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Artigo

Analysis of thermal decomposition kinetics and thermal hazard assessment of nitrobenzoic acid isomers by dsc and thermogravimetric method

Xin Yi Li; Zhi Xiang Xing*; Ye Cheng Liu; Yang Cheng; An Chi Huang

School of Safety Science and Engineering, Changzhou University, 213164 Jiangsu, China

Received: 09/07/2024
Accepted: 10/23/2024
Published online: 12/06/2024

Endereço para correspondência

*e-mail: xingzhixiang@cczu.edu.cn

RESUMO

Nitroaromatic acids, ubiquitous intermediates in nitro compounds, find broad applications in pharmaceuticals and fine chemical industries, yet their safety often receives inadequate attention. This paper investigates the thermal decomposition process of three isomers of nitrobenzoic acid using differential scanning calorimetry (DSC) and thermogravimetric analysis (TG), complemented by calculations of the apparent activation energy using typical kinetic methods. The reaction type was verified using a nonlinear fitting method. Thermal safety parameters including the time to maximum rate (TMR), critical temperature of self-acceleration (TCL), and thermal risk index (TRI) were employed to elucidate the thermal hazards. The thermal decomposition mechanism was further explained using density functional theory (DFT) methods, focusing on the Mayer bond order. The findings revealed that the average apparent activation energies for p-nitrobenzoic acid (PNBA), m-nitrobenzoic acid (MNBA), and o-nitrobenzoic acid (ONBA) were 157.00, 203.43, and 131.31 kJ mol-1, respectively. A single n-order reaction characterized the thermal decomposition process. The TMR and TCL parameters indicated a decomposition tendency at high temperatures, with the thermal stability at elevated temperatures following the order: PNBA < ONBA < MNBA. The TRI parameter indicates the danger level of the nitrobenzoic acids. The thermal decomposition mechanism was elucidated using Mayer bond energies, offering valuable insights into storage and transportation, and contributing to developing predictive models for thermal characteristics.

Palavras-chave: nitrobenzoic acid substances; differential scanning calorimetry; thermal risk; safe storage; density functional theory.

INTRODUCTION

Numerous chemical production and pharmaceutical industry accidents have been reported due to the thermal instability of specific compounds or process streams. Ensuring safety in chemical laboratories when handling hazardous substances is paramount, given their high reactivity and potential for explosions.1 In the production of fine chemicals and chemical processes, attention must be paid to the stability of nitro-containing chemicals to prevent accidents caused by thermal runaway. In the fine chemical industry, nitroaromatic compounds are often used as intermediates,2,3 with downstream products like 2,4-nitroanisole being utilized in the production of TKX-50 melt cast explosives.4-6 The wide application and high demand for nitro compounds, particularly their primary derivative p-nitrobenzoic acid (PNBA), underscore the importance of increasing the yield of downstream projects. PNBA, along with its isomers o-nitrobenzoic acid (ONBA) and m-nitrobenzoic acid (MNBA), plays a critical role in the production of various pharmaceuticals, dyes, and other chemical formulations. The thermal stability of nitrobenzoic acids is of significant importance in the production of energetic materials and melt-cast explosives, necessitating further research into their safety.

Extensive research by various scholars has been conducted on the thermokinetics of substances. Yang et al.7 used differential scanning calorimetry (DSC) and thermogravimetric analysis (TG) techniques to investigate the thermal hazards of nitration waste, calculating the apparent activation energy and employing multiple linear regression to explain that the thermal decomposition process of nitration waste consists of three exothermic peaks, corresponding to autocatalytic, and nth-order reactions, respectively. In another study, Yang et al. 8 conducted thermal and thermokinetic analyses on nitrocellulose, finding that divalent chlorides inhibit the thermal decomposition of nitrocellulose. Liu et al. 9 explored the thermal stability of typical aromatic nitrophenol by-products, including 2,4,6-trinitrophenol, 2,4,6-trinitroresorcinol, and 1,3,5-trihydroxy-2,4,6-trinitrobenzene, obtaining their thermokinetic parameters through calorimetric means. Cusu et al. 10 investigated the thermal decomposition of ethyl aldehyde-2,4-dinitrophenylhydrazone and propyl aldehyde-2,4-dinitrophenylhydrazone, suggesting that the decomposition follows an autocatalytic reaction, and determined this using models provided by Netzsch Thermokinetics software, including model-fitting isoconversional integral and differential models, and differential Kissinger and integral Flynn-Wall-Ozawa (FWO) methods.

The application of density functional theory (DFT) in the analysis of pyrolysis mechanisms has gained traction in recent years. Kim et al. 11 investigated the thermal decomposition mechanism of lithium methyl carbonate in the solid electrolyte interphase layer of lithium-ion batteries, delineating the sequential steps involved in its pyrolysis. Chen et al. 12 employed DFT methods to study the characteristic temperatures and heat release properties during the thermal decomposition of benzoyl peroxide, shedding light on the intricate thermal behavior of this compound. Yang et al. 13 probed into the thermal decomposition and explosion mechanism of the insensitive explosive 2,4-dinitroanisole, utilizing DFT calculations to elucidate the underlying processes governing its thermal instability. These studies underscore the utility of DFT in unraveling complex thermal decomposition pathways and mechanisms in various energetic materials and chemical compounds.

Determining the reaction kinetics model is one of the most important steps in study of chemical reactions, crucial for optimizing reactions and evaluating their thermal hazards.10,14,15 Using non-isothermal experiments with thermal analysis techniques such as DSC and TG, the apparent activation energy of nitrobenzoic acid substances was calculated using five typical integral and differential methods to determine the thermokinetic parameters during their thermal decomposition process.16-18 Using a nonlinear method, it was determined that the thermal decomposition process of the three nitrobenzoic acids follows a single n-order reaction, with an assessment of the thermal hazards provided to guide safe storage and transportation. The pyrolysis mechanism of nitrobenzoic acid was analyzed using DFT methods, further confirming that the pyrolysis process is of a single nth-order reaction type. This research contributes significantly to the understanding and safe handling of nitrobenzoic acids in various industrial applications.

 

EXPERIMENTAL

Sample preparation

The raw materials required for synthesizing PNBA include 4-nitrotoluene (CAS No. 99-99-0), sodium hypochlorite solution (CAS No. 7681-52-9), catalyst 2,2-bipyridine nickel chloride (CAS No. 22775-90-2), and the solvent acetonitrile solution (CAS No. 75-05-8) purchased from Shanghai Aladdin Biochemical Technology Co. , Ltd. (Shanghai, China). The synthesis of nitrobenzoic acid involves replacing methyl groups with carboxyl groups. Similarly, the synthesis of the other two isomers of nitrobenzoic acid was also carried out using a similar method, with the raw materials being m-nitrotoluene (CAS No. 99-08-1) and o-nitrotoluene (CAS No. 88-72-2). The specific implementation steps of the synthesis process and the color change of the solution are shown in Figure 1.

 

 

Characterization method

Scanning electron microscopy (SEM) is one of the commonly used characterization methods.19-21 The microstructure of the three substances was characterized using SEM (Hitachi Regulus 8100, Japan), and gold spraying treatment was performed at a magnification of 100 microns.

TG22,23 and DSC24,25 are high-precision tools used to study the thermokinetic behavior of substances. TG reflects the relationship between the mass loss of a substance and temperature, while DSC reflects the relationship between heat flow and temperature. By analyzing the curves obtained from these techniques, the thermal behavior of the substances can be determined. Commonly used thermal analysis techniques include TG and DSC.

DSC is an effective method for evaluating the potential hazards of substances and generating thermal decomposition curves of reactive chemicals. The principle of DSC analysis involves measuring the voltage difference between the crucible and the reference sensor, and then converting it into heat flow using calibration functions. DSC 3 (Mettler Toledo, Switzerland) was used to test the thermodynamic behavior of the three nitrobenzoic acids, and the relationship between temperature and heat flow related to phase transitions was determined. Gold-plated crucibles were used, and heat flow versus time curves were obtained at different heating rates (β). According to the regulations of the International Association for Thermal Analysis,26 the optimal setting of the heating rate should meet the requirement of a maximum-to-minimum ratio greater than or equal to 5 and less than or equal to 10. The DSC experiments were conducted at β of 1.0, 2.0, 3.0, 5.0, and 8.0 ºC min-1. A 25 μL high-pressure resistant gold-plated crucible was used as the experimental carrier, and a small portion of the sample was placed in the crucible, with a weight of 3.5 ± 0.1 mg. The gas atmosphere was nitrogen, and the flow rate was stable at 50.0 mL min-1.7,27 To ensure the reliability of the data, the experiment was repeated three times. The thermal flow curves of p-nitrobenzoic acid, m-nitrobenzoic acid, and o-nitrobenzoic acid enclosed in a sealed gold-plated crucible can reflect the heat absorption, heat release, and thermal decomposition behaviors of the substances.

The TGA 2, a thermogravimetric analyzer produced by Mettler-Toledo, Switzerland, was used in the thermogravimetric experiment. The TG was conducted using an alumina crucible as the experimental carrier for the sample. A small portion of the sample, weighing 3.5 ± 0.2 mg, was taken and placed in open 70 μL alumina crucibles.28 The experiment was carried out under a nitrogen atmosphere with a flow rate of 50 mL min-1 and a heating rate of 1, 2, 3, 5 and 8 ºC min-1, with a temperature range of 30-400 ºC. To enhance the trustworthiness of the experiment, it was replicated on multiple separate occasions.

Five typical kinetic methods and the nonlinear regression method

In the process of kinetics research, non-isothermal kinetics has been studied by many scholars because of its advantages. The equation of non-isothermal heterogeneous kinetics can be expressed as Equation 1:

where α is the degree of transformation (dimensionless), f(α) is the mechanism function of different reaction stages, T is the temperature of sample (ºC), β is the heating rate (ºC min-1) and k is the reaction rate constant (dimensionless).

The conversion rate can be expressed as Equation 2:

where m0 is the initial mass of the sample and mt is the mass of the sample at time t.

The relationship between the reaction rate k and the thermodynamic temperature T can be expressed by the Equation 3:

where A is the pre-exponential factor (s-1), Ea is the apparent activation energy (kJ mol-1) and R is the universal gas constant (8.314 J mol-1 K-1).

For a given conversion rate α, the reaction mechanism function f(α) can be given by the formula f(α) = (1 - α)n, which is substituted into the equation to obtain the Equation 4:

Five typical dynamical methods described below are derived from the formula, namely Kissinger, Kissinger-Akahira-Sunose (KAS), Starink, Vyazovkin, and FWO methods.

Kissinger and Kissinger-Akahira-Sunose kinetic methods

The Kissinger kinetic model29 is based on the Arrhenius model. Kissinger proposed a commonly used method for calculating the apparent activation energy (Ea) from the peak temperature of the DTA curve. The Kissinger equation is given by the Equation 5:

where k is the reaction rate constant (dimensionless), Tp is the temperature (ºC) corresponding to the maximum heat flow, A is the pre-exponential factor (s-1) and R is the universal gas constant (8.314 J mol-1 K-1).

The KAS kinetic model30-32 is an improvement on the Kissinger kinetic model for determining the Ea of a substance. The KAS method is based on the approximation of the Coats-Redfern method, given by the Equation 6:

The KAS equation, obtained by combining equations, is given by the Equation 7:

In the above equation, g(α) represents the integral form of the reaction mechanism function, and Tα corresponds to the temperature associated with the conversion rate α.

Starink kinetic model

Starink suggests that by discussing the KAS method and the FWO method, it can be represented by a general formula. The Starink kinetic model33,34 is used to calculate Ea and obtain thermodynamic results. The Starink kinetic method can be represented as:

where C is a constant, and represents an approximation of the temperature integral. In this study, the values of i and C are 1.80 and 1.0037, respectively.

Vyazovkin kinetic model

The Vyazovkin kinetic model35 is used to determine the Ea during chemical processes. This method is commonly employed in the study of chemical processes.

where tα,t represents the time required to achieve different conversion rates.

Flynn-Wall-Ozawa kinetic model

The FWO model36 is a widely used integral transformation method for calculating the Ea in various processes. The FWO equation can be simplified to the Equation 10, and by plotting the vertical axis as lg β and the horizontal axis as Tp-1, the Ea can be determined.

In this equation, β represents the heating rate, R is the universal gas constant (8.314 J mol-1 K-1), Tα corresponds to the temperature associated with the conversion rate α and G(α) represents the integral form of the reaction mechanism function.

Nonlinear regression method

The nonlinear regression method is further used to simulate the experimental and simulated values of the thermal decomposition process to further determine the reaction type of the thermal decomposition of the substance. The integration of dynamic assessment techniques and nonlinear model parameter estimation methods applies to diverse categories of kinetic models with varying levels of complexity. Moreover, this approach has the potential to yield more dependable estimations of dynamic parameters under exceptional scenarios. The inference of the exact reaction stage and type of the chemical reaction can be achieved using the fitting results of multivariate nonlinear simulations of various thermal decomposition reactions, utilizing the self-catalytic reaction model and n-order reaction model.

Thermodynamic parameters and thermal hazard assessment

According to the study of Wang et al. ,37 time to maximum rate (TMR) of n-order reaction can be expressed as Equation 11:

where q is the corresponding heat release rate (W kg-1) at the temperature T, Ea is the activation energy (kJ mol-1), and Cp is the specific heat of the reaction mass (J kg-1 K-1).

Thermal stability is determined by the time it takes for a chemical substance to reach a certain conversion level at a constant temperature to reach the critical temperature of self-acceleration (TCL).38 In addition, we calculate the thermal risk index (TRI),39 as shown in Equations 12-14, to assess the thermal risk of the substance.

where ρ is a measure of the severity of the reaction, ΔH is the heat release range (J g-1) and ε is a measure of the probability of reaction occurrence.

The lower the value of TRI, the less reactivity occurs when the substance is decomposed, and the lower the thermal risk. The hazard of three nitrobenzoic acid substances was assessed according to the order of the thermal hazard index: rank 1 for TRI < 1; rank 2 for 1 ≤ TRI < 2; rank 3 for 2 ≤ TRI < 3; rank 4 for TRI ≥ 3.

 

RESULTS AND DISCUSSION

Characterization result

The three synthesized nitrobenzoic acid substances were subjected to microscopic morphology characterization, chemical group characterization, and thermal analysis to observe the degree of synthesis and purity. It can be observed that the three synthesized nitrobenzoic acid substances have fundamentally different colors and appearances. 4-Nitrobenzoic acid is a yellow powder that adheres to the weighing paper and beaker wall when weighed. 3-Nitrobenzoic acid is a white granular solid, while 2-nitrobenzoic acid is a yellow solid that is prone to clumping and has a distinct aromatic odor.

It was found in Figure 2 that the PNBA surface has an elliptical and granular morphology, whereas the MNBA surface displays a rather smooth texture, and the ONBA surface exhibits a scaly appearance. Further research will be conducted on the chemical functional groups contained in the three substances. The three substances essentially have different positions of nitro functional groups, Fourier transform infrared spectroscopy (FTIR, Thermo Scientific Nicolet iS50, USA) will be used to compare and study the nitro groups contained in the three substances. The peaks marked in Figure 2 represent the antisymmetric and symmetric stretching absorption bands of the nitro functional group, located at 1650-1500 and 1370-1250 cm-1, respectively.

 

 

Thermal decomposition analysis of nitrobenzoic acid

All three nitrobenzoic acid substances exhibit significant exothermic behavior, the range of heat of decomposition (ΔHd) values is 327.05 to 1003.98 J g-1. Specifically, PNBA exhibits significantly higher decomposition heat values than other substances at each β. At a β of 1 ºC min-1, the decomposition heat of PNBA reached 1003.98 J g-1, significantly higher than other isomers, especially the corresponding value of ONBA. From Figure 3, it can be observed that although ONBA has a higher starting thermal decomposition temperature (T0), it has a smaller heat release compared to PNBA. At a β of 5.0 ºC min-1, m-nitrobenzoic first reaches the endothermic peak, and the temperatures at which m-nitrobenzoic and o-nitrobenzoic reach the molten state are similar, transitioning from solid to molten state, while ONBA first reaches the decomposition temperature. The decomposition heats of the three nitrobenzoic acid substances are 335.61, 458.62, and 542.27 J g-1. According to Figure 4 and Tables 1 and 2, with the increase in β, the T0, Tp, and Tf (final temperatutre) of the reaction all shift towards higher temperatures. This is because there is a lack of heat exchange between the substance and the environment.

 

 

 

 

 

 

 

 

The non-isothermal mass loss of substances in a nitrogen environment is shown in Figure 5. According to the mass loss obtained from TG testing, the three types of nitrobenzoic acids exhibit the same mass loss at different β. The peak decomposition temperatures of PNBA, m-nitrobenzoic, and o-nitrobenzoic are 205, 187, and 196 ºC. The temperature ranges at which p-nitrobenzoic, MNBA, and ONBA reach their maximum mass loss rate at a β of 1.0 ºC min-1 is 150-210, 125-190, and 120-200 ºC. Furthermore, five typical thermodynamic methods were used to quantitatively analyze nitrobenzoic acid.

 

 

Analysis of the thermal decomposition process of nitrobenzoic acid by linear regression equation

Thermokinetic investigation was carried out on three compounds, employing many widely employed linear fitting techniques including the Kissinger equation, KAS equation, Starink, Vyazovkin, and FWO kinetic models. The Ea was computed using these models, and the most appropriate model and method were chosen for the calculation. The coefficient of determination (R2) is a crucial metric in the linear regression equation that assesses the adequacy of the linear regression line. A value closer to one signifies a stronger fit.

In the kinetic results fitted by the Kissinger equation, the relationship between the Tp corresponding to different β for the three substances is shown in Figure 6. The results of the Kissinger fitting indicate that the average value of Ea ranges from 149.734 to 186.581 kJ mol-1, with R2 reaching above 0.89 and an average R2 of 0.9303, indicating a good fit. To compare the goodness of fit of the Kissinger model, the KAS kinetic model was proposed based on this, and different α values were taken, including 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, and 0.99. The more detailed the division of conversion rates, the more accurately the kinetic parameters can be reflected. Furthermore, a comparative analysis was conducted on the Starink model, Vyazovkin kinetic model, and FWO model, employing identical conversion rates. In general, the findings indicate that the Kissinger model exhibits a satisfactory level of accuracy. Additionally, doing a comparative analysis with alternative models such as KAS, Starink, Vyazovkin, and FWO, incorporating comprehensive conversion rates, can yield significant insights into the thermodynamic characteristics of the compounds being investigated.

 

 

Due to uncontrollable factors causing data errors in the experimental process, significant deviations in Ea data are observed at both ends, especially in cases of low conversion rates. The Starink kinetic model, Vyazovkin kinetic model, and FWO kinetic model all exhibit significant deviations at both ends. We select data with consistent Ea and fitting correlation at the same conversion rate for the kinetic model fitting. We choose a conversion rate α within an appropriate range, where Ea is relatively stable. We selected α to be applied to PNBA substances at a range of 0.2-0.7, α to be applied to MNBA substances at a range of 0.3-0.95, and α to be applied to ONBA substances at a range of 0.4-0.8.

Using the different linear regression equations mentioned earlier for substance analysis. Table 3 provides a detailed list of the Ea and correlation coefficients (R2) calculated by the Kissinger, KAS, Starink, Vyazovkin, and FWO methods for PNBA, MNBA, and ONBA. The fitting degree of each kinetic method is good. The linear fitting results obtained by each method for the three substances are shown in Figures 7, 8, and 9. Based on the radar plot depicted in Figures 10a and 10b, Kissinger technique had greater variations in the calculated Ea values compared to the other four fitting methods, which demonstrated a more balanced distribution.

 

 

 

 

 

 

 

 

 

 

Nonlinear regression analysis of several nitrobenzoic acid substances

Compared with linear fitting methods, nonlinear regression analysis can predict the reaction process of thermal decomposition of nitrobenzoic acid substances. Use nonlinear fitting methods to fit the thermal decomposition kinetics parameters of nitrobenzoic acid substances. Research has found that the thermal decomposition process of nitrobenzoic acid is an n-order reaction. As shown in Figure 11, taking MNBA as an example, the simulated values match well with the experimental values, indicating that the thermal process of nitrobenzoic acid decomposition has an n-order reaction stage. The average Ea generated in the simulation is comparable to the Ea generated in the other four mathematical models (as shown in Figure 12), further verifying the rationality of the model.

 

 

 

 

Thermal hazard parameter evaluation

To ensure the reliability of parameters, multiple linear fitting methods are compared with nonlinear regression analysis methods. Using an n-order model to measure the TCL, TMR,40,41 and other parameters of PNBA, MNBA, and ONBA to evaluate the thermal safety of storage. The thermal decomposition characteristics of substances can be predicted through nonlinear regression fitting.

The thermodynamic parameters of TCL, TMR for several nitrobenzoic acid substances are analyzed as shown in Figure 13.

 

 

The TCL curves of three nitrobenzoic acid substances indicate that PNBA and ONBA will decompose for less than one day at temperatures above 225 ºC. If the amount of stored substances is not large, this decomposition can be controlled. For large warehouses or vehicles, the dangerous consequences of the decomposition of nitrobenzoic acid substances cannot be estimated, and it is also prone to triggering the storage and transportation of surrounding chemicals, resulting in continuous domino effects. The TMR diagrams of three nitrobenzoic acid substances show 11 sets of initial temperature TMR. When the temperature exceeds 165 ºC, the reaction time drops sharply. Therefore, to prevent thermal runaway, it is necessary to avoid storage in a high-temperature environment and avoid thermal runaway in a short period. The comparison of the thermal hazard parameters of the three substances shows that when the nitro group of the energetic substance is in the intermediate position, the temperature required to reach the maximum conversion rate is higher than the other two substances, with PNBA < ONBA < MNBA in sequence. This may be attributed to the effect of the position of the nitro group on the thermal decomposition properties of the substance.

The calculation results are shown in Table 4. In comparison, PNBA and MNBA have higher risk ranks than ONBA and belong to rank 2. The effect of the position of nitro group on the thermal hazard of the substance was further explained. Therefore, the study of the effect of nitro position on thermal hazard should start from the study of the mechanism of thermal decomposition of substances.

 

 

Research on thermal decomposition mechanism

This article uses the DFT method in Gaussian software42 for geometric structure analysis, and uses Multiwfn software43 to calculate the Mayer bond order. Optimize the use of B3LYP functional, 6-31G (d,p) basis set.

As can be seen in Figure 14, the smallest bond order of o-, m- and p-nitrobenzoic acids are 0.752, 0,780 and 0,783, respectively. During the pyrolysis reaction, the NO2 group is broken first, and then the O-H bond in COOH is broken, and then the C-H and C-O bonds on the benzene ring are broken successively.

 

 

CONCLUSIONS

In the process of thermal stability analysis, thermal analysis methods such as TG and DSC can obtain the relevant kinetic parameters. In combination with Kissinger, KAS, Starink, Vyazovkin, FWO and other linear regression analysis methods, the nitrobenzoic acid reaction process can be effectively simulated. DSC shows that nitrobenzoic acid has only one exothermic stage at 250-400 ºC, and the initial order of exothermic temperature is ONBA < MNBA < PNBA. The range of ΔH is 335.61-542.27 J g-1. The TG curve shows that weight loss is significant at 125-200 ºC, which is due to the weight loss caused by chemical bond breaking and nitro group detachment during the pyrolysis reaction.

The results of Kissinger, FWO, KAS and nonlinear kinetic models are consistent, indicating that the thermal decomposition reaction of nitrobenzoic acid is a single n-order reaction. The analysis of TCL, TMR, TER and other thermal safety parameters shows that nitrobenzoic acid is easy to decompose at high temperatures, and the storage temperature should not exceed 165 ºC. The classification of TRI also indicates that the thermal risk index of PNBA and MNBA is higher. Furthermore, the bond level of nitrobenzoic acid was calculated from the point of view of quantum chemistry, and the mechanism of pyrolysis process was predicted, and the predicted situation was the same as that measured by thermal analysis instrument.

 

ACKNOWLEDGMENTS

The authors thank the Key Research and Development Program of China (2021YFC3001203, 2022YFB4002803), the Jiangsu Province Graduate Research Innovation Program (SJCX23_1591) and technology support from Yunlin University of Science and Technology.

 

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Associate Editor handled this article: Eduardo H. S. Sousa.

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