This work presents the non-destructive assessment of polymeric composites based on synthetic matrices low-density polyethylene (LDPE) and polystyrene (PS) enhanced with chitosan (CS) biopolymer for use in active packaging systems for moisture control. Composites were prepared by incorporating CS at different contents (1, 3 and 5 phr) into LDPE and PS matrices. To evaluate the structural and thermal alterations induced by biopolymer loading, the materials were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and differential scanning calorimetry (DSC). The composites’ water-regulating properties—specifically, moisture absorption, retention, diffusion, and water vapor transmission rate—were quantitively tracked. Furthermore, the mechanical integrity of both dried and water-exposed systems was assessed via Shore D hardness testing. The results reveal a direct correlation between CS concentrations and enhanced hydrophilic behavior and water absorption, primarily attributed to the polar hydroxyl and amine groups within its molecular structure. The composites maintained adequate mechanical strength even after water exposure, confirming their structural stability for practical applications. This study demonstrates that the incorporation of CS into non-polar synthetic matrices significantly improves water affinity without requiring chemical compatibilizers, representing a cost-effective route to develop responsive packaging. The promise of these composites as responsive materials for real-time environmental interaction is highlighted by the successful non-destructive monitoring of their performance. This research establishes the feasibility and efficacy of non-destructive monitoring techniques in developing active packaging technologies, accelerating the progress of polymer-based systems with integrated and tunable moisture regulation capabilities.

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.

Background: Biodegradable poly (lactic acid)/poly (butylene adipate-co-terephthalate) (PLA/PBAT) blends suffer from poor interfacial adhesion and suboptimal mechanical performance, limiting their use in sustainable packaging. Methods: A PRISMA-compliant systematic meta-analysis of 34 peer-reviewed publications (2018–2025) was conducted. Inclusion criteria required studies reporting tensile, thermal, or barrier data on PLA/PBAT blends with nanocellulose ≥2 wt%. Heterogeneity was assessed using Cochran’s Q and I ² statistics; sensitivity analysis confirmed robustness. Data were normalized to a 70/30 PLA/PBAT reference composition using a validated linear mixing-rule protocol (R ² = 0.91), acknowledging that this approach may introduce systematic bias for properties exhibiting nonlinear composition dependence, particularly barrier properties. Results: Surface-modified TEMPO-oxidized CNFs at 5 wt% achieved the most significant improvements: tensile strength increased 80% (28.4 → 51.2 MPa), Young’s modulus increased 158% (1.2 → 3.1 GPa), thermal degradation onset temperature improved by 35 °C (295 → 330 °C), and water vapor permeability decreased 57% (15.2 → 6.5 g mm/m ² ·day at 100 μm reference thickness). Crystallinity increased from 32% to 55%, and activation energy for thermal degradation rose from 155.8 to 205.3 kJ/mol. Nanocellulose incorporation accelerated biodegradation under industrial composting conditions (ISO 14855-1, 58 °C, 60%–65% RH), reducing half-life from 12.0 to 7.9 weeks and increasing degradation (weight loss) to 86% at 12 weeks (CO ₂ -based mineralization efficiency: 81%). The tortuosity factor increased from 1.2 to 2.4, consistent with Nielsen’s tortuous path model. Conclusion: Surface-modified nanocellulose enables high-performance, fully biodegradable composites suitable for sustainable packaging, with biodegradation rates meeting the ISO 14855-1 threshold of EN 13432. Full certification additionally requires disintegration and ecotoxicity data not available in the present meta-analysis corpus. Performance is comparable to, or exceeds, that of synthetic nanoreinforcements (CNTs, organoclays) in independent studies—though direct comparison under identical conditions would be needed to establish definitive superiority—while offering full biodegradability.

This work explores how to improve additive manufacturing (AM) with PolyJet (PJ) technology and Digital ABS material. The study quantified the impact of PolyJet printing parameters on the properties of Digital ABS parts. Using a 3 × 2 factorial design, 90 specimens were manufactured varying orientation (0°, 45°, 90°) and layer thickness (16, 32 μm). The results showed that the 0° orientation with 16 μm thickness produced the highest tensile strength (55.2 ± 1.3 MPa) and lowest dimensional deviation (0.048 ± 0.004 mm). Mechanical anisotropy was significant, with a 31.5% reduction in strength for the 90° orientation. Statistical analysis confirmed that orientation explains 78.3% of the variability in strength (p < 0.001). In conclusion, the combination of PolyJet and Digital ABS is an excellent option for creating prototypes that require detail and durability, but printing horizontally and at high resolution is key, especially if tolerances are very tight.

Hoy en día, los empaques inteligentes y empaques activos están revolucionando la manera en que conservamos frutas y verduras, especialmente aquellas que siguen madurando después de ser cosechadas (climatéricas). Este artículo explica de forma sencilla cómo los empaques inteligentes permiten monitorear la frescura de los alimentos, mientras que los empaques activos ayudan a alargar su vida útil controlando su ambiente interno. También te contamos de qué materiales suelen estar hechos estos empaques —como plásticos especiales y bioplásticos— y cuáles son los principales factores que deterioran frutas y verduras. Además, exploramos investigaciones recientes que buscan mejorar los materiales para hacerlos aún más útiles, agregándoles propiedades antimicrobianas o antioxidantes. La combinación de estos dos tipos de empaques promete no sólo productos más frescos y seguros, sino también menos desperdicio de alimentos y mejores condiciones para productores y consumidores.

The purpose of this study was to rigorously evaluate the effectiveness of integrating conformal cooling channels, manufactured using Metal Additive Manufacturing (MAM), in injection molds for process optimization. The cooling phase, representing up to 80% of the molding cycle, was identified as the primary bottleneck. The methodology combined Finite Element Analysis (FEA) optimization with experimental validation. Mold inserts were designed and manufactured for a test piece: a control with traditional straight channels and an experimental insert with shaped channels. The experimental results show a significant reduction in cycle time of 45% in the mold with conformal cooling compared to the traditional mold. Additionally, the dimensional analysis of the parts revealed a decrease in warpage (differentiated deformation) of 32%, which is attributed to the uniformity of the temperature distribution achieved across the surface temperature. It was concluded that MAM is a critical enabling technology for the production of high-performance injection molds, and its application in the design of shaped channels is a fundamental strategy for Industry 4.0, substantially improving the productivity and quality of the final product.