The pyrolysis of polyethylene terephthalate (PET) is a well-known process for producing high fuel value. This paper aims to study the kinetics of PET pyrolysis reactions at 4 different heating rates (2, 5, 10, and 20 K min⁻¹) using thermogravimetric analysis (TGA) data. TGA data show only one kinetic reaction within the temperature ranges of 650 to 750 K. Five different model-free models, namely, the Freidman (FR), Flynn–Wall–Qzawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), and distributed activation energy model (DAEM), were fitted to the experimental data to obtain the activation energy (E_a) and the pre-exponential factor (A₀) of the reaction kinetics. The Coats–Redfern (CR) model equation was fitted with the help of master plot (Criado’s) to identify the most convenient reaction mechanism for this system. E_a’s values were determined by the application of the five aforementioned models and were found to possess an average value of 212 kJ mol⁻¹. The mechanism of PET pyrolysis reaction was best described by first-order reaction kinetics; this was confirmed by the compensation. Further thermodynamic parameter analysis indicated that the reaction was endothermic in nature.

Thermogravimetric Analysis (TGA) serves a pivotal technique for evaluating the thermal behavior of Polyvinyl alcohol (PVA), a polymer extensively utilized in the production of fibers, films, and membranes. This paper targets the kinetics of PVA thermal degradation using high three heating rate range 20, 30, and 40 K min⁻¹. The kinetic study was performed using six model-free methods: Freidman (FR), Flynn-Wall-Qzawa (FWO), Kissinger-Akahira-Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY) for the determination of the activation energy (E_a). TGA showed two reaction stages: the main one at 550–750 K and the second with 700–810 K. But only the first step has been considered in calculating E_a. The average activation energy values for the conversion range (0.1–0.7) are between minimum 104 kJ mol⁻¹ by VY to maximum 199 kJ mol⁻¹ by FR. Model-fitting has been applied by combing Coats–Redfern (CR) with the master plot (Criado’s) to identify the most convenient reaction mechanism. E_a values gained by the above six models were very similar with the average value of (126 kJ mol⁻¹) by CR. The reaction order models-Second order (F2) was recommended as the best mechanism reaction for PVA pyrolysis. Mechanisms were confirmed by the compensation effect. Finally, (∆H, ∆G, and ∆S) parameters were presented and proved that the reaction is endothermic.

Polylactic acid (PLA) is a biodegradable polymer and is mainly used in the textile and food packaging fields. The aim of this work is to build knowledge on the kinetics of the pyrolysis of PLA with the help of thermogravimetric analysis (TGA) using four model-free methods, namely Friedman, Flynn–Wall–Qzawa (FWO), Kissinger–Akahira–Sunose (KAS), and Starink. Additionally, two model-fitting methods (the Coats–Redfern and Criado methods) were applied. TGA data at 5, 10, 20, and 30 K/min heating rates were collected. The obtained activation energies of the pyrolysis of PLA at different conversions by the model-free models were in good agreement and the average values were 97, 109, 104, and 104 kJ/mol for Friedman, FWO, KAS, and Starink, respectively. The Criado model was used together with the Coats–Redfern model to identify the most appropriate reaction mechanism. As per this work, the best controlling reaction mechanism of the PLA pyrolysis can be expressed by the geometrical contraction model (R2).

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.

The buildup of abandoned plastics in the environment and the need to optimize agricultural waste utilization have garnered scrutiny from environmental organizations and policymakers globally. This study presents an assessment of the thermal decomposition of date seeds (DS), polypropylene homopolymer (PP), and their composites (DS/PP) through experimental measurements, machine learning convolutional deep neural networks (CDNN), and kinetic and thermodynamic analyses. The experimental measurements involved the pyrolysis and co-pyrolysis of these materials in a nitrogen-filled thermogravimetric analyzer (TGA), investigating degradation temperatures between 25 and 600 °C with heating rates of 10, 20, and 40 °C.min⁻¹. These measurements revealed a two-stage process for the bio-composites and a decrease in the thermal stability of pure PP due to the moisture, hemicellulose, and cellulose content of the DS material. By utilizing machine learning CDNN, algorithms and frameworks were developed, providing responses that closely matched ($R^2~0.942$) the experimental data. After various modelling modifications, adjustments, and regularization techniques, a framework comprising four hidden neurons was determined to be most effective. Furthermore, the analysis revealed that temperature was the most influential parameter affecting the thermal decomposition process. Kinetic and thermodynamic analyses were performed using the Coats–Redfern and general Arrhenius model-fitting methods, as well as the Flynn–Wall–Ozawa and Kissinger–Akahira–Sunose model-free approaches. The first-order reaction mechanism was identified as the most appropriate compared to the second and third order F-Series solid-state reaction mechanisms. The overall activation energy values were estimated at 51.471, 51.221, 156.080, and 153.767 kJ·mol⁻¹ for the respective kinetic models. Additionally, the kinetic compensation effect showed an exponential increase in the pre-exponential factor with increasing activation energy values, and the estimated thermodynamic parameters indicated that the process is endothermic, non-spontaneous, and less disordered.

Medical sutures are important surgical aids for promoting wound closure and establishing the ideal environment for wound healing. Several key factors must be considered in medical sutures, including the material of choice for the wound closure, the type of injury (internal or external), the mechanical support required to sustain the closure, the causes of infection, and the suture’s thickness and absorbability. Therefore, this study focuses on producing absorbable surgical sutures from a bio-compatible polymer material called polylactic acid (PLA) along with a PLA–chitosan composite suture initially using the extrusion method followed by the stretching method. The experimental results showed that the PLA suture can be successfully produced and coated with chitosan. The resulting suture elongated up to 148% with an achieved crystallinity of 27%, along with a superior surgical tying and knotting quality. The average thickness of the PLA sutures and PLA sutures coated with chitosan were found to be 0.33 mm and 0.58 mm, respectively. The efficient biocompatibility and wound healing/closure of the sutures were practically deep-rooted using a human skin simulator and rat animal tissue. Based on the degradation study, the manufactured suture in this study proved its degradability in physiological saline water. After a period of 15 days, the sutures lost 50% of their weight and the pH decreased from 6.49 to 4.42.