Polypyrrole adhesion to indium–tin oxide electrodes was improved by adding pre-hydrolyzed alkoxysilanes to the electrodeposition media. The pyrrole oxidation and film growth rates were studied by potentiostatic polymerization in acidic media. The morphology and thickness of the films were studied by contact profilometry and surface-scanning electron microscopy. The bulk and surface semiquantitative chemical composition was studied by Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy. Finally, the adhesion was studied by scotch-tape adhesion test, where both alkoxysilanes showed a significant improvement in adhesion. We proposed a hypothesis for the improvement in adhesion, that involves the formation of siloxane material as well as in situ surface modification of the transparent metal oxide electrode.

In this work, we report the effect of combining styrene-vinyl tetrazole copolymer (StVTz) and ammonium polyphosphate (APP) on the thermal degradation, mechanical properties, flame retardancy, and char formation of low-density polyethylene with ethyl vinyl acetate (LDPE/EVA) composites. The tetrazole heterocycle exhibits high thermal stability (>200 °C), and during its thermal decomposition, it releases non-toxic nitrogen gas. Its degradation generates reactive species capable of cross-linking the polymer chains, thereby promoting the formation of a protective char layer. To evaluate the influence of composition on the intumescent flame-retardant (IFR) properties of LDPE/EVA blends, different concentrations of APP and StVTz additives were incorporated. The composites were prepared in an internal mixer (Brabender Intelli-Torque Plasti-Corder). Test specimens were obtained by compression molding and subsequently cut into appropriate shapes for each analysis. Thermal stability was studied by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Mechanical properties were evaluated by tensile testing. Morphology of cone calorimetry (CC) residues was examined using SEM. Flammability properties, studied using CC, revealed a 70% reduction in the peak heat release rate (pHRR) and a 48% reduction in the total heat release (THR) compared to the neat LDPE/EVA blend. These results indicate that StVTz and APP act synergistically to improve the flame-retardant properties of LDPE/EVA.

Optimizing the injection molding of short-fiber-reinforced thermoplastics (SFRTs) is a persistent challenge due to the complex interplay between processing parameters and final mechanical performance. To address this, we developed and validated a machine learning (ML) pipeline to maximize both the tensile strength and Charpy impact resistance in polyamide 6 with 30% glass fiber (PA6-GF30). Through a designed experimental campaign, we systematically varied four key process parameters—melt temperature (260–300 °C), injection pressure (600–1000 bar), packing pressure (400–800 bar), and cooling time (15–35 s). The resulting dataset was used to train and compare three different regression models: Random Forest (RF), Gradient Boosting (GB), and Support Vector Regression (SVR). Our findings indicate that the Gradient Boosting (GB) algorithm yielded the most reliable predictions, significantly outperforming the other evaluated models. Further analysis using SHAP (Shapley Additive exPlanations) identified packing pressure as the dominant factor influencing tensile strength (contributing approximately 40% to the prediction), while melt temperature emerged as the key driver for impact resistance (around 35% contribution). By integrating our best-performing GB model with a multi-objective genetic algorithm, we identified an optimal set of parameters that simultaneously enhances both mechanical properties. Among the evaluated models (Random Forest, Support Vector Regression, and Gradient Boosting), the Gradient Boosting algorithm achieved the highest predictive accuracy. Compared to the baseline condition (280 °C melt temperature, 800 bar injection pressure, 600 bar packing pressure, 25 s cooling time), experimental validation of these optimized settings demonstrated substantial improvement: tensile strength increased from 145 MPa to 171 MPa (an 18% enhancement), and impact resistance rose from 45 kJ/m² to 55 kJ/m² (a 22% gain). This work establishes that an integrated ML and optimization framework can serve as a transformative approach for high-precision manufacturing of advanced engineering polymers. The primary novelty of this work lies in the development of a fully integrated, bias-free methodological framework that explicitly couples physical interpretability with multi-objective optimization, bridging the critical gap between black-box predictions and actionable industrial insights.

Los compuestos poliméricos reforzados con nanopartículas conductoras surgen como una estrategia prometedora para superar la baja conductividad térmica de estos materiales, típicamente en el rango de 0.1 a 0.5 W·m-1·K-1. En este contexto, la incorporación de nanopartículas como grafeno, nanoplaquetas, nanotubos de carbono, partículas metálicas y cerámicas permite mejorar propiedades termofísicas como la conductividad y difusividad térmica, la capacidad calorífica y la estabilidad térmica. Se analizan los mecanismos de transporte de calor, incluyendo la formación de redes conductoras, la resistencia térmica interfacial y el umbral de percolación. Asimismo, se evalúa la influencia de métodos de síntesis como la polimerización in situ, el mezclado en solución y en fundido sobre la dispersión de las nanopartículas y las interacciones interfaciales. Finalmente, se abordan retos como la aglomeración, la escalabilidad y los costos, así como perspectivas futuras orientadas al desarrollo de materiales multifuncionales con aplicaciones en gestión térmica. Este trabajo presenta una revisión actualizada de las innovaciones más recientes en este campo.