Thermogravimetric Analysis Data Research Articles

Failure evaluation of outdoor polymeric insulators based on statistical condition-assessment methods

Field aging of outdoor polymeric insulators results in unscheduled outages in power systems particularly in harsh climate conditions. In this paper, a strategic maintenance framework is proposed in order to prevent overhead line outages. The presented scheme is based on condition assessment of the real aged polymeric insulators. Data of tensile test, hardness test, thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) are used to extract critical features of the aged samples based on classification of fuzzy c-means clustering. Then, a lifetime prediction analysis is done based on Cox hazards analysis to develop further maintenance strategy. The proposed model is validated through numerical analyses on polymeric insulators for various validation evaluations including pollution severity analysis and hydrophobicity class (HC) measurement. Obtained results verify accuracy of the intelligent model to predict lifetime of the aged polymeric insulators. The presented strategic framework can be effectively applied to online monitoring, condition assessment and preventive maintenance programs of the power system utilities.

Kinetics study of pyrolysis of petroleum sludge and characterization of char

Thermochemical conversion (or pyrolysis) has emerged as an effective technique for the disposal of sludge generated in process industries. This paper reports physicochemical characterization and kinetic analysis of pyrolysis of sludge from a petroleum refinery and characterization of resultant char. The sludge had high volatile matter (>70 wt%) and low ash content (15 wt%). Pyrolysis of sludge was studied using a thermo-gravimetric analyzer (TGA). The TGA data was analyzed using two isoconversional models, viz. KAS and Vyazovkin AIC are used for the deduction of kinetic triplets, viz. activation energy, pre-exponential factor, and reaction mechanism. The activation energy (Ea ) of sludge pyrolysis varied in the 64.79–190.37 kJ/mol range. The pre-exponential factor varied from 1.18E + 07 to 4.29E + 12 s−1. The predominant solid-state mechanism of thermal conversion was an order-based reaction (F2/F3). The thermodynamic factors (viz. ΔH, ΔS, ΔG) of sludge pyrolysis were also determined. Relatively small values of (Ea – ΔH) ∼ 5 kJ/mol revealed high susceptibility of sludge for thermal conversion. Residual char after pyrolysis was characterized for physicochemical properties. The char properties were fixed carbon = 54.37%, calorific value = 22.48 MJ/kg, and BET surface area = 18.35 m2/g. These properties make char suitable for numerous applications.

Green synthesis of copper oxide nanoparticles using solanum melongena seeds extract and its applications in degradation of Rose Bengal dye, antibacterial, catalytic reduction and antioxidant activity

The generation of copper oxide (CuO) nanoparticles using a biogenic extract (solanum melongena seeds) was the primary focus of this study. As prepared catalyst has been utilized for dye degradation of Rose Bengal and studies on Antibacterial and Antioxidants. The catalyst was characterized for its structural aspects by using Scanning electron microscopy - energy dispersive spectroscopy (SEM-EDS), Powder X-ray Diffraction (PXRD), Fourier Transform - InfraRed spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), Brunauer-Emmett-Teller (BET) and UV–Visible DRS. The characterization results of XRD shows that the catalyst is in monoclinic phase. Copper Oxide is irregular in morphology and the average particle size is 47.97 nm which can be obtained using the SEM images and EDS spectra shows the Oxygen and Copper atoms presence which indicates the free from impurities of the fabricated biogenic CuO nanoparticles. FT-IR result indicated the stretching frequency of Cu–O appears at 487 cm−1. TGA data reveal that high-purity and thermally stable nanoparticles were fabricated, by the absence of significant weight losses at temperatures greater than 400 °C. Through UV–Visible DRS the calculate band gap was found to be 2.8eV. Based on the characterisation results the best catalyst was used to degrade the Rose Bengal dye within 60 min and also carried out the antibacterial and antioxidant activity which was found to be satisfactory when compared with their standard values.

Kinetic analysis of coal oxidative pyrolysis before and after immersion effect

The oxidative pyrolysis kinetics of coal before and after immersion effect was studied by thermogravimetric experiments. Non-isothermal thermogravimetric analysis data were analyzed at four heating rates of 5, 10, 20 and 30 K/min. Furthermore, temperature-programmed experiments and Fourier-transform infrared (FTIR) spectroscopy were employed to understand the oxidation activity and chemical structure of raw coal and soaked coal. After coal immersion effect, the increase in the content of active functional groups, the increase in the concentration of gaseous products, and the decrease in crossing point temperature during oxidation process all indicate that soaked coal is easier to oxidize and spontaneously combust. Lastly, four model-free methods such as Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), Friedman and Kissinger methods are employed to calculate the activation energy (Eα). Kinetic analysis reveals that Eα value obtained by the former three methods significantly depends on the conversion of coal oxidative pyrolysis process, and the average Eα value of soaked coal obtained by four methods is lower than that of raw coal. For the kinetic analysis of coal oxygen adsorption and combustion stages, it is more reliable to adopt the FWO and KAS methods in turn. This research helps to better understand the mechanism of enhanced oxidation activity of soaked coal and optimizes the calculation method of kinetic parameters in different oxidation stages of coal.

Synthesis, characterization, volatility, and thermal stability of fluorinated copper(II) aminoalkoxide complexes as potential vapour deposition precursors

The sodium aminoalkoxides Na[OCR(CF3)CH2NMeR′] {R = H, R′ = Me (1a); R = R′ = Me (1b); R = CF3, R′ = Me (1c); R = Me, R′ = Et (1d); R = CF3, R′ = Et (1e)} were synthesized by reaction of the corresponding fluorinated epoxide {OCH2CR(CF3); R = H (A), Me (B), CF3 (C)} with a secondary amine (HNMe2 or HNMeEt), followed by deprotonation with sodium hydride. Compounds 1a–e were isolated as white powders and characterized by combustion elemental analysis and NMR spectroscopy. Epoxides A and C are commercially available, while epoxide B was prepared by in situ deprotonation and alkylation of A. Reactions of 1a–e with copper(II) chloride in THF afforded [Cu{OCR(CF3)CH2NMeR′}2] {R = H, R′ = Me (2a); R = R′ = Me (2b); R = CF3, R′ = Me (2c); R = Me, R′ = Et (2d); R = CF3, R′ = Et (2e)}; compounds 2b and 2c have previously been reported but not structurally characterized, while 2a, 2d, and 2e are new to this report. All copper(II) compounds were characterized by combustion elemental analysis and single crystal X-ray crystallography. The physical properties of 2a–e were also evaluated using thermogravimetric analysis, melting point, and sublimation (at 5 mTorr) data, with comparison to previously reported non-fluorinated [Cu{OCHMeCH2NMe2}2] (III; [Cu(dmap)2]).

Development of a pyrolysis reaction model for epoxy based flame retardant composites: Relationship between pyrolysis behavior and material composition

This paper presents a methodology to develop a pyrolysis reaction model that captures the flammability behavior of epoxy-based intumescent flame retardant coatings (EP/IFR) as a function of material composition. This approach adopted Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), and Microscale Combustion Calorimetry (MCC) experiments as well as the inverse modeling of the experimental results using ThermaKin2Ds and COMSOL Multiphysics modeling framework. The interactive reactions between EP and IFR were identified and quantified in the resulting lumped model, which is based on fitting of experimental data. It was found that the developed model was able to reproduce the TGA/DSC/MCC data within the prescribed criteria for EP/IFR blends with different material compositions. The model’s extrapolating capability was further validated through accurately predicting the experimental TGA data under different thermal conditions and for blends with different IFR ratios which were not used for model development. Additional TG-FTIR and XPS experiments were conducted to quantify the effect of IFR additive on the evolution of gaseous products and char formation. With a higher ratio of IFR, the amount of fuel dilution gases (i.e., CO2, NH3) was increased and the amount of combustible organic gaseous products was reduced. This work provides the core subset parameters for comprehensive pyrolysis modeling and enables the intelligent design of EP/IFR coatings with enhanced flame resistance.

Uncertainty Evaluation of the TGA Oxidation Data

Thermogravimetric analysis (TGA) involves the measurement of the changes in mass that occur when a constant heat is applied to an unknown specimen as a function of time or temperature. It is used to evaluate thermal stability, oxidative stability, composition, and estimated lifetime, among other parameters. Given that accurate TGA data is important information for lifetime and stability evaluation of parts and products, an uncertainty evaluation procedure is required to ensure the reliability of the data. In this study, the uncertainty evaluation procedure was established according to the Guide to the Expression of Uncertainty in Measurement (GUM). The mathematically expression of the relationship between the measurand and the input quantities, evaluation of the standard uncertainty of each input estimate, determination of the combined standard uncertainty, and calculation of the expanded uncertainty were performed. The biggest factor affecting the uncertainty of TGA data is the uncertainty of correction factor caused by system calibration

Enhanced Dissolution and Bioavailability of Curcumin Nanocrystals Prepared by Hot Melt Extrusion Technology.

Curcumin nanocrystals (Cur-NCs) were prepared by hot melt extrusion (HME) technology to improve the dissolution and bioavailability of curcumin (Cur). Cur-NCs with different drug-carrier ratios were prepared by one-step extrusion process with Eudragit® EPO (EEP) as the carrier. The dispersed size and solid state of Cur in extruded samples were characterized by dynamic light scattering (DLS), scanning electron microscope (SEM), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). The thermal stability of Cur was analyzed by thermogravimetric analysis (TGA) and high performance liquid chromatography (HPLC). Dissolution and pharmacokinetics were studied to evaluate the improvement of dissolution and absorption of Cur by nano-preparation. Cur-NCs with particle sizes in the range of 50~150 nm were successfully prepared by using drug-carrier ratios of 1:1, 2:1 and 4:1, and the crystal form of Cur was Form 1 both before and after HME. The extrudate powders showed very efficient dissolution with the cumulative dissolution percentage of 80% in less than 2 min, and the intrinsic dissolution rates of them were 13.68 ± 1.20 mg/min/cm2, 11.78 ± 0.57 mg/min/cm2 and 4.35 ± 0.20 mg/min/cm2, respectively, whereas that of pure Cur was only 0.04 ± 0.00 mg/min/cm2. The TGA data demonstrated that the degradation temperature of Cur was about 250 °C, while the HPLC results showed Cur was degraded when extruded at the temperature over 150 °C. Pharmacokinetic experiment showed a significant improvement in the absorption of Cur. The Cmax of Cur in the Cur-NC group was 1.68 times that of pure Cur group, and the Cmax and area under the curve (AUC0-∞) of metabolites were 2.79 and 4.07 times compared with pure Cur group. Cur-NCs can be prepared by HME technology in one step, which significantly improves the dissolution and bioavailability of Cur. Such a novel method for preparing insoluble drug nanocrystals has broad application prospects.

Chemical Potential Analysis as an Alternative to the van't Hoff Method: Hypothetical Limits of Solar Thermochemical Hydrogen.

The van't Hoff method is a standard approach for determining reaction enthalpies and entropies, e.g., in the thermochemical reduction of oxides, which is an important process for solar thermochemical fuels and numerous other applications. However, by analyzing the oxygen partial pressure pO2, e.g., as measured by thermogravimetric analysis (TGA), this method convolutes the properties of the probe gas with the solid-state properties of the examined oxides, which define their suitability for specific applications. The "chemical potential method" is here proposed as an alternative. Using the oxygen chemical potential ΔμO instead of pO2 for the analysis, this method does not only decouple gas-phase and solid-state contributions but also affords a simple and transparent approach to extracting the temperature dependence of the reduction enthalpy and entropy, which carries important information about the defect mechanism. For demonstration of the approach, this work considers three model systems; (1) a generic oxide with noninteracting, charge-neutral oxygen vacancy defects, (2) Sr0.86Ce0.14MnO3(1-δ) alloys with interacting vacancies, and (3) a model for charged vacancy formation in CeO2, which reproduces the extensive experimental TGA data available in the literature. The reduction behavior of these model systems obtained from the chemical potential method is correlated with simulated results for the thermochemical water splitting cycle, highlighting the exceptional behavior of CeO2, which originates from defect ionization. The theoretical performance limits for solar thermochemical hydrogen within the charged defect mechanism are assessed by considering hypothetical materials described by a variation of the CeO2 model parameters within a plausible range.

Crystal structures, electron spin resonance, and thermogravimetric analysis of three mixed-valence copper cyanide polymers.

The crystal structures of three mixed-valence copper cyanide alkanolamine polymers are presented, together with thermogravimetric analysis (TGA) and electron spin resonance (ESR) data. In all three structures, a CuII moiety on a crystallographic center of symmetry is coordinated by two alkanolamines and links two CuICN chains via cyanide bridging groups to form diperiodic sheets. The sheets are linked together by cuprophilic CuI-CuI interactions to form a three-dimensional network. In poly[bis(μ-3-aminopropanolato)tetra-μ-cyanido-dicopper(I)dicopper(II)], [Cu4(CN)4(C3H8NO)2]n, 1, propanolamine bases have lost their hydroxyl H atoms and coordinate as chelates to two CuII atoms to form a dimeric CuII moiety bridged by the O atoms of the bases with CuII atoms in square-planar coordination. The ESR spectrum is very broad, indicating exchange between the two CuII centers. In poly[bis(2-aminopropanol)tetra-μ-cyanido-dicopper(I)copper(II)], [Cu3(CN)4(C3H9NO)2]n, 2, and poly[bis(2-aminoethanol)tetra-μ-cyanido-dicopper(I)copper(II)], [Cu3(CN)4(CH7NO)2]n, 3, a single CuII atom links the CuICN chains together via CN bridges. The chelating alkanolamines are not ionized, and the OH groups form rather long bonds in the axial positions of the octahedrally coordinated CuII atoms. The coordination geometries of CuII in 2 and 3 are almost identical, except that the Cu-O distances are longer in 2 than in 3, which may explain their somewhat different ESR spectra. Thermal decomposition in 2 and 3, but not in 1, begins with the loss of HCN(g), and this can be correlated with the presence of OH protons on the ligands in 2 and 3, which are not present in 1.

The Influence of Mo Content and Annealing on the Oxidation Behavior of Arc‐Melted Cr–xMo–8Si Alloys

The influence of Mo (10–40 at%) additions to Cr–8 at%Si is systematically studied by analyzing arc‐melted alloys in the as‐cast and annealed state. The oxidation resistance is tested by thermogravimetric analysis (TGA) at 1200 °C in air for up to 100 h. Samples are characterized by X‐ray diffraction, scanning electron microscopy, and wavelength‐dispersive X‐ray spectroscopy. The studied Cr–xMo–8Si alloy series shows a beneficial effect of Mo on the corrosion behavior. For all of the investigated Mo contents, no nitridation is observed and the oxide scale adhesion is improved. An increased amount of intermetallic A15 phase leads to the formation of a SiO2 scale beneath the Cr2O3 layer during high‐temperature exposure. The internal SiO2 layer inhibits further internal oxidation of the A15 phase. Uniform size and distribution of A15, benefits the formation of a duplex oxide scale. Cr–25Mo–8 at%Si, shows the most promising oxidation behavior. The TGA data follows the paralinear law, with both the growth rate, kp, and volatilization rate, kv, of Cr2O3 being reduced by adding 10–25 at% Mo. A binary Cr/Mo–Si phase diagram is generated in order to determine accurate annealing conditions and estimate the stability range of the metastable σ phase.

Evaluation of reaction rate of thermogravimetric analysis data using periodic sinc function interpolation

AbstractThe periodic sinc function interpolation offers a compelling solution to address the issue of noise in the analysis of thermogravimetric analysis (TGA) data, thereby enhancing the outcomes of differential techniques such as the Friedman isoconversional method. In this study, we introduce a novel approach that leverages the periodic sinc function interpolation to directly obtain smooth reaction rates from TGA data, eliminating the reliance on numerical differentiation methods. The efficacy of this method has been confirmed through its application to noisy experimental data derived from the thermal decomposition of various polymers, showcasing its robustness. Readers are provided with the corresponding code for Gnu Octave, serving as a free alternative to MATLAB. Additionally, the activation energies calculated from the experimental data using both the Friedman method and periodic sinc function interpolation closely align with those determined by the integral Vyazovkin method, emphasizing the validity and reliability of this new approach.

Novel approach for optimizing mechanical and damping performance of MABS composites reinforced with basalt fibers: An experimental study

The purpose of this research is to explore how basalt fibers, when compounded in specific proportions, impact the mechanical and damping attributes of methyl methacrylate acrylonitrile butadiene styrene (MABS). The fabrication process involved compounding basalt fibers in a twin-screw extruder at four distinct weight percentages: 5%, 10%, 15%, and 20%, with an MABS matrix. This study uniquely employs a comprehensive suite of characterization techniques including dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), X-ray microcomputed tomography (micro-CT), scanning electron microscopy (SEM), tensile tests, and density measurements to evaluate the composite's performance. The research significantly reveals that the integration of basalt fibers enhances the damping characteristics of MABS composites, as confirmed by DMA. Additionally, micro-CT scans provide unprecedented insights into the uniform distribution of basalt fibers within the MABS matrix, thereby elucidating the underlying mechanisms for the observed improvements. TGA data further bolsters the composite's thermal resilience, revealing its aptitude for high-temperature applications. Our findings establish a novel correlation between the basalt fiber weight percentage and the damping properties, revealing a non-monotonic relationship. This study thus not only augments the understanding of MABS based composites but also opens new avenues for the exploitation of basalt fibers in advanced composite materials, particularly in terms of their damping capabilities.

Study on enhancing waste PVC management through predictive Machine Learning analysis of TGA and its economic benefits

The onset of COVID-19 has led to a sudden surge of existing medical plastic wastes, majority of them made up of PVCs. Pyrolysis, a globally adopted waste management alternative, can be a crucial aid to handle the bulk plastic wastes which makes the identification of thermal behaviour of materials important to aid in the scale-up of pyrolytic processes. The study presents a novel approach of utilising regression-based algorithms- Support Vector Regression (SVR), Random Forest Regression (RFR) and K-Nearest Neighbour algorithms (KNN), to predict the weight loss % at a heating rate of 20 K/min for 6 different PVC based medical plastic wastes. The correlation between the factors affecting Thermogravimetric Analysis (TGA) have been identified using heat maps, the Machine Learning (ML) models were trained on this TGA data and the model efficiencies were identified using performance metrics of Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2. Several kinetic parameters like activation energy and reaction order were estimated using Coats - Redfern method followed by a cost analysis. The results showed that experimental and predicted weight loss were in good agreement with a regression R2 value > 0.97 for all the materials except the composite outer cover. Coats - Redfern method was successful in the estimation of kinetic parameters and the economic analysis indicated that the utilisation of ML in TGA analysis can have significant financial benefits for the concerned industry.

High temperature synthesis, surface characterization, and photoluminescence property of boron carbon nitride

The present study demonstrates the synthesis of boron carbon nitride (BCN) by molten salt method which utilizes melamine (source of carbon and nitrogen), boric acid (source of boron) and potassium chloride as the salt. The synthesis is performed at 915 °C for 4 h under N2-atmosphere. The synthesized material is further acid-treated. The physicochemical characterization data for as-synthesized and acid-treated materials confirm successful synthesis of BCN. SEM and AFM data display that synthesized BCN pre and post acid-treatment exhibit sheet-type morphology. Thermogravimetric analysis data confirm that the acid-treated material is thermally stable up to 800 °C, with only 2 % mass-loss is observed till 800 °C. Water contact angle measurement study demonstrates that acid-treatment significantly improves the surface hydrophilicity. Additionally, the low photoluminescence peak intensity of the acid-treated material indicates low electron-hole pair recombination rate. The synthesized material may be useful for the photocatalytic degradation of potentially toxic chemicals from water.

Crystal structure of the sodium salt of mesotrione: a triketone herbicide

The crystal structure of the sodium salt of mesotrione, namely, catena-poly[[sodium-μ3-2-[(4-methanesulfonyl-2-nitrophenyl)carbonyl]-3-oxocyclohex-1-en-1-olato] ethanol monosolvate], {[Na(C14H12NO7S)]C2H5OH} n , is described. The X-ray structural analysis results reveal that the coordination sphere is established by two chelating O atoms, the O atom of the coordinated ethanol molecule, and an O atom from the methylsulfonyl group of a neighboring molecule. Simultaneously, an O atom of the cyclohexane fragment serves as a bridge to a neighboring sodium ion, forming a flat Na–O–Na–O quadrangle, thereby forming a mono-periodic polymer. The structure displays O—H...O hydrogen bonds and C—H...O short contacts. Thermogravimetric analysis (TGA) data indicate that the sodium salt of mesotrione decomposes in four stages.

Machine Learning Backpropagation Prediction and Analysis of the Thermal Degradation of Poly (Vinyl Alcohol).

Thermogravimetric analysis (TGA) is crucial for describing polymer materials' thermal behavior as a result of temperature changes. While available TGA data substantiated in the literature significantly focus attention on TGA performed at higher heating rates, this study focuses on the machine learning backpropagation analysis of the thermal degradation of poly (vinyl alcohol), or PVA, at low heating rates, typically 2, 5 and 10 K/min, at temperatures between 25 and 600 °C. Initial TGA analysis showed that a consistent increase in heating rate resulted in an increase in degradation temperature as the resulting thermograms shifted toward a temperature maxima. At degradation temperatures between 205 and 405 °C, significant depths in the characterization of weight losses were reached, which may be attributed to the decomposition and loss of material content. Artificial neural network backpropagation of machine learning algorithms were used for developing mathematical descriptions of the percentage weight loss (output) by these PVA materials as a function of the heating rate (input 1) and degradation temperature (input 2) used in TGA analysis. For all low heating rates, modelling predictions were observably correlated with experiments with a 99.2% correlation coefficient and were used to interpolate TGA data at 3.5 and 7.5 K/min, indicating trends strongly supported by experimental TGA data as well as literature research. Thus, this approach could provide a useful tool for predicting the thermograms of PVA materials at low heating rates and contribute to the development of more advanced PVA/polymer materials for home and industrial applications.

Application of the orthogonal collocation method in evaluating the rate of reactions using thermogravimetric analysis data

The orthogonal collocation method offers a solution to the challenge of noise in thermogravimetric analysis data, improving upon the differential methods like the Friedman isoconversional method. This new technique requires only a few data points for interpolation to recreate conversion-time or conversion-temperature curves, from which a smooth reaction rate curve can be derived by differentiating polynomials. Remarkably, this method demands fewer than 20 data points for a precise estimation of reaction rates in single step reactions. The method's validity is supported by both simulated and actual reactions. Users can implement this technique using provided Gnu Octave (a free alternative to MATLAB) codes. Furthermore, the activation energy results of experimental data obtained from the Freidman and orthogonal collocation methods were in good agreement with those evaluated from the integral Vyazovkin and Ortega methods.

Polylactide-Based Nonisocyanate Polyurethanes: Preparation, Properties Evaluation and Structure Analysis.

This study investigated the successful synthesis and characterization of nonisocyanate polyurethanes (NIPUs) based on polylactide. The NIPUs were synthesized by a condensation reaction of oligomers with hard segments (HSs) and synthesized carbamate-modified polylactic acid containing flexible segments (FSs). The oligomers with HSs were prepared from phenolsulfonic acid (PSA) or a mixture of PSA and hydroxynaphthalenesulfonic acid (HNSA), urea and formaldehyde. The mixing of oligomeric compounds with different amounts of formaldehyde was carried out at room temperature. Obtained NIPU samples with different hard segment content were tested for their mechanical and thermal properties. The tensile strength (TS) of all NIPU samples increased with an increasing amount of HSs, attaining the maximum value at an HS:FS ratio of 1:3. Samples prepared from PSA and HNSA showed higher tensile strength (TS) without significant change in elongation at break compared to the samples based only on PSA. Thermogravimetric analysis data indicated an absence of weight loss for all samples below 100 °C, which can be considered a safe temperature for using NIPU materials. Maximum degradation temperatures reached up to 385 °C. Fourier transform infrared spectroscopy results confirmed the existence of expected specific groups as well as the chemical structure of the prepared polyurethanes. DSC analysis showed the existence of two characteristic phase transitions attributed to the melting and crystallization of hard segments in the NIPU samples.

Thermal degradation evaluation of polyethylene terephthalate microplastics: Insights from kinetics and machine learning algorithms using non-isoconversional TGA data

The thermal stability of polyethylene terephthalate (PET) microplastics (MPs) is crucial in diverse applications, necessitating a comprehensive understanding of their behavior under high temperatures. This study explores the impact of chemical modifications and aging on the thermal degradation of different PET MPs, including pristine (Pr-PET), acid-modified (Mod-PET), and oxidatively aged (Ag-PET), using kinetics, thermodynamics, and machine learning algorithms using non-isoconversional thermogravimetric analysis (TGA) data. The calculated activation energy (E) and pre-exponential factor (A) for Coats-Redfern model showed Pr-PET with highest E value of 21.09 and 15.24 kJ/mol for zero-order and first order kinetic model, indicating its increased energy requirement for chemical reactions, while Mod-PET and Ag-PET demonstrated improved thermal resistance with lower E values. Thermodynamic data from kinetic parameters showed positive values of Gibbs free energy (ΔG°), enthalpy (ΔH°) and negative values of entropy (ΔS°) (−0.17. kJ/mol·K to −0.20 kJ/mol·K) indicated non-spontaneous degradation process, necessitated energy input, and reduced disorder with decreased reactivity. For accurate predictions of thermal behavior, various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Regressor (DTM), Random Forest Regressor (RFM), and Artificial Neural Network (ANN), were utilized. The RFM consistently outperformed other models, exhibiting the lowest error metrics and providing reliable predictions of weight loss and derivative thermogravimetric (DTG) for different PET MPs with a predicted-R2 of 0.999. This study sheds light on the thermal stability of PET MPs, considering chemical modifications and aging. The integration of these methods offers a powerful approach to understanding and predicting PET MPs under thermal conditions.