Green synthesis of CuO nanoparticles using Malva sylvestris leaf extract with different copper precursors and their effect on nitrocellulose thermal behavior

Abstract

In this work, we have synthesized copper oxide nanoparticles (CuO NPs) by a precipitation method using leaf extract of Malva sylvestris as a stabilizing agent and three different copper precursors. The obtained CuO NPs have been characterized in detail by X-ray diffraction, ultraviolet–visible spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, and scanning electron microscopy. The as-prepared CuO NPs present the same pure chemical composition and belong to a monoclinic crystalline phase, with a spherical shape and crystallite diameter in the range of 19–26 nm, according to their precursors. Based on the differential scanning calorimetry (DSC) analyses performed at different heating rates, the thermal behavior of pure nitrocellulose (NC) and NC-CuO NPs composites has been investigated using four integral isoconversional kinetic methods. The obtained results show that, whatever the precursor, CuO NPs could be safely used as a catalyst for NC. Moreover, the added nanocatalysts could reduce the activation energy and slightly decrease the peak temperature. Finally, the thermal decomposition process of both NC and NC-CuO composites determined with Kissinger–Akahira–Sunose and Flynn–Wall–Ozawa) models, respectively, is classified as R2, contracting cylinder \(g \, \left( \alpha \right) \, = 1 - (1 - \alpha )^{\frac{1}{2}}\), whereas that of Trache–Abdelaziz–Siwani integral model is ascribed to F1/3 and F3/4 chemical reaction \(g \, \left( \alpha \right) \, = 1 - (1 - \alpha )^{\frac{2}{3}}\).

Graphic abstract

Introduction

Nanoparticles with grain size smaller than 100 nm are currently of great interest in several fields due to the importance of their physicochemical properties, such as high specific surface area and reactivity, compared to the same bulk materials. Currently, transition metal oxides are the most widely used nanoparticles, such as iron oxide, zinc oxide, nickel oxide, copper oxide, occupying a very significant place in several fields of biotechnology, chemistry, physics, and material science [1,2,3,4,5]. Among these transition metal oxides, cupric oxide (CuO) is of great interest due to its particular features and has been used in a wide range of applications such as gas sensors [6], bio sensors [7, 8], photodetectors [9], batteries [10, 11], catalysis [6, 12], biotechnologies [13], and energetic materials [14,15,16]. Its large utilization is the result of its interesting characteristics like stability, low cost, abundance, superhydrophobic properties, nontoxicity, and the preparation simplicity of various nanoparticles with different size and shapes (flower, spherical, nanorods, etc.) [2, 17, 18].

Numerous synthetic methods, in which the morphology and particle size are tailored, were recently developed. Solid-state thermal conversion of precursors, electrochemical method, thermal oxidation method, and solution-based methods [19], which is divided into hydrothermal synthetic method [20] and solution-based chemical precipitation method [21], among others, have been reported [19]. The solution-based chemical precipitation process used in the current work can be defined as a simple chemical reaction carried out at low temperature in an open reactor or container (below 100 °C at atmospheric pressure) between a copper precursor salts and alkaline media solution (e.g., NaOH, urea) as reducing agents [19]. To avoid the aggregation problems of nanoparticles (NPs) caused by their huge surface energy, surfactants as stabilizing or capping agents are commonly used, generally dominated by steric effects that consist of creating a film around the produced crystals to avoid their agglomeration [22]. Two different stabilizing agents can be employed: the first type (chemical approach) is provided by the synthetic routes to produce chemicals, such as polymers with long chains, whereas the second one is derived from biological sources (green approach) using plant extracts as stabilizing and/or reducing agents [19, 22, 23].

The green synthesis approach of CuO is considered as a suitable alternative to the chemical synthesis one, which uses different toxic chemicals as capping or reducing agents, due to the eco-friendly nature of the procedure and the quality of the obtained product [1]. Numerous research works have utilized the plant extracts to synthesize CuO nanoparticles, including Sapindus mukorossi fruit extract [8], Acalypha indica leaf [13], Aloe vera leaf extract [1], Aloe barbadensis Miller leaf extract [24], and gum karaya aquous solutions [25].

Malva sylvestris, commonly known as Mallow, is widely used as medicinal plant in European and Mediterranean countries. In Algeria for instance, it is used for injuries, internal and external inflammations, as anti-ulcerogenic and antioxidant [26, 27]. The main composition of Malva sylvestris is phenols, flavonoids, and vitamins, and it can act as a reducing and stabilizing agent for the synthesis of nanoparticles [26, 28, 29]. Malva sylvestris was used to produce silver nanaoparticles [29], silver nanocrystals [30], gold nanotriangles, and spherical silver nanoparticles [31], as well as CuO nanoparticles as antibacterial for medical applications [32].

Nitrocellulose (NC) is the nitrate ester of cellulose, a carbohydrate polymer, which is a highly flammable compound formed by nitration cellulose through its exposure to nitric acid and sulfuric acid mixture or another powerful nitrating agent [33]. NC has been widely used in military applications such as smokeless powders, dynamites, and rocket propellants, as well as in various civilian fields such as lacquers, paints, coatings, binder, and membranes [34,35,36]. Given the position of nitrocellulose in the manufacturing of different energetic materials and the importance of its thermal decomposition mechanisms that play a key role in the overall reaction processes, several studies have been conducted to investigate the influence of metal oxide micro/nanoparticles on its thermal behavior and thermal decomposition [37,38,39]. Mahajan et al. had assumed that adding 4 mass% CuO bulk powder delays the NC thermal decomposition with 6–10 °C [40]. Wei el al. demonstrated that the incorporation of 2 mass%. NiO to NC improved the gas generation and accelerated the thermal decomposition process [41]. Recently, NC-based superthermites performance is revealed to be dependent on the specific surface area of the corresponding Fe2O3 nanocatalyst [42]. In another work, Zhao et al. have confirmed that Fe2O3 nanoparticles may be safely used with NC without affecting the thermal decomposition model. However, Fe2O3 reduces significantly the activation energy and the critical temperature of the thermal explosion of NC [38, 43]. In another research activity, nanoboron/nitrocellulose composite exhibited a good combustion propagation velocity than B/NC physical mixture [44]. Recently, Maraden et al. found that lead component maybe replaced by bismuth compounds such as bismuth oxide considered as less hazardous than lead as ballistic modifiers in solid propellants based on nitrocellulose with the same thermal behavior [44].

To the best of our knowledge, there is no paper dealing with the study of the influence of green copper oxide nanoparticles on the thermal behavior of nitrocellulose. Thus, this work deals with the green synthesis of CuO nanoparticles (NPs) by precipitation method using three different copper precursors (copper nitrate, copper sulfate, and copper chloride), Malva sylvestris plant extract as stabilizing agent and sodium hydroxide as a reducing agent. The catalytic effect of CuO NPs on the thermal decomposition of nitrocellulose was evaluated using differential scanning calorimetry (DSC) analyses, and the kinetics triplets (activation energy, Ea; pre-exponential factor, log (A); and the most integral reaction model, g(α)) have been determined using four isoconversional methods.

Experimental

Materials

Chemicals of analytical grade purchased from VWR Internationalas are used without further purification. Copper II nitrate trihydrate (Cu(NO3)2, 3H2O), denoted as copper oxide–nitrate (CuO–N), copper(II) chloride dehydrate (CuCl2, 2H2O), name as copper oxide–chloride (CuO–Cl), and copper II sulfate pentahydrate (Cu(SO4), 5H2O), indicated as copper oxide–sulfate (CuO–S), are used as copper precursors to produce CuO nanoparticle samples. Sodium hydroxide (NaOH), as a reducing agent and Malva sylvestris leaf extract as a reducing and stabilizing agent, distilled water as reaction solvent, absolute ethanol and acetone for purification and composite preparation, and nitrocellulose NC with nitrogen content of 12.56% are prepared in our laboratory as reported elsewhere [33, 45, 46].

Malva sylvestris leaf extract preparation

Malva sylvestris leaf extract was prepared by an aqueous extraction. Typically, Malva leafs were collected, washed and dried at room temperature. Twenty g of dried Malva leafs were cut, placed in 300 mL of distilled water, and heated at 80 °C for 10 min until the color of the solution changed to the green-yellow. After filtration and centrifugation, the aqueous leaf extract was conserved at 5 °C for further utilizations.

Synthesis of CuO nanoparticles

The synthesis of CuO nanoparticles was conducted with a precipitation method. In a typical procedure, a stoechiometric amount of copper precursor (0.05 mol L−1) was dissolved in 400 mL of distilled water under magnetic stirring; then, 40 mL of plant aqueous extract was added, and the mixture was heated under stirring at 90 °C for 30 min. The solution color changed from blue to the brown-green. At this temperature, the aqueous solution of NaOH (0.1 mol L−1) was introduced dropwise until pH = 11 and the color changed to black color with a black precipitate of copper oxide. The precipitate was centrifuged and washed several times with absolute ethanol and distilled water. The obtained powder was dried at 70 °C in oven for 6 h to eliminate the impurities of green extract and the possible Cu2O formed and enhance the crystallinity of CuO [47]. The synthesized samples were subjected to a calcinations treatment at 550 °C for 4 h at a heating rate of 4 °C min−1.

Preparation of NC-CuO composites

To obtain more homogenous nitrocellulose–copper oxide (NC–CuO) composites, we prepared each mixture by dissolution rather than physical mixing at solid state. First, 1 g of NC was dissolved in 60 mL of acetone and stirred for 1 h. The appropriate amount of CuO nanoparticles was gradually added under stirring for 4 h. The colloïdal solution was introduced in 80 mL crystallizer, let solvent evaporates and dried at room temperature. The obtained thin light-black NC-CuO films were finely cut into tiny pieces and conserved in a desiccator for further analysis. The NC–CuO composites were prepared with mass ratio NC–CuO of (95: 5, mass%). To study the effect of CuO content, two other samples (98:2, mass%) and (90:10, mass%) were prepared using CuO–N.

Characterization

The microstructure of CuO NPs was examined by PANalytical X’Pert PRO X-ray diffractometer (XRD) with an accelerating voltage of 45 kV, Cu anode Kα radiation (λ = 1.54 Å) and a current of 40 mA. The micrographs of the nanoparticles were recorded on a JEOLJEM200CX scanning electron microscope (SEM) with an accelerating voltage of 2 kV. The chemical composition was investigated using Fourier transform infrared (FTIR) spectroscopy conducted with Bruker-vertex70 using potassium bromide (KBr) to produce pellets, which were analyzed in wavelength range of 400–4000 cm−1. Raman spectroscopy analyses were conducted on thermo Scientific DXR with laser excitation wave at 532 nm in the range of 1000 and 100 cm−1. UV–visible spectra of the synthesized samples were recorded by Shimadzu UV–visible spectrophotometer (UV-1800). The thermal behavior and the thermal decomposition kinetics of the different composites were studied using differential scanning calorimetry (Perkin Elmer DSC8000) at the heating rates of 5, 10, 15, and 20 °C min−1 from room temperature to 300 °C under nitrogen at a flow rate of 20 mL min−1 using Tzero aluminum pans with a sample mass of 0.5 mg.

Results and discussion

Characterization of the produced CuO NPs

The XRD patterns of the synthesized copper oxide are shown in Fig. 1a. The XRD profiles of the different samples exhibit high crystalline nature for the three Cu NPs samples. The results coincide well with the available literature data and belong to the monoclinic crystalline phase of the CuO (Tenorite) with space group C2/c and lattice parameters a = 4.68 Å, b = 3.42 Å, c = 5.13 Å, and β = 99.540 (JCPDS 00–048-1548). The diffraction peaks at 2-theta of 32.55, 35.44, 38.72, 48.77, 53.50, 58.31, 61.59, 66.34, 68.08, 72.76, 75.07° were, respectively, assigned to (110), (−111−002), (111−200), (202), (020), (202), (−113), (−311), (220), (311), and (−222) planes, respectively, with major primary diffraction peaks of (−111−002) and (111−200) planes that have a low surface energy [48]. Moreover, no peak of eventual impurities such as Cu(OH)2 as an intermediate product or Cu2O as a secondary product was observed in the patterns, indicating the purity of the as-synthesized CuO NPs [15]. Furthermore, the XRD results demonstrate that the diffraction peaks of CuO–N and CuO–Cl are sharper than those of CuO–S, indicating the high crystallinity of the two former [49], whereas the broadening of diffraction peaks of CuO–S revealed its small particle size [21, 50].

Fig. 1
figure1

a XRD patterns of nanosized CuO, b UV–visible spectra of the prepared CuO nanoparticles dispersed in water

The average of crystallite size diameter D was estimated using Debye–Scherrer’s equation:

$${\text{D}} = \frac{k \cdot \lambda }{{\beta \cos \theta }}$$
(1)

where D stands for the crystallite size diameter, k is the shape factor (0.94), λ corresponds to Cu-Kα anode radiation wavelength (λ = 1.54 Å), whereas β is the full width at half maxima value (FWHM) in radians, and θ is the scattering angle. The average crystallite size is 19 nm, 24 nm, and 26 nm for CuO–S, CuO–N, and CuO–Cl, respectively.

It is worthy to note that the crystallite size differs from the particle size commonly calculated using generally SEM or porosimetry because of the polycrystalline aggregates. CuO–S exhibited the lowest crystallite size in comparison with that of CuO–N and CuO–Cl, respectively, due to the effect of sulfate anions that suppresses the crystal growth. Similar trend was previously observed by Safa et al. [51] for CuO and by Abbasi et al. for ZnO [52].

The role of surfactant or capping agent, i.e., the aqueous leaf extract in our case, is to reduce the average size of CuO that could increases after nucleation by a stabilization mechanism. The stabilization conducted by steric effect is widely used in solution synthesis through the employment of either surfactants with long chain or plant aqueous extracts containing big molecules such as flavonoids and vitamins [23].

To examine the optical properties of CuO NPs using UV–visible spectroscopy, a suspension of 0.1 g L−1 of each sample was prepared under stirring and sonication in distilled water. The absorption spectra of the different samples at room temperature are displayed in Fig. 1b. An adsorption peak was observed at around 280 nm for CuO–S and CuO–N samples and at 360 nm for CuO–Cl, what is due to the surface Plasmon absorption of copper oxide [23]. When metallic nanoparticles are irradiated, the electric field of the electromagnetic radiation exerts a force on the free conduction electrons, which causes them to move on the surface of these nanoparticles. At the same time, the Colombian attraction between these electrons and the positive ionic metallic network lead to a restoring force, which causes an oscillation of free conduction electrons. When the frequency of the incident radiation resonates with this free conduction electrons oscillation. Plasmon surface resonance (SPR) with an absorption band, which depends on the size and shape of the nanoparticles, appears in the UV–visible spectrum [24, 53, 54]. The SPR band at the range 280–360 nm indicates the formation of CuO nanoparticles since the SPR was observed when the wavelength of incident radiation is far greater than the particle size and increases with the particle size [55]. It was reported that the optical absorption peak is usually affected by the crystallinity, particle size and morphology [56]. The obtained UV peak values are within the same trend than those to other works [50]. The adsorption peak of CuO–Cl shifts to large wavelengths due to the larger particle size with a probable agglomeration as also reported by other reports [57], which assumed that at higher he particle size and agglomeration, redshift of the absorption peak takes place.

SEM images of the synthesized CuO NPs are displayed in Fig. 2. From plots (a, b, c and d), one can see that the prepared CuO NPs using nitrate, sulfate, or chloride are in granular in shape with a relatively small size within nanometric range. The prepared CuO NPs seem to be somewhat agglomerated. SEM images of two CuO–NC films show a regular distribution of CuO catalyst within the NC. The FTIR spectra of the green synthesized CuO nanoparticles are shown in Fig. 3a. The results reveal a perfect superposition of the three spectra, indicating the similar or identical chemical form of the three synthesized samples. A broad adsorption peak at around 3414 cm−1 is attributed to the stretching vibrational mode of O–H bond of the physisorbed H2O molecules [20]. The peaks at around 1642 and 1483 cm−1 are assigned to the bending mode of O–H [6]. The absorption peaks at 2359 and 1350 cm−1 are corresponded, respectively, to the asymmetric and symmetric stretching of the adsorbed CO2 during KBr sample preparation [58]. Three defined absorption peaks at 670–430 cm−1can be clearly seen in the FTIR spectra, which are assigned to stretching vibrational modes of metal–oxygen (Cu–O) bonds. It has been reported that the absorption peak at around 669 cm−1 corresponds to the Cu–O stretching vibration along the (−101) direction, and the high bands at 510 cm−1 are attributed to Cu–O stretching vibration along the (−101) direction [15, 59]. Moreover, as already confirmed by the XRD analysis, no active mode of Cu2O absorption was detected. In addition, the Raman spectra of the synthesized catalysts are shown in Fig. 3b. The different spectra displayed three characteristic bands at 270, 320, and 610 cm−1 and assigned to CuO nanocrystals.

Fig. 2
figure2

SEM images of the synthesized CuO NPs and NC-CuO film: a CuO–Cl (10000× magnification), b CuO–N (7000×  magnification), c CuO–S (×5000 magnification), d NC-CuO–N– film (150×  magnification), e NC-CuO–S film (1200×  magnification)

Fig. 3
figure3

a FTIR spectra of the prepared CuO nanoparticles, b Raman spectra of the prepared CuO nanoparticles

Mechanism of the CuO formation

During the formation of CuO NPs by a solution-based chemical precipitation method, the copper solution precursor releases the cupric cations Cu2+ that react with hydroxyls anions OH– produced by NaOH solution or another OH– generator, giving rise to the growth of Cu(OH)2[60, 61]. The copper precursors concentration determines the CuO NPs morphology [62]. The formation of CuO involves a dissolution of Cu(OH)2 and the precipitation of CuO following the following reactions [20, 63].

$${\text{Cu}}^{2 + } + 2{\text{OH}}^{ - } \to {\text{Cu}}({\text{OH}})_{2}$$
$${\text{Cu}}\left( {{\text{OH}}} \right)_{2} + 2{\text{OH}}^{ - } \to {\text{Cu}}\left( {{\text{OH}}} \right)^{2 - }_{4}$$
$${\text{Cu}}\left( {{\text{OH}}} \right)^{2 - }_{4} \to {\text{CuO}}_{s} + \, 2{\text{OH}}^{ - } + {\text{ H}}_{2} {\text{O}}$$

Thermal analysis

The DSC curves, highlighting the influence of CuO prepared from different precursors on the NC thermal decomposition at different heating rates (βi = 5, 10, 15, 20 °C min−1) are given in Fig. 4. Similarly to other works [37, 64, 65], all systems exhibit only one exothermic peak corresponding to decomposition phenomenon caused by the breakage of O–NO2 bonds during the thermal decomposition of nitrocellulose, due to the low binding energy and the release of NO2 [66, 67]. Such decomposition is commonly followed by autocatalytic parallel reactions involving the formation of reactive species, which could accelerate the thermolysis/hydrolysis processes [38].

Fig. 4
figure4

DSC curves of the investigated systems at different heating rates

The NO2, a very strong oxidizing agent, stagnates in the polymer skeleton and then reacts with the RO radical or its degradation products, leading to the opening of the NC anhydroglucopyranose ring to produce other evolved gases [43, 68], which could be identified using hyphenated analytical techniques such as TG-FTIR (thermogravimetry-Fourier Transform Infrared) [69]. Likewise, with the increase of heating rate, the peak temperature shifts to higher values for which the exothermic peak becomes sharper, indicating a faster chemical reaction [70]. This trend has been evoked as well in our previous works [37, 65]. Furthermore, according to the values of peak temperatures [Table S1 (supporting materials)], one can observe that the introduction of the nanocatalysts does not cause a significant change of the peak temperature, irrespective of the catalyst used. The CuO content on NC–CuO composites has been varied from 2 to 10 mass%, and the obtained DSC curves are presented in Fig. 5. The increase of the CuO content has slightly decreased the peak temperature. This result is conversely to what was observed with copper oxide bulk materials by Mahajan et al. [40], who assumed an increase of maximum peak of exothermic reaction with 4 mass% of CuO.

Fig. 5
figure5

DSC curves of NC-CuO–N at β = 10 °C min−1 at different contents

The compatibility evaluation is an imperative parameter that should be considered to manufacture energetic material formulations. Such parameter is usually studied using DSC [71]. The obtained results indicate the good compatibility of CuO NPs with NC [72]. Thus, from Table S1, it can be inferred that the shift of the decomposition temperatures (at β = 5 °C min−1) for NC-CuO–N, NC-CuO–S and NC-CuO–Cl is 0.1 K, 0.2 K, and 0.1 K, respectively. These values are small enough to conclude that the additives are compatible with NC [73]. Consequently, copper oxide nanoparticles could be safely used as a catalyst in the preparation of homogenous propellants [38].

The catalytic effect of CuO NPs on the thermal decomposition of NC was also highlighted through the comparison of the exothermic heat released ΔH during the decomposition process, since higher energy release means higher performance for modern rocket applications [74]. The exothermic energy releases at different heating rates are shown in Table S1. Accordingly, for a heating rate of 10 °C min−1, the overall released energy of NC is 1435 J g−1. When 5 mass% of CuO NPs was introduced, the energy release increases exceptionally to 2188, 1670, and 2474 J g−1 for NC-CuO–S, NC-CuO–N, and NC-CuO–Cl, respectively, leading to the total decomposition of NC through the improvement of the contact between fuel/oxidizer species of NC. This finding can be easily connected to the better dispersion of CuO within the NC matrix.

Isoconversional kinetic study

It is widely stated that the most accurate method to investigate the thermal decomposition processes is the use of multiple heating rates [75, 76]. At different heating rate, the reaction rate of solid-state thermal decomposition could be expressed as follows:

$$\frac{{{\text{d}}\alpha }}{{{\text{d}}T}} = \frac{A}{\beta }\exp \left( { - \frac{{{\text{Ea}}}}{{{\text{RT}}}}} \right)f\left( \alpha \right)$$
(2)

where log (A) is the pre-exponential factor, Ea is the activation energy, \(\beta \;(\beta = \frac{{{\text{d}}T}}{{{\text{d}}t}})\) is the heating rate, R is the universal gas constant, α is the extent of conversion (0 < α < 1), experimentally derived from DSC or other thermal analysis, T stands for the temperature and f(α) corresponds to the differential reaction model.

The integral reaction model g(α), among the 41 forms reported in the paper of Trache et al. [77], could be obtained by the integration of Eq. 2.

$$g\left( \alpha \right) = \mathop \smallint \limits_{0}^{\alpha } \frac{{{\text{d}}\alpha }}{f\left( \alpha \right)} = \frac{A}{\beta }\mathop \smallint \limits_{0}^{T} \exp \left( { - \frac{{{\text{Ea}}}}{{{\text{RT}}}}} \right){\text{d}}T$$
(3)

Since the temperature term of this equation has no analytic solution, in the purpose to perform the kinetic analysis, several approximate integral methods have been proposed by Coats–Redfern [78], Doyle [79], and Senum and Yang [80].

Based on the obtained DSC data, we have determined the kinetic triplets (activation energy Ea, pre-exponential factor log(A), and the most probable mechanism g(α)) using four isoconversional integral methods, namely it-FWO (Flynn–Wall–Ozawa), it-KAS (Kissinger–Akahira–Sunose), TAS (Trache–Abdelaziz–Siwani), and Vyazovkin’s equation (VYA). The details of the different kinetic methods can be found elsewhere [77, 81].

Figures 6 and 7 represent, respectively, the evolution of activation energy and the pre-exponential factor according to the extent of conversion α for pure NC and NC-CuO–N composites, (Figs. S1 and S2 for NC-CuO–S and NC-CuO–Cl). It can be clearly verified that the used methods give close values and present similar trend with slightly lower values for it-FWO integral model as already pointed out in our previous work [37]. Furthermore, the obtained values of Ea and log(A) are within the range interval of energetic materials (Ea = 80–250 kJ mol−1, log(A)/s−1 = 7–30) [38]. Moreover, the added CuO nanoparticles seem to have the same catalytic effect on the thermal decomposition of nitrocellulose; indeed, for the three systems, the addition of CuO (5 mass%) decreases the activation energy by 7.75 kJ mol−1. The accuracy of the obtained values of Ea and log (A) was supported by the linear coefficient R2 being in the range of 0.9812–0.9997.

Fig. 6
figure6

Activation energy evolution with extent of conversion for pure NC and NC-CuO–N

Fig. 7
figure7

Pre-exponential factor evolution with extent of conversion for pure NC and NC-CuO–N

Additionally, the evolution of the activation energy and the pre-exponential factor and their associated confidence intervals as function of the reaction extent of conversion (α) for NC and NC-CuO–N systems are given in Figs. 8 and 9, respectively. The values of Ea, log (A) and their associated uncertainties as well as the most probable reaction model are given in Table 1 and Table S3. The average deviation for both Ea and log(A) is found to be 13.22% and 16.52% for NC and 10.68% and 14.18% for NC-CuO–N. The obtained deviation values for both Ea and log (A) point out the accuracy of the performed calculations and the noted differences could be assigned to the approximations used by the used models.

Fig. 8
figure8

Activation energy Ea and pre-exponential factor log (A) evolution associated with their confidence intervals with respect to reaction extent for pure NC

Fig. 9
figure9

Activation energy Ea and pre-exponential factor log (A) evolution associated with their confidence intervals with respect to reaction extent for NC-CuO–N

Table 1 Arrhenius parameters and reaction model for pure NC and NC-CuO–N composite

From the plot of both Ea and log (A), it could be noted that the activation energy and the pre-exponential factor have a similar tendency; hence, these values of Ea and log (A) could be correlated in a linear form equation well known as compensation effect (CE) following Eq. 4.

$$\ln A_{{\upalpha }} = aE_{{\upalpha }} + b$$
(4)

where In Aα, Eα are the Arrhenius parameters, whereas a, b correspond to the compensation parameters. Table 2 displays the compensation parameters as well as the corresponding correlation coefficients (R2) for TAS and VYA/CE integral methods using DSC data obtained at different heating rates. All the compensation plots are in linear form with a correlation coefficient (R2) greater than 0.99, confirming once again the good compensation between the activation energy and the pre-exponential factor.

Table 2 Compensation parameters obtained with TAS and VYA/CE for pure NC and NC-CuO–N composite

Figure 10 represents the evolution of the integral reaction model against the reaction extent α for NC and NC-CuO–N, (Fig. S3 for NC-CuO–S and NC-CuO–Cl) obtained by different kinetic methods. For all systems, the results show that the introduction of copper oxide nanoparticles have no effect on the NC thermal decomposition. Accordingly, the thermal decomposition process is classified as R2, contracting cylinder \(g\left( \alpha \right) = 1 - (1 - \alpha )^{\frac{1}{2}}\) with it-KAS and it-FWO methods, whereas with TAS integral model, it is ascribed to F1/3 and F3/4 chemical reaction \(g\left( \alpha \right) = 1 - (1 - \alpha )^{\frac{2}{3}}\), \(g\left( \alpha \right) = 1 - (1 - \alpha )^{\frac{1}{4}}\) or P1/2: Nucleation (Power low) \(g\left( \alpha \right) = \alpha^{\frac{1}{2}}\) for pure NC. It should be noted that the obtained reaction models are the most probable models according the used analytical technique and isoconversional methods and could be slightly different from the real reaction models. In addition, the reaction models could be changed with the NC nitrogen content [64, 82]. Chalouche et al. assumed a nucleation (parabolic law) for NC thermal decomposition [82]. In other works, Zhao assumed a Random nucleation mechanism (Avrami-Erofeev) for the NC decomposition, [38] whereas Benhammada et al. [37] attributed such process to a D4 three-dimensional diffusion (Ginstling–Brounshtein). However, the same integral reaction model (Ranndom nucleation (Avrami-Erofeev) has been found for NC-Al/Fe2O3 composites NC (N:w12.6% [43] and NC-Fe2O3 composite [38] and NC-Fe2O3 [37].

Fig. 10
figure10

Experimental reaction model g(α) evolution with respect to reaction extent for pure NC and NC-CuO–N composite

Besides, the VYA/CE method provides a numerical values of g(α) and f(α). Thus, the calculated f(α) using VYA/CE values were used to verify the accordance of the obtained triplet values with the experimental data. As shown in Fig. 11, the obtained data performed using VYA/CE are in good agreement with the experimental data, which confirm the consistency of the obtained kinetic triplet.

Fig. 11
figure11

Comparison of experimental values (lines) and calculated values (symbols) using numerical reaction model values f(α) obtained by VYA/CE method

Conclusions

The green synthesis of CuO nanoparticles has been successfully performed by a simple precipitation method using Malva sylvestris leaf aqueous extract as a capping agent and copper sulfate, copper nitrate, and copper chloride as copper precursors. It was found that the crystallite size of CuO NPs is within the range of 19–26 nm with lower value for CuO-S. A higher crystallinity is obtained for nanoparticles prepared using copper nitrate and chloride. The as-prepared CuO NPs exhibited the same chemical composition and structure with a spherical shape. The prepared NC–CuO composite is homogeneous and is characterized by a good compatibility according to the DSC analysis. Likewise, the added nanocatalysts could reduce the activation energy without sensibly affecting the peak temperature, whatever the copper precursor used.

Based on DSC analysis, the thermal behavior of NC and NC-CuO NPs composites has been investigated using four integral isoconversional methods performed at different heating rates. The results show that, regardless of the precursor nature, CuO NPs could be safely used as a catalyst for NC and both activation energy and pre-exponential factor have a similar tendency. Hence, the presence of CuO NPs decreases the activation energy by 7.75 kJ mol−1. The isoconversional kinetics of both NC and NC-CuO composite thermal decomposition process could be modeled by R2 Contracting cylinder \(g \, \left( \alpha \right) \, = 1 - (1 - \alpha )^{\frac{1}{2}}\) with it-KAS and it-FWO model. TAS integral model ascribe F3/4 chemical reaction \(g \, \left( \alpha \right) \, = 1 - (1 - \alpha )^{\frac{2}{3}}\).

References

  1. 1.

    Kumar PV, Shameem U, Kollu P, Kalyani R, Pammi S. Green synthesis of copper oxide nanoparticles using Aloe vera leaf extract and its antibacterial activity against fish bacterial pathogens. BioNanoScience. 2015;5(3):135–9.

    Article  Google Scholar 

  2. 2.

    Ahamed M, Alhadlaq HA, Khan M, Karuppiah P, Al-Dhabi NA. Synthesis, characterization, and antimicrobial activity of copper oxide nanoparticles. Journal of Nanomater. 2014;2014:17.

    Article  CAS  Google Scholar 

  3. 3.

    Hu Y, Yang S, Tao B, Liu X, Lin K, Yang Y, et al. Catalytic decomposition of ammonium perchlorate on hollow mesoporous CuO microspheres. Vacuum. 2019;159:105–11.

    CAS  Article  Google Scholar 

  4. 4.

    Ghosh D, Majumder S, Sharma P. Anticancerous activity of transition metal oxide nanoparticles. NanoBioMedicine. Singapore: Springer; 2020. p. 107–37.

    Google Scholar 

  5. 5.

    Zhang D, Cao C-Y, Lu S, Cheng Y, Zhang H-P. Experimental insight into catalytic mechanism of transition metal oxide nanoparticles on combustion of 5-Amino-1H-Tetrazole energetic propellant by multi kinetics methods and TG-FTIR-MS analysis. Fuel. 2019;245:78–88.

    CAS  Article  Google Scholar 

  6. 6.

    Yang C, Xiao F, Wang J, Su X. Synthesis and microwave modification of CuO nanoparticles: crystallinity and morphological variations, catalysis, and gas sensing. J Colloid Interface Sci. 2014;435:34–42.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Ibupoto Z, Tahira A, Raza H, Ali G, Khand A, Jilani N, et al. Synthesis of heart/dumbbell-like CuO functional nanostructures for the development of uric acid biosensor. Materials. 2018;11(8):1378.

    PubMed Central  Article  CAS  Google Scholar 

  8. 8.

    Sundar S, Venkatachalam G, Kwon S. Biosynthesis of copper oxide (CuO) nanowires and their use for the electrochemical sensing of dopamine. Nanomaterials. 2018;8(10):823.

    PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    Sun S, Zhang X, Zhang J, Wang L, Song X, Yang Z. Surfactant-free CuO mesocrystals with controllable dimensions: green ordered-aggregation-driven synthesis, formation mechanism and their photochemical performances. CrystEngComm. 2013;15(5):867–77.

    CAS  Article  Google Scholar 

  10. 10.

    Wang C, Li Q, Wang F, Xia G, Liu R, Li D, et al. Morphology-dependent performance of CuO anodes via facile and controllable synthesis for lithium-ion batteries. ACS Appl Mater Interfaces. 2014;6(2):1243–50.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Zhao Y, Mu S, Sun W, Liu Q, Li Y, Yan Z, et al. Growth of copper oxide nanocrystals in metallic nanotubes for high performance battery anodes. Nanoscale. 2016;8(48):19994–20000.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Zhou K, Wang R, Xu B, Li Y. Synthesis, characterization and catalytic properties of CuO nanocrystals with various shapes. Nanotechnology. 2006;17(15):3939.

    CAS  Article  Google Scholar 

  13. 13.

    Sivaraj R, Rahman PK, Rajiv P, Narendhran S, Venckatesh R. Biosynthesis and characterization of Acalypha indica mediated copper oxide nanoparticles and evaluation of its antimicrobial and anticancer activity. Spectrochim Acta Part A Mol Biomol Spectrosc. 2014;129:255–8.

    CAS  Article  Google Scholar 

  14. 14.

    Leventis N, Chandrasekaran N, Sadekar AG, Sotiriou-Leventis C, Lu H. One-pot synthesis of interpenetrating inorganic/organic networks of CuO/resorcinol-formaldehyde aerogels: nanostructured energetic materials. J Am Chem Soc. 2009;131(13):4576–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Chen L, Li L, Li G. Synthesis of CuO nanorods and their catalytic activity in the thermal decomposition of ammonium perchlorate. J Alloy Compd. 2008;464(1–2):532–6.

    CAS  Article  Google Scholar 

  16. 16.

    Alizadeh-Gheshlaghi E, Shaabani B, Khodayari A, Azizian-Kalandaragh Y, Rahimi R. Investigation of the catalytic activity of nano-sized CuO, Co3O4 and CuCo2O4 powders on thermal decomposition of ammonium perchlorate. Powder Technol. 2012;217:330–9.

    CAS  Article  Google Scholar 

  17. 17.

    Gacia PD, Shrestha LK, Bairi P, Sanchez-Ballester NM, Hill JP, Boczkowska A, et al. Low-temperature synthesis of copper oxide (CuO) nanostructures with temperature-controlled morphological variations. Ceram Int. 2015;41(8):9426–32.

    CAS  Article  Google Scholar 

  18. 18.

    Mallakpour S, Madani M. A review of current coupling agents for modification of metal oxide nanoparticles. Prog Org Coat. 2015;86:194–207.

    CAS  Article  Google Scholar 

  19. 19.

    Zhang Q, Zhang K, Xu D, Yang G, Huang H, Nie F, et al. CuO nanostructures: synthesis, characterization, growth mechanisms, fundamental properties, and applications. Prog Mater Sci. 2014;60:208–337.

    CAS  Article  Google Scholar 

  20. 20.

    Umar A, Alshahrani A, Algarni H, Kumar R. CuO nanosheets as potential scaffolds for gas sensing applications. Sens Actuators B Chem. 2017;250:24–31.

    CAS  Article  Google Scholar 

  21. 21.

    Rahnama A, Gharagozlou M. Preparation and properties of semiconductor CuO nanoparticles via a simple precipitation method at different reaction temperatures. Opt Quant Electron. 2012;44(6–7):313–22.

    CAS  Article  Google Scholar 

  22. 22.

    Tran TH, Nguyen VT. Copper oxide nanomaterials prepared by solution methods, some properties, and potential applications: a brief review. Int Sch Res Not. 2014;2014:1–14.

    Article  Google Scholar 

  23. 23.

    Hussain I, Singh N, Singh A, Singh H, Singh S. Green synthesis of nanoparticles and its potential application. Biotech Lett. 2016;38(4):545–60.

    CAS  Article  Google Scholar 

  24. 24.

    Gunalan S, Sivaraj R, Venckatesh R. Aloe barbadensis Miller mediated green synthesis of mono-disperse copper oxide nanoparticles: optical properties. Spectrochim Acta Part A Mol Biomol Spectrosc. 2012;97:1140–4.

    CAS  Article  Google Scholar 

  25. 25.

    Padil VVT, Černík M. Green synthesis of copper oxide nanoparticles using gum karaya as a biotemplate and their antibacterial application. Int J Nanomed. 2013;8:889.

    Google Scholar 

  26. 26.

    Gasparetto JC, Martins CAF, Hayashi SS, Otuky MF, Pontarolo R. Ethnobotanical and scientific aspects of Malva sylvestris L.: a millennial herbal medicine. J Pharm Pharmacol. 2012;64(2):172–89.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Barros L, Carvalho AM, Ferreira IC. Leaves, flowers, immature fruits and leafy flowered stems of Malva sylvestris: a comparative study of the nutraceutical potential and composition. Food Chem Toxicol. 2010;48(6):1466–72.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Zohra SF, Meriem B, Samira S, Muneer MA. Phytochemical screening and identification of some compounds from mallow. J Nat Prod Plant Resour. 2012;2(4):512–6.

    Google Scholar 

  29. 29.

    Esfanddarani HM, Kajani AA, Bordbar A-K. Green synthesis of silver nanoparticles using flower extract of Malva sylvestris and investigation of their antibacterial activity. IET Nanobiotechnol. 2017;12(4):412–6.

    Article  Google Scholar 

  30. 30.

    Govindarajan M, Hoti S, Rajeswary M, Benelli G. One-step synthesis of polydispersed silver nanocrystals using Malva sylvestris: an eco-friendly mosquito larvicide with negligible impact on non-target aquatic organisms. Parasitol Res. 2016;115(7):2685–95.

    PubMed  Article  PubMed Central  Google Scholar 

  31. 31.

    Chandran SP, Chaudhary M, Pasricha R, Ahmad A, Sastry M. Synthesis of gold nanotriangles and silver nanoparticles using Aloevera plant extract. Biotechnol Prog. 2006;22(2):577–83.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Awwad A, Albiss B, Salem N. Antibacterial activity of synthesized copper oxide nanoparticles using Malva sylvestris leaf extract. SMU Med J. 2015;2(1):91–101.

    Google Scholar 

  33. 33.

    Trache D, Khimeche K, Mezroua A, Benziane M. Physicochemical properties of microcrystalline nitrocellulose from Alfa grass fibres and its thermal stability. J Therm Anal Calorim. 2016;124(3):1485–96.

    CAS  Article  Google Scholar 

  34. 34.

    Zhang Y, Wang F, Gao K, Liu Y, Shao Z. Alcogel and aerogel of nitrocellulose formed in nitrocellulose/acetone/ethanol ternary system. Int J Polym Mater Polym Biomater. 2016;65(8):377–83.

    CAS  Article  Google Scholar 

  35. 35.

    Yang X, Li Y, Wang Y, Yang Y, Hao J. Nitrocellulose-based hybrid materials with T7-POSS as a modifier: effective reinforcement for thermal stability, combustion safety, and mechanical properties. J Polym Res. 2017;24(3):50.

    Article  CAS  Google Scholar 

  36. 36.

    He Y, He Y, Liu J, Li P, Chen M, Wei R, et al. Experimental study on the thermal decomposition and combustion characteristics of nitrocellulose with different alcohol humectants. J Hazard Mater. 2017;340:202–12.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Benhammada A, Trache D, Kesraoui M, Tarchoun AF, Chelouche S, Mezroua A. Synthesis and characterization of α-Fe2O3 nanoparticles from different precursors and their catalytic effect on the thermal decomposition of nitrocellulose. Thermochim Acta. 2020;686:178570.

    CAS  Article  Google Scholar 

  38. 38.

    Zhao N, Li J, Gong H, An T, Zhao F, Yang A, et al. Effects of α-Fe2O3 nanoparticles on the thermal behavior and non-isothermal decomposition kinetics of nitrocellulose. J Anal Appl Pyrol. 2016;120:165–73.

    CAS  Article  Google Scholar 

  39. 39.

    Benhammada A, Trache D, Kesraoui M, Chelouche S. Hydrothermal synthesis of hematite nanoparticles decorated on carbon mesospheres and their synergetic action on the thermal decomposition of nitrocellulose. Nanomaterials. 2020;10(5):968.

    CAS  PubMed Central  Article  Google Scholar 

  40. 40.

    Mahajan R, Makashir P, Agrawal J. Combustion behaviour of nitrocellulose and its complexes with copper oxide. Hot stage microscopic studies. J Therm Anal Calorim. 2001;65(3):935–42.

    CAS  Article  Google Scholar 

  41. 41.

    Wei W, Cui B, Jiang X, Lu L. The catalytic effect of NiO on thermal decomposition of nitrocellulose. J Therm Anal Calorim. 2010;102(3):863–6.

    CAS  Article  Google Scholar 

  42. 42.

    Zhang T, Zhao N, Li J, Gong H, An T, Zhao F, et al. Thermal behavior of nitrocellulose-based superthermites: effects of nano-Fe2O3 with three morphologies. RSC Adv. 2017;7(38):23583–90.

    CAS  Article  Google Scholar 

  43. 43.

    Zhao N, Li J, Zhao F, An T, Hu R, Ma H. Combustion catalyst: nano-Fe2O3 and nano-thermite Al/Fe2O3 with different shapes. Dev Combust Technol. 2016;2016:325.

    CAS  Google Scholar 

  44. 44.

    Li Y, Yang H, Hong Y, Yang Y, Cheng Y, Chen H. Electrospun nanofiber-based nanoboron/nitrocellulose composite and their reactive properties. J Therm Anal Calorim. 2017;130(2):1063–8.

    CAS  Article  Google Scholar 

  45. 45.

    Trache D, Donnot A, Khimeche K, Benelmir R, Brosse N. Physico-chemical properties and thermal stability of microcrystalline cellulose isolated from Alfa fibres. Carbohyd Polym. 2014;104:223–30.

    CAS  Article  Google Scholar 

  46. 46.

    Tarchoun AF, Trache D, Klapötke TM, Chelouche S, Derradji M, Bessa W, et al. A promising energetic polymer from posidonia oceanica brown algae: synthesis, characterization, and kinetic modeling. Macromole Chem Phys. 2019;220:1900358.

    CAS  Article  Google Scholar 

  47. 47.

    Phiwdang K, Suphankij S, Mekprasart W, Pecharapa W. Synthesis of CuO nanoparticles by precipitation method using different precursors. Energy Procedia. 2013;34:740–5.

    CAS  Article  Google Scholar 

  48. 48.

    Huang X, Ren Z, Zheng X, Tang D, Wu X, Lin C. A facile route to batch synthesis CuO hollow microspheres with excellent gas sensing properties. J Mater Sci Mater Electron. 2018;29(7):5969–74.

    CAS  Article  Google Scholar 

  49. 49.

    Zak AK, Majid WA, Abrishami ME, Yousefi R. X-ray analysis of ZnO nanoparticles by Williamson-Hall and size–strain plot methods. Solid State Sci. 2011;13(1):251–6.

    Article  CAS  Google Scholar 

  50. 50.

    Sahooli M, Sabbaghi S, Saboori R. Synthesis and characterization of mono sized CuO nanoparticles. Mater Lett. 2012;81:169–72.

    CAS  Article  Google Scholar 

  51. 51.

    Safa S, Azimirad R, Safalou Moghaddam S, Rabbani M. Investigating on photocatalytic performance of CuO micro and nanostructures prepared by different precursors. Desalin Water Treat. 2016;57(15):6723–31.

    CAS  Article  Google Scholar 

  52. 52.

    Abbasi MA, Khan Y, Hussain S, Nur O, Willander M. Anions effect on the low temperature growth of ZnO nanostructures. Vacuum. 2012;86(12):1998–2001.

    CAS  Article  Google Scholar 

  53. 53.

    Ider M. Elaboration et caractérisation des nanomatériaux à base de métaux nobles 2017.

  54. 54.

    García MA. Surface plasmons in metallic nanoparticles: fundamentals and applications. J Phys D Appl Phys. 2011;44(28):283001.

    Article  CAS  Google Scholar 

  55. 55.

    Das D, Nath BC, Phukon P, Dolui SK. Synthesis and evaluation of antioxidant and antibacterial behavior of CuO nanoparticles. Colloids Surf B. 2013;101:430–3.

    CAS  Article  Google Scholar 

  56. 56.

    Yang Z, Xu J, Zhang W, Liu A, Tang S. Controlled synthesis of CuO nanostructures by a simple solution route. J Solid State Chem. 2007;180(4):1390–6.

    CAS  Article  Google Scholar 

  57. 57.

    Zhang H, Shen C, Chen S, Xu Z, Liu F, Li J, et al. Morphologies and microstructures of nano-sized Cu2O particles using a cetyltrimethylammonium template. Nanotechnology. 2005;16(2):267.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Kim S, Umar A, Kumar R, Ibrahim AA, Kumar G. Facile synthesis and photocatalytic activity of cocoon-shaped CuO nanostructures. Mater Lett. 2015;156:138–41.

    CAS  Article  Google Scholar 

  59. 59.

    Siddiqui H, Qureshi M, Haque FZ. Effect of copper precursor salts: facile and sustainable synthesis of controlled shaped copper oxide nanoparticles. Optik. 2016;127(11):4726–30.

    CAS  Article  Google Scholar 

  60. 60.

    Wang H, Xie J, Yan K, Duan M. Growth mechanism of different morphologies of ZnO crystals prepared by hydrothermal method. J Mater Sci Technol. 2011;27(2):153–8.

    Article  Google Scholar 

  61. 61.

    Fan J, Tang D, Wang D. Spontaneous growth of CuO nanoflakes and microflowers on copper in alkaline solutions. J Alloy Compd. 2017;704:624–30.

    CAS  Article  Google Scholar 

  62. 62.

    Kushwaha A, Moakhar RS, Goh GK, Dalapati GK. Morphologically tailored CuO photocathode using aqueous solution technique for enhanced visible light driven water splitting. J Photochem Photobiol, A. 2017;337:54–61.

    CAS  Article  Google Scholar 

  63. 63.

    Momeni M, Mirhosseini M, Nazari Z, Kazempour A, Hakimiyan M. Antibacterial and photocatalytic activity of CuO nanostructure films with different morphology. J Mater Sci Mater Electron. 2016;27(8):8131–7.

    CAS  Article  Google Scholar 

  64. 64.

    Pourmortazavi S, Hosseini S, Rahimi-Nasrabadi M, Hajimirsadeghi S, Momenian H. Effect of nitrate content on thermal decomposition of nitrocellulose. J Hazard Mater. 2009;162(2–3):1141–4.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  65. 65.

    Chelouche S, Trache D, Tarchoun AF, Abdelaziz A, Khimeche K. Compatibility assessment and decomposition kinetics of nitrocellulose with eutectic mixture of organic stabilizers. J Energ Mater. 2020;38(1):48–67.

    CAS  Article  Google Scholar 

  66. 66.

    Katoh K, Ito S, Kawaguchi S, Higashi E, Nakano K, Ogata Y, et al. Effect of heating rate on the thermal behavior of nitrocellulose. J Therm Anal Calorim. 2009;100(1):303–8.

    Article  CAS  Google Scholar 

  67. 67.

    Trache D, Tarchoun AF. Stabilizers for nitrate ester-based energetic materials and their mechanism of action: a state-of-the-art review. J Mater Sci. 2018;53(1):100–23.

    CAS  Article  Google Scholar 

  68. 68.

    Bohn MA, Volk F. Aging behavior of propellants investigated by heat generation, stabilizer consumption, and molar mass degradation. Propellants Explos Pyrotech. 1992;17(4):171–8.

    CAS  Article  Google Scholar 

  69. 69.

    Benhammada A, Trache D. Thermal decomposition of energetic materials using TG-FTIR and TG-MS: a state-of-the-art review. Appl Spectrosc Rev. 2019;55:1–54.

    Google Scholar 

  70. 70.

    Chen S, He W, Luo C-J, An T, Chen J, Yang Y, et al. Thermal behavior of graphene oxide and its stabilization effects on transition metal complexes of triaminoguanidine. J Hazard Mater. 2019;368:404–11.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  71. 71.

    Nasibulin AG, Rackauskas S, Jiang H, Tian Y, Mudimela PR, Shandakov SD, et al. Simple and rapid synthesis of α-Fe2O3 nanowires under ambient conditions. Nano Research. 2009;2(5):373–9.

    CAS  Article  Google Scholar 

  72. 72.

    An T, Zhao F, Gao H, Ma H, Hao H, Yi J, et al. Preparation of super thermites and their compatibilities with DB propellants components. J Mater Eng. 2011;1(11):23–8.

    Google Scholar 

  73. 73.

    Agreement S. 4147: Chemical compatibility of ammunition components with explosives (non-nuclear applications). NATO Military Agency for Standardization. 2001.

  74. 74.

    Chen T, Du P, Jiang W, Liu J, Hao G, Gao H, et al. A facile one-pot solvothermal synthesis of CoFe2O4/RGO and its excellent catalytic activity on thermal decomposition of ammonium perchlorate. RSC Adv. 2016;6(87):83838–47.

    CAS  Article  Google Scholar 

  75. 75.

    Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC kinetics committee recommendations for performing kinetic computations on thermal analysis data. Thermochim Acta. 2011;520(1–2):1–19.

    CAS  Article  Google Scholar 

  76. 76.

    Vyazovkin S. Isoconversional kinetics of thermally stimulated processes. Berlin: Springer; 2015.

    Google Scholar 

  77. 77.

    Trache D, Abdelaziz A, Siouani B. A simple and linear isoconversional method to determine the pre-exponential factors and the mathematical reaction mechanism functions. J Therm Anal Calorim. 2017;128(1):335–48.

    CAS  Article  Google Scholar 

  78. 78.

    Coats AW, Redfern J. Kinetic parameters from thermogravimetric data. Nature. 1964;201(4914):68–9.

    CAS  Article  Google Scholar 

  79. 79.

    Doyle CD. Kinetic analysis of thermogravimetric data. J Appl Polym Sci. 1961;5(15):285–92.

    CAS  Article  Google Scholar 

  80. 80.

    Senum G, Yang R. Rational approximations of the integral of the Arrhenius function. J Therm Anal Calorim. 1977;11(3):445–7.

    Article  Google Scholar 

  81. 81.

    Chelouche S, Trache D, Tarchoun AF, Abdelaziz A, Khimeche K, Mezroua A. Organic eutectic mixture as efficient stabilizer for nitrocellulose: kinetic modeling and stability assessment. Thermochim Acta. 2019;673:78–91.

    CAS  Article  Google Scholar 

  82. 82.

    Chelouche S, Trache D, Tarchoun AF, Khimeche K. Effect of organic eutectic on nitrocellulose stability during artificial aging. J Energ Mater. 2019;37(4):387–406.

    CAS  Article  Google Scholar 

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Benhammada, A., Trache, D. Green synthesis of CuO nanoparticles using Malva sylvestris leaf extract with different copper precursors and their effect on nitrocellulose thermal behavior. J Therm Anal Calorim (2021). https://doi.org/10.1007/s10973-020-10469-5

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Keywords

  • Nanocatalyst
  • Green synthesis
  • Nitrocellulose
  • Thermal decomposition
  • Kinetics
  • CuO