Autocatalytic Model Research Articles

A novel bio-based autocatalytic amide-type phthalonitrile monomer: Synthesis, curing kinetics and thermal properties

A bio-based bisphenol compound (DFA) was prepared using bisphenolic acid and furfurylamine in biomass as raw materials. Then, a bio-based amide phthalonitrile monomer (DFAP) was obtained in an environmentally friendly solvent. Nuclear magnetic resonance and Fourier transform infrared spectroscopy (FT-IR) proved the successful synthesis of DFA and DFAP. The curing behavior and curing kinetics of the polymer were studied using FT-IR and differential scanning calorimetry. Calculate the activation energy using the isoconversion method. The SB(m, n) autocatalytic reaction model describing the curing process of poly(DFAP) was modified and fitted by introducing the variable activation energy model. The thermal stability, thermomechanical properties and processing properties of the resin were studied using technologies such as thermogravimetric analyzer, dynamic mechanical analyzer and rheometer. The results show that the prepolymer has a wide processing window and a low melt viscosity. Poly (DFAP) has a high glass transition temperature and excellent thermal stability.

Developing the optimized control scheme for digital light processing 3D printing by combining numerical simulation and machine learning-guided temperature prediction

Digital light processing (DLP) 3D printing has attracted significant attention for its rapid printing speed, high accuracy, and diverse applications. However, the continuous DLP printing process releases substantial heat, resulting in a swift temperature rise in the curing area, which may lead to printing failures. Due to the lack of effective means to measure real-time temperature changes of the curing surface during continuous DLP 3D printing, the prevailing approach is to predict temperature variations during printing via numerical simulation. Nevertheless, temperature prediction methods relying solely on numerical simulation tend to be slow and overlook heat exchange dynamics during printing, potentially resulting in prediction inaccuracies, particularly for complex models. To address these issues, this paper proposes a method to combine numerical simulation and a machine learning approach for temperature prediction in the DLP 3D printing process, along with a printing control scheme generation method. Firstly, the m+nth order autocatalytic kinetic model considering the light intensity and the Beer–Lambert law are employed to formulate the heat calculation equation for the photopolymer resin curing reaction. Subsequently, a heat exchange calculation equation is established based on Fourier heat conduction law and Newton’s cooling equation. A numerical simulation model for temperature changes during the printing process is then developed by integrating the heat calculation equation, heat exchange calculation equation, and measurement data from Photo-DSC. Furthermore, a temperature measurement device for the printing process is designed to validate the accuracy of the numerical simulation. Following this, an improved Long Short-term Memory (LSTM) network is proposed, using temperature change data generated by the numerical simulation model to train the network for rapid (2×10−4s/layer) prediction of temperature changes during printing. Finally, aiming for the shortest printing time, an optimized control scheme planning algorithm and a target function are designed based on the model’s temperature change data and the monomer’s flash point to ensure the temperature remains below this threshold. This algorithm can automatically generate the optimal printing control scheme for any model. Experimental results demonstrate that the proposed temperature prediction method can predict temperature variation accurately. Based on this, the generated printing control scheme can guarantee efficient and high-quality manufacturing for any model.

Performance Enhancement of Polyurethane Acrylate Resin by Urushiol: Rheological and Kinetic Studies.

A natural extract, i.e., urushiol, was employed to effectively cross-link and modify commercial wet-cured polyurethane acrylic resin. Comprehensive characterization of the paint film was performed using techniques such as FTIR, SEM, and TGA. The results indicated that the incorporation of urushiol significantly increased the cross-linking density of the resin, which in turn enhanced the film-forming properties, mechanical strength, and thermal stability of the paint film. Additionally, the study discovered that under isothermal conditions, the dynamic moduli (G' and G″) of the paint film are related to the gel point frequency by a power law, aligning with the predictions of percolation theory. The application of the autocatalytic model has provided a novel approach to studying non-isothermal kinetic reactions, offering valuable insights for process optimization and further development of urushiol-based polyurethane.

Development of a Cure Model for Unsaturated Polyester Resin Systems Based on Processing Conditions.

Unsaturated polyester resin (UPR) systems are extensively used in composite materials for applications in the transportation, marine, and infrastructure sectors. There are continually evolving formulations of UPRs that need to be evaluated and optimized for processing. Differential Scanning Calorimetry (DSC) provides valuable insight into the non-isothermal and isothermal behavior of UPRs within a prescribed temperature range. In the present work, non-isothermal DSC tests were carried out between temperatures of 0.0 °C and 250 °C, through different heating and cooling ramp rates. The isothermal DSC tests were carried out between 0.0 and 170 °C. The instantaneous rate of cure of the tested temperatures were measured. The application of an autocatalytic model in a calculator was used to simulate curing behaviors under different processing conditions. As the temperature increased from 10 °C up to 170 °C, the rate of cure reduced, and the heat of reaction increased. The simulated cure behavior from the DSC data showed that the degree of cure (α) maximum value of 71.25% was achieved at the highest heating temperature of 85 °C. For the low heating temperature, i.e., 5 °C, the maximum degree of cure (α) did not exceed 12% because there was not enough heat to activate the catalyst to crosslink further.

A Study of Hydroxyl-Terminated Block Copolyether-Based Binder Curing Kinetics.

In order to determine the curing reaction model and corresponding parameters of hydroxyl-terminated block copolyether (HTPE) and provide a theoretical reference for its practical application, the non-isothermal differential scanning calorimetry (DSC) method was used to analyze the curing processes of three curing systems with HTPE and N-100 (an aliphatic polyisocyanate curing agent), isophorone diisocyanate (IPDI), and a mixture of N-100 and IPDI as curing agents. The results show that the curing activation energy of N-100 and HTPE was about 69.37 kJ/mol, slightly lower than the curing activation energy of IPDI and HTPE (75.60 kJ/mol), and the curing activation energy of the mixed curing agent and HTPE was 69.79 kJ/mol. The curing process of HTPE conformed to the autocatalytic reaction model. The non-catalytic reaction order (n) of N-100 and HTPE was about 1.2, and the autocatalytic order (m) was about 0.3, both lower than those of IPDI and HTPE. The reaction kinetics parameters of the N-100 and IPDI mixed curing agent with HTPE were close to those of N-100 and HTPE. The verification results indicate a high degree of overlap between the experimental data and the calculated data.

Catalytic coagulation.

We introduce an autocatalytic aggregation model in which the rate at which two clusters merge is controlled by the third "catalytic" cluster, whose mass must equal the mass of one of the reaction partners. The catalyst is unaffected by the joining event and can participate in or catalyze subsequent reactions. This model is meant to mimic the self-replicating reactions that occur in models for the origin of life. We solve the kinetics of catalytic coagulation for the case of mass-independent reaction rates and show that the total cluster density decays as t^{-1/3}, while the density of clusters of fixed mass decays as t^{-2/3}. These behaviors contrast with the corresponding t^{-1} and t^{-2} scalings for classic aggregation. We extend our model to mass-dependent reaction rates, to situations where only "magic" mass clusters can catalyze reactions, and to include steady monomer input.

The effects of the contents of modified‐diatomite on the properties of modified‐diatomite/epoxy resin composites

AbstractExternal plasticization is one of the effective ways to toughen epoxy resins. In this study, an inorganic filler (EMD) was prepared by chemical modification of diatomite and was used to fill epoxy resin matrix. The effects of different amounts (5%, 10%, 15%, and 20%) of raw diatomite (RD) and modified‐diatomite (EMD) on the mechanical and thermal properties of epoxy resin composites were studied. The mechanical results showed that the impact strength, tensile strength, and bending strength of EMD/EP were higher than those of RD/EP. The impact strength of EMD‐15/EP reached 5.39 KJ/m2, which was 36.8% higher than that of RD‐15/EP. Thermogravimetric analysis showed that the thermal stability of EMD/EP was better than that of RD/EP. The results of DMA show that the glass transition temperature of EMD/EP was lower than that of RD/EP. Non‐isothermal differential scanning calorimetry study showed that the apparent activation energy of EMD‐15/EP was much lower than that of RD‐15/EP, which proved that EMD could promote the curing of epoxy resin. The Malek method was used to determine that both the RD‐15/EP and EMD‐15/EP curing systems were autocatalytic (Šesták–Berggren) models.

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

Numerical Simulation of Polyacrylamide Hydrogel Prepared via Thermally Initiated Frontal Polymerization.

Traditional polymer curing techniques present challenges such as a slow processing speed, high energy consumption, and considerable initial investment. Frontal polymerization (FP), a novel approach, transforms monomers into fully cured polymers through a self-sustaining exothermic reaction, which enhances speed, efficiency, and safety. This study focuses on acrylamide hydrogels, synthesized via FP, which hold significant potential for biomedical applications and 3D printing. Heat conduction is critical in FP, particularly due to its influence on the temperature distribution and reaction rate mechanisms, which affect the final properties of polymers. Therefore, a comprehensive analysis of heat conduction and chemical reactions during FP is presented through the establishment of mathematical models and numerical methods. Existing research on FP hydrogel synthesis primarily explores chemical modifications, with limited studies on numerical modeling. By utilizing Differential Scanning Calorimetry (DSC) data on the curing kinetics of polymerizable deep eutectic solvents (DES), this paper employs Malek's model selection method to establish an autocatalytic reaction model for FP synthesis. In addition, the finite element method is used to solve the reaction-diffusion model, examining the temperature evolution and curing degree during synthesis. The results affirm the nth-order autocatalytic model's accuracy in studying acrylamide monomer curing kinetics. Additionally, factors such as trigger temperature and solution initial temperature were found to influence the FP reaction's frontal propagation speed. The model's predictions on acrylamide hydrogel synthesis align with experimental data, filling the gap in numerical modeling for hydrogel FP synthesis and offering insights for future research on numerical models and temperature control in the FP synthesis of high-performance hydrogels.

Predicting method for self-heating temperature of chemical substances using isothermal DSC instead of wire basket test (EN15188)

Wire basket test is a hazard analysis method for the self-heating hazard of accumulated substances regulated in EN15188. However, this test requires a large amount of sample and long test period. To solve these issues, a new isothermal method to predict the self-heating temperature for any scale by obtaining pseudo activation energy of heat accumulation reaction using DSC has been developed. Governing equations at isothermal condition for nth-order reaction model and for autocatalytic reaction model were induced. From self-heating temperature at a scale obtained by wire basket tests and activation energy by DSC tests, the self-heating temperature can be easily predicted regardless of the accumulation scales.

Effect of Particle Diameter on Water Sorption by Rice Grains

AbstractWater sorption of non‐glutinous Uruchi and sticky Mochi rice grains of different diameters is kinetically analyzed using linear driving force and autocatalytic models. Water‐sorption curves are classified into two types based on their shape and according to the state of rice grains below and above the gelatinization temperature. Below the gelatinization temperature, the apparent activation energy and the frequency factor decrease with increasing rice‐particle diameter. The water sorption mechanisms of both types of rice appear to be the same in terms of the enthalpy–entropy compensation effect. Above the gelatinization temperature, it appears that the gelatinizable areas of starch granules contribute to the equilibrium moisture content, whereas the non‐gelatinizable areas affect the rate constant.

Enhancing RDX Thermal Decomposition in Al@RDX Composites with Co Transition Metal Interfacial Layer

In this study, an Al/Co@RDX composite was meticulously prepared through a combination of planetary high-energy ball-milling and a spray-drying technique. The thermal reactivity of these Al/Co@RDX composites was comprehensively investigated and compared using the TG/DSC technique. It is shown that the initial decomposition temperature of RDX in the DSC curve was decreased by 26.3 °C in the presence of Al/Co, which could be attributed to the nano-sized Co transition metal catalyzing the decomposition reaction of nitrogen oxides in RDX decomposition products. The decomposition peak temperature of RDX and the heat released by the thermal decomposition of RDX in the Al/Co@RDX composite were decreased by 26.3 °C and increased by 74.5 J·g−1, respectively, in comparison with those of pure RDX. The types of major gaseous products released from Al/Co@RDX were found to be identical to those of pure RDX, encompassing N2O, CH2O, CO2 and HCN. However, the concentrations of those gaseous products for Al/Co@RDX were higher than those observed for pure RDX, which may owe to the fact that the Al/Co composite can interact with the –CH2 and –NO2 within RDX molecules, which leads to the weakening of the C-N and N-N bonds. In addition, the decomposition of RDX in the Al/Co@RDX composite was observed as a one-step process with an apparent activation energy (Ea) of 115.6 kJ·cm−3. The decomposition mechanism of the RDX in the Al/Co@RDX composite was identified to follow the chain scission model (L2), whereas the two-step decomposition physical models observed for pure RDX were found to closely resemble the L2 and autocatalytic models.

Low viscosity and low temperature curing reactive POSS/epoxy hybrid resin with enhanced toughness and comprehensive thermal performance.

The mechanical and high-temperature resistance properties of epoxy resins cured at low temperatures (Tcuring ≤ 100 °C) are often inferior, and the most toughening modification methods for epoxy resins tend to compromise thermal resistance, which significantly limit the practical applications of it. Therefore, this work reported a low viscosity and low-temperature curing epoxy hybrid resin system (OPEP), adopting E-51 as a resin matrix, liquid anhydride (MHHPA) as a curing agent, tertiary amine (DMBA) as a curing accelerator, and reactive octa-epoxy terminated polyhedral oligomeric silsesquioxane (OG-POSS) as a toughening modifier. Results demonstrated that the OPEP system has excellent processability with low viscosity and long processing window period and satisfies the practical requirements of low-temperature curing. The OG-POSS exhibits superior compatibility and reactivity with the resin matrix, and its addition slightly reduces the Eα of the curing reaction and has a certain promotive effect on the curing of epoxy resin. In addition, the curing reaction rate of the OPEP resin complies with the Šesták-Berggren autocatalytic kinetics model. The impact strength, flexural strength, tensile strength, and elongation at break of the OPEP resin reached a maximum of 15.55 kJ m-2, 121.65 MPa, 90.36 MPa, and 2.48%, representing increases of 55.97%, 3.1%, 64.68%, and 26.51% compared to those of the pure resin, respectively. Notably, due to the heat-resistant inorganic silicon cage structure of OG-POSS, the thermal decomposition temperature (Td5), glass transition temperature (Tg), and heat distortion temperature (THDT) of the OPEP0.02 resin were 313.2 °C, 123.7 °C, and 102.0 °C, showing increases of 13.0 °C, 2.3 °C, and 6.8 °C compared to the pure resin, respectively, which is difficult to achieve for the general thermosetting resin toughening modification method. This research utilized organic-inorganic nanohybrid materials (POSS) to optimize the toughness and thermal stability of the resin in a coordinated manner, providing guidance for the preparation of high-performance epoxy resins that cure at low temperatures.

Modeling the curing kinetics of two thermoset adhesives under varying temperature conditions

AbstractThis paper presents detailed curing kinetics models for two thermoset adhesives. The cure kinetics were characterized using differential scanning calorimetry in both anisothermal and isothermal modes. The Sestak–Berggren autocatalytic model was applied to describe the anisothermal cure kinetics of the two adhesives with the Malek and undetermined coefficients methods determining their kinetic parameters. The Kamal autocatalytic model was adopted for the isothermal curing processes with the Kenny analytical‐graphical method determining the kinetic parameters. A modified Kamal model was developed by introducing a concept of the maximum degree of cure (DOC) and temperature‐depended kinetic parameters to describe the isothermal cure kinetics of the adhesive with a typical exothermic peak, and an extended Kamal model was further proposed by adding an initial‐phase‐control term to the modified Kamal model to describe the isothermal cure kinetics of the adhesive with two exothermic peaks. The results showed that the presented curing kinetics models with the determined parameters can precisely predict the evolutions of the DOC of the two thermoset adhesives in both anisothermal and isothermal modes.

Accelerating the kinetics of curing reaction of SBR/BR blend by silicon carbide via modification of thermal diffusivity

In this work, the accelerating effect of nano-silicon carbide (SiC) as a high thermal conductive filler on the kinetics of curing reaction of styrene-butadiene rubber/butadiene rubber (SBR/BR) as a compound for tire tread was investigated. The oscillating disk rheometer (ODR) showed that the SiC filler reduced the scorch and optimum curing times by ∼10%. The SiC amount of 5 phr caused an unparalleled reduction of ∼50% in the time difference between the scorch and curing times. The calculated parameters of the autocatalytic kinetic model of Kamal–Sourour using ODR results indicated that SiC accelerated the curing reaction by ∼1.25 times. To find the main mechanism of action of SiC in the improvement of vulcanization kinetic of SBR/BR-SiC, thermal diffusivity of the compound was detected in the thermalization process using a special setup during the curing and also the dethermalization process after that. The results exhibited that the SiC particles led to enhanced heat transfer rate into the compound during the vulcanization by ∼30% and into the cured composite by ∼50%, particularly at 5 phr of SiC. Moreover, fitting the heat transfer equation on the dethermalization data showed that the amount of 5 phr SiC in the recipe enhanced the thermal diffusivity by ∼97%, which can prevent the tire explosion while deriving due to the increment of the air temperature within the tire. The SiC filler can save significant energy in the manufacturing process of the tire tread, which has been neglected so far.

Cure kinetics, glass transition advancement and chemo-rheological modelling of an epoxy vitrimer based on disulphide metathesis

This study develops cure and chemo-rheological models for a vitrimeric system appropriate for use as matrix in continuous fibre composites based on a disulphide-endowed amine/epoxy formulation. The focus is on the processing conditions relevant to liquid composite moulding. Kinetics is investigated using Differential Scanning Calorimetry. An autocatalytic model is developed, simulating the reaction with an average reaction rate error of 5.2 % and degree of cure error of 3 %. The viscosity development is modelled at relatively low degrees of cure, focusing on the liquid moulding infusion window, yielding results with an average relative error of 4.8 %. The glass transition temperature advancement is represented by the Di Benedetto equation, whilst the topological transition temperature of the system is determined using stress relaxation experiments and found to be 149.5 °C. These results set the scene for the development of composites based on the matrix system investigated in this work.

The effect of cross-linking system and reinforcement on the cross-linking reaction of peroxide vulcanized ethylene-propylene-diene terpolymer (EPDM) matrix

In this work, the effect of using of dicumyl-peroxide (DCP) (2, 5 & 8 phr) and co-agent triallyl-cyanurate (TAC) (2, 4 & 6 phr) as cross-linking system as well as multi-walled carbon nanotubes (MWCNTs) (5, 8 και 10 phr) as a filler, for ethylene-propylene-diene terpolymer (EPDM) was investigated. Differential scanning calorimetry (DSC) was employed in order to study the progress of the cross-linking reaction, based on the exothermic vulcanization peaks of isothermal (at 160, 170, 180, 190 °C) and non-isothermal experiments (with heating rates 5, 10, 25, 50 °C min−1). The autocatalytic model was successfully applied to the results of isothermal experiments and the activation energy (Ea) of the reaction was calculated, based on the Arrhenius equation. The Ozawa-Kissinger equations were also applied to the data obtained from the non-isothermal study. From the results obtained from isothermal DSC experiments, an increase was observed in the reaction rates accompanied with a decrease in Ea with the increase of DCP content, whereas a proportional relationship between TAC content and the enthalpy and Ea of the reaction was recorded. The incorporation of MWCNT’s in EPDM increased its Ea. From the non-isothermal DSC experiments, a significant increase in the enthalpy and Ea of the cross-linking reaction was observed at higher DCP content, whereas TAC content did not show any notable effect on the reaction. Based on the results of this research, it is concluded that the increase of peroxide content facilitates the vulcanization of EPDM, whereas inhibition was observed by the incorporation of MWCNT’s.

Modeling of frontal polymerization of carbon fiber and dicyclopentadiene woven composites with stochastic material uncertainty

A novel finite element modeling framework is proposed for simulating dicyclopentadiene (DCPD) frontal polymerization in carbon fiber (CF) woven composites. A mesoscale CF/DCPD representative volume element (RVE) model was developed with two triggering directions, resulting in out-of-plane and in-plane DCPD polymerization. The Arrhenius equation coupled with a modified Prout-Tompkins autocatalytic model was used to evaluate the dynamic DCPD polymerization process. The rate and degree of DCPD cure were calculated by the material property uncertainties of DCPD with and without random void inclusions (3 % used as a reference in this work). The DCPD frontal polymerization in CF/DCPD composite was strongly influenced by the triggering direction. In the RVE model considered in this work, DCPD frontal polymerization was slightly faster in the in-plane (warp/weft yarn) directions than the out-of-plane (thickness) direction; polymerization occurs first in DCPD resin and is followed by CF/DCPD interface. Material property uncertainty and void inclusion had significant effects on both out-of-plane and in-plane DCPD polymerization. A large variation in DCPD material properties and the presence of void inclusions significantly delayed the rate and degree of DCPD frontal polymerization. This work provides the preliminary estimation of the frontal polymerization of DCPD-based composites and guides the structural applications of these materials.

Fraser-Suzuki function as an essential tool for mathematical modeling of crystallization in glasses

The performance of the Fraser-Suzuki function during mathematic deconvolution of crystal growth kinetic processes was extensively analyzed based on theoretical simulations. Regarding pure imitation, the Fraser-Suzuki function well describes processes with moderate negative asymmetry of a3 ≈ <−0.6; −0.2>. Considering the ability of the Fraser-Suzuki function to transfer the kinetic information during the mathematic deconvolution (i.e., performance in the procedure: kinetic signal → fit by Fraser-Suzuki function → kinetic analysis of the Fraser-Suzuki data-curve), it is very well suited for separating processes following single-exponent kinetic models such as the nucleation-growth Johnson-Mehl-Avrami-Kolmogorov model or the nth order reaction model. For the nth order autocatalytic model, the magnitude of errors depends directly on the exponent nNC. Reliable performance of the Fraser-Suzuki function is achieved when resulting nNC falls in <0; 1.2> interval. Combining the Fraser-Suzuki mathematic deconvolution with the consequent kinetic analysis utilizing the nth order autocatalytic model is highly recommended.

Catalytic effect of high thermal conductive SiC on the kinetics and thermodynamics of vulcanization reaction of SBR/BR-filled nano-SiC

Nano-silicon carbide (SiC) as a high thermal conductive material with an intrinsic thermal conductivity of ~ 490 W/m K was used to improve the cure characteristics, kinetics, and thermodynamics of curing reaction of styrene-butadiene rubber/butadiene rubber (SBR/BR) compounds. The considerations were carried out by non-isothermal differential scanning calorimetry (DSC). Results revealed that the presence of SiC shifted the peak and end temperatures of the curing peak to lower temperatures. The calculated activation energy of the curing reaction based on the Kissinger approach showed a descent from 409.8 to 93.8 kJ/mol by adding SiC from 0 to 7.5 phr (part per hundred rubber). Moreover, the obtained Gibbs free energy variation and equilibrium constant of the curing reaction proved that the reaction was absolutely forced and irreversible, which can be increasingly characterized as a one-way process. According to the results, SiC accelerated the curing reaction because of the increment of heat transfer into the compound. This phenomenon caused the increment of enthalpy variation of the vulcanization reaction, particularly at the SiC content of 5 phr. The achieved kinetic parameters via fitting an autocatalytic model based on the Sestàk–Berggren model by the Màlek method to describe the kinetics of the curing reaction indicated that the SiC filler had a catalytic effect on the curing reaction of SBR/BR-SiC, particularly after 2.5 phr of the filler.