating the reliability of the model. The optimized formulation was further characterized to confirm the validity of the in situ combination of sono-precipitation and alkali hydrolysis technique in SNPs preparation under ambient conditions.

Zeta potential:

The result of zeta potential was -20±0.25mV, suggesting good formulation stability⁵⁸. The reason behind the negative charge is due to the presence of hydroxyl groups on the surface of SNPs⁵⁹, which was confirmed by the results obtained from FT-IR analysis, which will be discussed later. Furthermore, using ultrasound that breaks down the powerful van der Waals and electrostatic forces between the surfaces of the starch and decreases SNPs agglomeration³⁵.

Transmission Electron Microscopy:

The SNPs morphology showed that the SNPs had a spherical shape in the nano-range and uniform distribution as observed (Fig 3). It was noticed that the size of the TEM analysis was not significantly smaller than that obtained from the PS analysis, which was explained by the fact that the PS analysis measures the Brownian motion of nanoparticles collection in suspension, thereby giving a mean hydrodynamic size. In contrast, TEM measures the physical size only, which is representative for nanoparticles dried on a TEM grid^31,60,61. Also, the interference of the dispersant into the hydrodynamic diameter⁶². Furthermore, in PS analysis, there is a tendency for the particles to be swelled and hence leading to a higher PS⁵⁹.

Fig. 3: Transmission electron photomicrograph of the SNPs optimized formulation (F14) (scale bar=200 nm).

The monodispersity observed could be attributed to the efficiency of the in situ combination of sono-precipitation and alkali hydrolysis technique in generating effective repulsive forces between SNPs using ultrasonication and ethanol, which produced some degree of steric stabilization^31,56. Results obtained from TEM showed the efficiency of the adopted technique in producing less aggregated nanoparticles in contrast to that obtained using nanoprecipitation alone⁴³.

Fourier Transmission Infrared:

From data obtained from FT-IR (Fig 4), there was no significant chemical change between native starch and SNPs. The main characteristic bands of starch were found in the FT-IR scans of both the native starch and SNPs with a change in intensity. The change in intensity could be linked to a decrease in the molecular order of the starch double-helix structure¹³. The band of C-H was observed at 2921 cm^-1 in native starch, while SNPs at 2917 cm^-1 and 2943 cm^-1 with increasing intensity. The FT-IR band corresponding to the hydroxyl group was observed at 2921 cm^-1 in native starch showing a narrow band, while in SNPs showed a broadband between 2753 cm^-1 and 2986 cm^-1. The broadband observed in SNPs was attributed to the dynamic vibrational stretching of free inter- and an intermolecular bonded hydroxyl group⁶³. The glycoside linkage of starch (C-O-C) was observed in native starch and SNPs at 868.9 cm^-1 and 863.16 cm^-1, respectively. It can be inferred from the above that SNPs obtained from the adopted technique under ambient conditions have the same structure as native starch with small divergence. These results confirm the efficiency and validity of the adopted technique for obtaining SNPs without a change in their chemical structure.

Fig. 4: Fourier Transmission Infrared of (a) native starch and (b) SNPs.

Differential Thermal Analysis:

The thermogram of native starch (Fig. 5a) showed one endothermic peak around 50°C, which was assigned to the gelatinization temperature and helix-coil transition^16,27,64-67.

The thermogram of the SNPs (Fig 5b) showed two endothermic peaks around 40°C and 278°C. The broad peak appearing around 40°C in the SNPs thermogram corresponds to the pasting temperature of SNPs^15,27. The broad glass transition was due to the amorphous structure of SNPs and the overlapping of endothermic events at the gelation point of amylopectin⁶⁸. It was noticed that the pasting temperature was significantly lower than native starch, indicating that SNPs were more easily gelatinized than native starch. Similar findings were reported by Qin et al., who explained it by the difference in the helix structure as the single helix of SNPs more susceptible to be destroyed than the double helix structure of the native starch²⁷.

The peak observed around 277.5°C corresponds to the SNPs degradation temperature^16,19,27. It was figured that the degradation temperature of native starch was higher than the degradation temperature of the SNPs. The higher thermal stability of native starch is due to the compact semi-crystalline structure and the high degree of polymerization of native starch³⁹. It was reported that the compacted structure requires more energy for complete degradation than the less ordered structure⁴³. Our findings were in concurring with the reported data that starch presents a degradation temperature between 280 °C and 340 °C, while their nanoparticles have a degradation temperature of (270-325 °C)²⁷. Also, In agreement with the FT-IR results, which showed that the SNPs' structure was less ordered compared to native starch.

Fig. 5: Differential thermal analysis thermograms (DTA) of (a) native starch and (b) SNPs.

X-Ray Diffraction:

The structural and crystallinity properties of native starch and SNPs are illustrated in Fig. 6. Native starch demonstrated peaks at Bragg angles (2Ɵ) at 14.76°, 16.9°, 17.62°, 19.66°, and 22.8°, which are characteristic of an A-type crystalline structure.¹⁷ The X-ray diffraction pattern of SNPs exhibited amorphous characteristics, as the peaks at 14.76°, 16.9°, 17.62°, 19.66°, and 22.8° completely vanished. The amorphous character of SNPs could be attributed to the effect of sodium hydroxide on breaking hydrogen bonds between starch molecules and its effect on the swelling of the starch molecules⁴².

Fig. 6: X-Ray Diffraction of (a) native starch and (b) SNPs. RC: relative crystallinity

XRD patterns revealed that the relative crystallinity of SNPs (9.20%) was lower than that of native starch (26.84%). This decrease in relative crystallinity was attributed to an increase in the amorphous region as a result of decreasing crystallite size after in situ combination of sono-precipitation and alkali hydrolysis treatment⁶⁹. Bel Haaj et al. reported that the ultrasonication of starch suspension for 75 min at temperatures ranging from 8 to 10°C resulted in the reduction of the crystalline pattern¹⁷. Addition, Hebeish et al. reported a loss of the crystalline pattern of the SNPs that were prepared by nanoprecipitation and attributed it to the effect of sodium hydroxide and absolute alcohol in the disruption of the crystalline structure of starch³¹.

Qin et al. stated that the decrease in the crystallinity of SNPs might derive from a single helical structure²⁷. The results of this investigation confirm the efficiency of the in situ combination of sono-precipitation technique in obtaining amorphous nanoparticles with a short preparation time under ambient conditions. The amorphous character of SNPs could be essential for their use as carriers of several drugs in a drug delivery system.

CONCLUSION:

SNPs were successfully produced using an in situ combination of sono-precipitation and alkali hydrolysis under ambient temperature technique. The results revealed that the SNPs were prepared in a short preparation time with a high yield, low solvent used and uniform nano size distribution. A reasonable agreement between Adj R² and Pred R² was obtained for each dependent variable, which confirmed the validity of the design.

The regression analysis study concluded that increasing the tween and ethanol concentration produced particles with a small size. The starch, tween, and ethanol concentration could affect the size and distribution of SNPs. With careful manipulation of these parameters, SNPs with anticipated PS and PDI could be produced. Characterization of the optimized formulation (F14) showed good stability, as observed in thermal and zeta analysis.

Furthermore, FT-IR results confirmed the absence of any chemical change in the structure of the SNPs compared to the native starch. Additionally, the X-ray results confirmed the amorphous structure of the SNPs. All these data suggesting that in situ combination of sono-precipitation and alkali hydrolysis is a rapid and reproducible technique for preparing SNPs under ambient conditions. SNPs with controllable PS and PDI may be useful as a drug delivery carrier in pharmaceutical and food industries and packaging. These findings make SNPs prepared by this technique suitable as a drug carrier by In-vivo delivery for future research.

DECLARATION OF INTEREST:

Declared none.

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Received on 24.07.2020 Modified on 29.08.2020

Research J. Pharm. and Tech. 2021; 14(7):3543-3552.

DOI: 10.52711/0974-360X.2021.00614

Source	Sum of Squares	DF	Mean Square	F-Value	p-value (Prob > F)
Model	2252.66	6	375.44	497.01	< 0.0001
X₁	2036.82	1	2036.82	2696.31	< 0.0001
X₂	56.18	1	56.18	74.37	0.0001
X₃	12.23	1	12.23	16.19	0.0069
X₁X₂	16.32	1	16.32	21.61	0.0035
X₁X₃	3.59	1	3.59	4.75	0.0720
X₁²	127.52	1	127.52	168.81	< 0.0001
Residual	4.53	6	0.76
Cor Total	2257.19	12

Source	Sum of Squares	DF	Mean Square	F-Value	p-value (Prob > F)
Model	0.006138	3	0.002046	189.79	< 0.0001
X₁	0.005356	1	0.005356	496.86	< 0.0001
X₂	0.0007411	1	0.0007411	68.75	< 0.0001
X₃	0.0000405	1	0.0000405	3.76	0.0845
Residual	0.00009702	9	0.00001078
Cor Total	0.006235	12