João Manuel Maia
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
João Manuel Maia has a total of 232 co-authors. Here are the top co-authors by number of shared publications:
- José António Covas
Department of Polymer Engineering, University of Minho, 4800-058 Guimarães, Portugal
16 shared publications - Ricardo Jorge Espanhol Andrade
Universidade Presbiteriana Mackenzie/ Instituto Mackenzie de Pesquisa em Grafeno e Nanotecnologias- Mackgraphe
11 shared publications - Jorge Silva
University of Minho
10 shared publications - Ana Vera Machado
Department of Polymer Engineering, Institute for Polymers and Composites (IPC), University of Minho, 4804-533 Guimarães, Portugal
9 shared publications - Olga Sousa Carneiro
Institute for Polymers and Composites (IPC) University of Minho Guimarães Portugal
9 shared publications - Shaghayegh Khani
Department of Macromolecular Science and Engineering, Case Western Reserve University, 2100 Adelbert Road, Cleveland, Ohio 44106, United States
9 shared publications
Latest Publications
- 6 October 2026
- Published by Wiley in Journal of Applied Polymer Science
- p. e71610
Influence of Two Different Solution Mixing Processes on the Mechanical, Viscoelastic, and Rheological Properties of PA6 /Graphene Nanocomposites
In this research, nanocomposites of polyamide 6 (PA6) reinforced with different contents of graphene nanoplatelets (GNP) were developed. Initially, the GNP was dispersed in a PA6 matrix through the solution mixing process, and two distinct precipitation methods were employed during the solvent removal step to subsequently obtain the masterbatches: emulsion precipitation and precipitation. Afterward, the PA6/GNP nanocomposites were prepared by a melt‐blending process, where 5% of the masterbatch was diluted in PA6. The produced materials were characterized through mechanical, thermal, rheological, and morphological tests. The results revealed that GNP reinforcement enhanced mechanical properties, particularly tensile strength, flexural strength, and flexural modulus, up to 26.8%, 19%, and 30.3%, respectively. Additionally, dynamic mechanical analysis demonstrated improvements of the nanocomposites in storage modulus at 23°C and 100°C, heat deflection temperatures, and strain resistance at 100°C and 200°C. Therefore, the results highlighted that the addition of graphene nanoplatelets and the precipitation method used are relevant to the nanocomposite properties.
- 6 October 2026
- Published by Wiley in Journal of Applied Polymer Science
- p. e71610
Influence of Two Different Solution Mixing Processes on the Mechanical, Viscoelastic, and Rheological Properties of PA6 /Graphene Nanocomposites
In this research, nanocomposites of polyamide 6 (PA6) reinforced with different contents of graphene nanoplatelets (GNP) were developed. Initially, the GNP was dispersed in a PA6 matrix through the solution mixing process, and two distinct precipitation methods were employed during the solvent removal step to subsequently obtain the masterbatches: emulsion precipitation and precipitation. Afterward, the PA6/GNP nanocomposites were prepared by a melt‐blending process, where 5% of the masterbatch was diluted in PA6. The produced materials were characterized through mechanical, thermal, rheological, and morphological tests. The results revealed that GNP reinforcement enhanced mechanical properties, particularly tensile strength, flexural strength, and flexural modulus, up to 26.8%, 19%, and 30.3%, respectively. Additionally, dynamic mechanical analysis demonstrated improvements of the nanocomposites in storage modulus at 23°C and 100°C, heat deflection temperatures, and strain resistance at 100°C and 200°C. Therefore, the results highlighted that the addition of graphene nanoplatelets and the precipitation method used are relevant to the nanocomposite properties.
- Open Access
Graph neural network for multitask prediction of rheological and microstructural behavior in suspensions
Fast prediction of suspension rheology is fundamental for optimizing process efficiency and performance in numerous industrial settings. However, traditional simulations are computationally demanding due to explicit evaluation of contact networks and stress tensors in dense regimes approaching shear thickening and jamming. This study presents a microstructure-informed multitask learning framework based on the graph neural network (GNN) that learns an implicit mapping between particle configurations and emergent microstructural and rheological properties of suspensions. This model simultaneously predicts particle pressure $Π$, viscosity $η$, and friction coordination $Z_μ$, in a dynamic steady-state, without explicit knowledge of interparticle forces. Here, semi-dilute to dense suspension systems in 2D were simulated across a wide range of shear stresses $σ$, spanning continuous, discontinuous shear thickening, and shear-jamming conditions. The trained models demonstrated high correlation coefficients ($R^2$ = 0.99) with narrow mean absolute error for packing fractions up to $φ\le φ_J^μ$ for all predictive targets. However, prediction scatter increases near jamming conditions, attributed to inherent fluctuations in suspension behavior as the critical packing fraction is approached, yet predictions remain in excellent agreement, closely following the trend of the simulated flow curves across stress evolution. Once trained, the model can infer rheological responses directly from structural topology, avoiding explicit stress evaluation during prediction. The approach yields computationally efficient mesoscale surrogates for accelerated simulation with potential for real-time exploration of particulate suspension behavior.Open AccessGraph neural network for multitask prediction of rheological and microstructural behavior in suspensions
Fast prediction of suspension rheology is fundamental for optimizing process efficiency and performance in numerous industrial settings. However, traditional simulations are computationally demanding due to explicit evaluation of contact networks and stress tensors in dense regimes approaching shear thickening and jamming. This study presents a microstructure-informed multitask learning framework based on the graph neural network (GNN) that learns an implicit mapping between particle configurations and emergent microstructural and rheological properties of suspensions. This model simultaneously predicts particle pressure $Π$, viscosity $η$, and friction coordination $Z_μ$, in a dynamic steady-state, without explicit knowledge of interparticle forces. Here, semi-dilute to dense suspension systems in 2D were simulated across a wide range of shear stresses $σ$, spanning continuous, discontinuous shear thickening, and shear-jamming conditions. The trained models demonstrated high correlation coefficients ($R^2$ = 0.99) with narrow mean absolute error for packing fractions up to $φ\le φ_J^μ$ for all predictive targets. However, prediction scatter increases near jamming conditions, attributed to inherent fluctuations in suspension behavior as the critical packing fraction is approached, yet predictions remain in excellent agreement, closely following the trend of the simulated flow curves across stress evolution. Once trained, the model can infer rheological responses directly from structural topology, avoiding explicit stress evaluation during prediction. The approach yields computationally efficient mesoscale surrogates for accelerated simulation with potential for real-time exploration of particulate suspension behavior. - Open Access
- 1 September 2025
- Published by Wiley in Journal of Applied Polymer Science
- Vol. 142 (48)
Effect of Confinement on Gas Barrier and Mechanical Properties of Rheologically Mismatched Multilayer TPU Films
Multilayer thermoplastic polyurethanes (TPUs) and polyethylene terephthalate glycol (PETG) films are fabricated using layer‐multiplying coextrusion with high‐aspect‐ratio (HAR) dies to explore the effects of high viscosity mismatch on gas barrier and mechanical properties. Three 50:50 polymer combinations spanning soft and rigid TPUs and PETG are studied. Despite viscosity ratios up to 24:1 and elasticity ratios up to 80:1, scanning electron microscopy (SEM) confirms successful multilayer structures up to 129 layers. Rheological analysis guides optimization of processing temperatures. Differential scanning calorimetry (DSC) reveals that spatial confinement enhances molecular packing and reduces diffusion pathways, lowering oxygen permeability by over 30% in some cases. Mechanical testing shows significant increases in modulus and puncture resistance as the number of layers increases, with the modulus of 129‐layer films approaching 80% of the rigid TPU control. Among the compositions studied, the multilayer films combining rigid and soft TPUs exhibit the greatest enhancement in barrier and mechanical performance, attributed to effective confinement, strong intermolecular hydrogen bonding, and refinement of hard segment domains (HSDs). This study demonstrates the feasibility of coextruding TPUs with extreme viscosity mismatch and establishes a processability benchmark, enabling the design of advanced multilayer films with improved performance for packaging and protective applications.
- 1 September 2025
- Published by Wiley in Journal of Applied Polymer Science
- Vol. 142 (48)
Open AccessEffect of Confinement on Gas Barrier and Mechanical Properties of Rheologically Mismatched Multilayer TPU Films
Multilayer thermoplastic polyurethanes (TPUs) and polyethylene terephthalate glycol (PETG) films are fabricated using layer‐multiplying coextrusion with high‐aspect‐ratio (HAR) dies to explore the effects of high viscosity mismatch on gas barrier and mechanical properties. Three 50:50 polymer combinations spanning soft and rigid TPUs and PETG are studied. Despite viscosity ratios up to 24:1 and elasticity ratios up to 80:1, scanning electron microscopy (SEM) confirms successful multilayer structures up to 129 layers. Rheological analysis guides optimization of processing temperatures. Differential scanning calorimetry (DSC) reveals that spatial confinement enhances molecular packing and reduces diffusion pathways, lowering oxygen permeability by over 30% in some cases. Mechanical testing shows significant increases in modulus and puncture resistance as the number of layers increases, with the modulus of 129‐layer films approaching 80% of the rigid TPU control. Among the compositions studied, the multilayer films combining rigid and soft TPUs exhibit the greatest enhancement in barrier and mechanical performance, attributed to effective confinement, strong intermolecular hydrogen bonding, and refinement of hard segment domains (HSDs). This study demonstrates the feasibility of coextruding TPUs with extreme viscosity mismatch and establishes a processability benchmark, enabling the design of advanced multilayer films with improved performance for packaging and protective applications.
- Open Access
- 4 August 2025
- Published by Wiley in Macromolecular Materials and Engineering
- Vol. 310 (12)
Glass Microsphere‐Filled Microlayer Films via Layer Multiplying Co‐Extrusion: Morphology and Process Stability
Layer multiplying co‐extrusion (LMCE) is a versatile and scalable technology for manufacturing polymeric composites with micro‐ and nano‐scale templated features. While previous studies have primarily focused on applications with neat polymers, incorporation of solid fillers into layered structures at high loading levels would enable new types of polymeric composites and templated precursors to dense solids. In this initial study, processing conditions and limitations of the LMCE process with respect to highly filled systems are explored using a model system of glass microsphere fillers in a polyolefin binder. Scanning electron microscopy of film cross‐sections indicates that layering persists for viscosity‐matched systems even as layer thicknesses approach the characteristic particle size of the fillers. However, when there is a significant viscosity mismatch between the filled and unfilled layers, an interfacial instability arises which disrupts the layer structure in high shear regions. These results demonstrate the feasibility of LMCE for highly filled systems and point to processing guidelines for the stable production of filled microlayer structures.
- 4 August 2025
- Published by Wiley in Macromolecular Materials and Engineering
- Vol. 310 (12)
Open AccessGlass Microsphere‐Filled Microlayer Films via Layer Multiplying Co‐Extrusion: Morphology and Process Stability
Layer multiplying co‐extrusion (LMCE) is a versatile and scalable technology for manufacturing polymeric composites with micro‐ and nano‐scale templated features. While previous studies have primarily focused on applications with neat polymers, incorporation of solid fillers into layered structures at high loading levels would enable new types of polymeric composites and templated precursors to dense solids. In this initial study, processing conditions and limitations of the LMCE process with respect to highly filled systems are explored using a model system of glass microsphere fillers in a polyolefin binder. Scanning electron microscopy of film cross‐sections indicates that layering persists for viscosity‐matched systems even as layer thicknesses approach the characteristic particle size of the fillers. However, when there is a significant viscosity mismatch between the filled and unfilled layers, an interfacial instability arises which disrupts the layer structure in high shear regions. These results demonstrate the feasibility of LMCE for highly filled systems and point to processing guidelines for the stable production of filled microlayer structures.
- 1 July 2025
- Published by AIP Publishing in Physics of Fluids
- Vol. 37 (7)
Robust prediction of frictional contact network in near-jamming suspensions employing deep graph neural networks
The viscosity of the suspension consisting of fine particles dispersed in a Newtonian liquid diverges close to the jamming packing fraction. The contact microstructure in suspensions governs this macroscopic behavior in the vicinity of jamming through a frictional contact network (FCN). FCN is composed of mechanical load-bearing contacts that lead to the emergence of rigidity near the jamming transition. The stress transmission and network topology, in turn, depend sensitively on constraints on the relative motion of the particles. Despite their significance, predicting the FCN, especially close to jamming conditions, remains challenging due to experimental and computational impediments. This study introduces a cost-effective machine learning approach to predict the FCN using a graph neural network (GNN), which inherently captures hidden features and underlying patterns in dense suspension by mapping interparticle interactions. Employing a variation of GNN called the Deep Graph Convolutional Network (DeepGCN) trained on data-driven simulations, this study demonstrates robust generalization and extrapolation capabilities, accurately predicting FCNs in systems with divergent flow parameters and phase spaces without explicit knowledge of the interparticle forces. The study covers a wide range of phase spaces, from semi-dilute to jammed states, spanning transient to steady states, while systematically varying parameters such as shear stress (σxy), packing fraction (ϕ) and sliding and rolling friction ({μs,μr}). The results of this research pave the way for innovative transferable techniques in predicting the properties of particulate systems, offering new avenues for advancement in materials science and related fields.- 1 July 2025
- Published by AIP Publishing in Physics of Fluids
- Vol. 37 (7)
Robust prediction of frictional contact network in near-jamming suspensions employing deep graph neural networks
The viscosity of the suspension consisting of fine particles dispersed in a Newtonian liquid diverges close to the jamming packing fraction. The contact microstructure in suspensions governs this macroscopic behavior in the vicinity of jamming through a frictional contact network (FCN). FCN is composed of mechanical load-bearing contacts that lead to the emergence of rigidity near the jamming transition. The stress transmission and network topology, in turn, depend sensitively on constraints on the relative motion of the particles. Despite their significance, predicting the FCN, especially close to jamming conditions, remains challenging due to experimental and computational impediments. This study introduces a cost-effective machine learning approach to predict the FCN using a graph neural network (GNN), which inherently captures hidden features and underlying patterns in dense suspension by mapping interparticle interactions. Employing a variation of GNN called the Deep Graph Convolutional Network (DeepGCN) trained on data-driven simulations, this study demonstrates robust generalization and extrapolation capabilities, accurately predicting FCNs in systems with divergent flow parameters and phase spaces without explicit knowledge of the interparticle forces. The study covers a wide range of phase spaces, from semi-dilute to jammed states, spanning transient to steady states, while systematically varying parameters such as shear stress (σxy), packing fraction (ϕ) and sliding and rolling friction ({μs,μr}). The results of this research pave the way for innovative transferable techniques in predicting the properties of particulate systems, offering new avenues for advancement in materials science and related fields.- Open Access
- 31 May 2025
- Published by Wiley in Polymer Engineering & Science
- Vol. 65 (8) , 4061-4069
Burst Pressure Performance of Multilayer Co‐Extruded Polystyrene/Poly (Methyl Methacrylate) Pipes
This study investigates advancements in multilayer co‐extrusion of annular structures, focusing on the mechanical properties of a 129‐layer polystyrene (PS)/poly(methyl methacrylate) (PMMA) pipe with 50% PS as a skin layer for proof‐of‐concept validation. We explore the impact of angular rotation on burst pressure, layer structure, and failure mechanisms, emphasizing the potential of this technique for enhanced mechanical performance through biaxial orientation and the elimination of weld lines. Utilizing high aspect ratio (HAR) multipliers and angular rotation, our findings demonstrate improved layer structure and burst pressure performance at higher velocity ratios, which is the ratio of the linear component of angular velocity to the extrusion throughput velocity. At a velocity of 1.15, the 129‐layer PS/PMMA tube exhibits a hoop stress of 10.9 MPa and a burst pressure of 3.9 MPa, achieving about 76% and 90% of the performance metrics of pure PMMA tubes with only 25% PMMA content, respectively. Microscopic examination reveals increased residual stress and biaxial orientation within the layers, contributing to the observed performance enhancements. These insights highlight the effectiveness of angular rotation in achieving high‐performance polymeric tubes, presenting a promising approach for optimizing multilayer co‐extruded annular structures in fluid transport applications.
- 31 May 2025
- Published by Wiley in Polymer Engineering & Science
- Vol. 65 (8) , 4061-4069
Open AccessBurst Pressure Performance of Multilayer Co‐Extruded Polystyrene/Poly (Methyl Methacrylate) Pipes
This study investigates advancements in multilayer co‐extrusion of annular structures, focusing on the mechanical properties of a 129‐layer polystyrene (PS)/poly(methyl methacrylate) (PMMA) pipe with 50% PS as a skin layer for proof‐of‐concept validation. We explore the impact of angular rotation on burst pressure, layer structure, and failure mechanisms, emphasizing the potential of this technique for enhanced mechanical performance through biaxial orientation and the elimination of weld lines. Utilizing high aspect ratio (HAR) multipliers and angular rotation, our findings demonstrate improved layer structure and burst pressure performance at higher velocity ratios, which is the ratio of the linear component of angular velocity to the extrusion throughput velocity. At a velocity of 1.15, the 129‐layer PS/PMMA tube exhibits a hoop stress of 10.9 MPa and a burst pressure of 3.9 MPa, achieving about 76% and 90% of the performance metrics of pure PMMA tubes with only 25% PMMA content, respectively. Microscopic examination reveals increased residual stress and biaxial orientation within the layers, contributing to the observed performance enhancements. These insights highlight the effectiveness of angular rotation in achieving high‐performance polymeric tubes, presenting a promising approach for optimizing multilayer co‐extruded annular structures in fluid transport applications.
- 18 June 2024
- Published by American Physical Society (APS) in Physical Review E
- Vol. 109 (6) , 064408
Adhesion dynamics of Janus nanocarriers to endothelial cells: A dissipative particle dynamics study
Janus nanocarriers (NCs) provide promising features in interfacial applications such as targeted drug delivery. Herein, we use dissipative particle dynamics simulations to study the adhesion dynamics of NCs with Janus ligand compositions to the endothelial cell as a function of a series of effects, such as the initial orientation, ligand density, shape, and size of Janus NCs. The Janus NCs, with its long axis parallel to the endothelial glycocalyx (EG) layer, has the best penetration depth due to its lower potential energy and the lowest shell entropy loss. Among different shapes of Janus NCs, both the potential energy and the EG entropy loss control the penetration. In fact, at the parallel orientations, Janus shapes with a robust mechanical strength and larger surface area at the EG/water interface can rotate and penetrate more efficiently. An increase in the ligand density of Janus NCs increases entropy losses of both the hydrophilic and the hydrophobic ligands and decreases the potential energy. Thus, for a specific Janus NCs, functionalizing with an appropriate ligand density would help driving forces prevail over barriers of penetration into the EG layer. For a particular ligand density, once the radius of the Janus NCs exceeds the appropriate size, barriers such as hydrophobic ligands and shell entropy losses are also reinforced significantly and surpass driving forces. Our observations reveal that entropy losses for hydrophobic ligands of Janus NCs and for the shell of NCs are decisive for the adhesion and penetration of Janus NCs to endothelial cells.
- 18 June 2024
- Published by American Physical Society (APS) in Physical Review E
- Vol. 109 (6) , 064408
Adhesion dynamics of Janus nanocarriers to endothelial cells: A dissipative particle dynamics study
Janus nanocarriers (NCs) provide promising features in interfacial applications such as targeted drug delivery. Herein, we use dissipative particle dynamics simulations to study the adhesion dynamics of NCs with Janus ligand compositions to the endothelial cell as a function of a series of effects, such as the initial orientation, ligand density, shape, and size of Janus NCs. The Janus NCs, with its long axis parallel to the endothelial glycocalyx (EG) layer, has the best penetration depth due to its lower potential energy and the lowest shell entropy loss. Among different shapes of Janus NCs, both the potential energy and the EG entropy loss control the penetration. In fact, at the parallel orientations, Janus shapes with a robust mechanical strength and larger surface area at the EG/water interface can rotate and penetrate more efficiently. An increase in the ligand density of Janus NCs increases entropy losses of both the hydrophilic and the hydrophobic ligands and decreases the potential energy. Thus, for a specific Janus NCs, functionalizing with an appropriate ligand density would help driving forces prevail over barriers of penetration into the EG layer. For a particular ligand density, once the radius of the Janus NCs exceeds the appropriate size, barriers such as hydrophobic ligands and shell entropy losses are also reinforced significantly and surpass driving forces. Our observations reveal that entropy losses for hydrophobic ligands of Janus NCs and for the shell of NCs are decisive for the adhesion and penetration of Janus NCs to endothelial cells.
- 5 April 2024
- Published by Taylor & Francis in Molecular Simulation
- Vol. 50 (10) , 622-630
Machine learning of performance space mapping for the DPD simulation of drug delivery to endothelial cells
- 5 April 2024
- Published by Taylor & Francis in Molecular Simulation
- Vol. 50 (10) , 622-630
Machine learning of performance space mapping for the DPD simulation of drug delivery to endothelial cells
- Open Access
- 27 March 2024
- Published by Wiley in Polymer Engineering & Science
- Vol. 64 (6) , 2476-2490
On the cogent formulation of an elastomeric silicone ink material for direct ink write (DIW) 3D printing
Adhesives and sealants show fine rheology with good physical and mechanical properties as viscous pastes, a possible starting point for developing direct ink writing (DIW) 3D printing ink. However, many commercial adhesives and sealants take days or weeks to cure fully. DIW 3D‐printed parts made directly from these sealants are not designed for a scalable manufacturing process and high‐volume production. Moreover, most of these adhesives and sealants have volume shrinkage during cure. A systematic understanding of formulation methods and design principles for an elastomeric silicone DIW ink can overcome these issues. This study presents the cogent formulation development of a 3D printable thermoset elastomer silicone that gels and cures isotropically in minutes, reducing cycle time for rapid ink development with no shrinkage during cure. More specifically, we outline the principles of raw material selection of a formulation to achieve excellent rheology, printability, synchronized working, and gel time fitting requirements closer to scalable manufacturing. The reaction kinetics and their corresponding 3D‐printed structural properties are also described. Interest in future work is toward a rational DIW 3D printing ink material development protocol and use of machine learning (ML).Highlights: Formulation method flexibility and design principle of DIW ink. Raw material selection principle to achieve optimal rheology for DIW printing. Ink gel kinetics for large‐scale DIW manufacturing. Hydrosilylation conversion over time at different ambient temperatures. Structural properties of DIW 3D printed parts.
- 27 March 2024
- Published by Wiley in Polymer Engineering & Science
- Vol. 64 (6) , 2476-2490
Open AccessOn the cogent formulation of an elastomeric silicone ink material for direct ink write (DIW) 3D printing
Adhesives and sealants show fine rheology with good physical and mechanical properties as viscous pastes, a possible starting point for developing direct ink writing (DIW) 3D printing ink. However, many commercial adhesives and sealants take days or weeks to cure fully. DIW 3D‐printed parts made directly from these sealants are not designed for a scalable manufacturing process and high‐volume production. Moreover, most of these adhesives and sealants have volume shrinkage during cure. A systematic understanding of formulation methods and design principles for an elastomeric silicone DIW ink can overcome these issues. This study presents the cogent formulation development of a 3D printable thermoset elastomer silicone that gels and cures isotropically in minutes, reducing cycle time for rapid ink development with no shrinkage during cure. More specifically, we outline the principles of raw material selection of a formulation to achieve excellent rheology, printability, synchronized working, and gel time fitting requirements closer to scalable manufacturing. The reaction kinetics and their corresponding 3D‐printed structural properties are also described. Interest in future work is toward a rational DIW 3D printing ink material development protocol and use of machine learning (ML).Highlights: Formulation method flexibility and design principle of DIW ink. Raw material selection principle to achieve optimal rheology for DIW printing. Ink gel kinetics for large‐scale DIW manufacturing. Hydrosilylation conversion over time at different ambient temperatures. Structural properties of DIW 3D printed parts.
- 22 December 2023
- Published by Walter de Gruyter GmbH in International Polymer Processing
- Vol. 39 (2) , 202-209
Multi-layer co-extrusion blow molding
Multi-layer co-extrusion via the layer multiplication technique and a blow-molding set-up were used to produce bottles with a 129-layered structure of a model system of alternating polystyrene (PS) and poly (methyl methacrylate) (PMMA) layers. This method shows layer retention and thickness control with the use of melt rotation during the extrusion process. Samples were extruded and deformed angularly at different rotation speeds, blow-molded into bottles, and the overall wall thickness and analysis of individual layer thicknesses were performed. Angular rotation leads to weld line deformation and a change in layer thickness above a critical rotation speed in which the weld lines, inherited in the extrusion of the tube structures, are suitably deformed helically leading to uniform deformation during the blowing process. This method has potentially large implications for single cavity blow molding processes where high-performance properties, e.g., high barrier, insulation, mechanical, are of upmost importance, potential industries include gas transport, specialty packaging, and medical.
- 22 December 2023
- Published by Walter de Gruyter GmbH in International Polymer Processing
- Vol. 39 (2) , 202-209
Multi-layer co-extrusion blow molding
Multi-layer co-extrusion via the layer multiplication technique and a blow-molding set-up were used to produce bottles with a 129-layered structure of a model system of alternating polystyrene (PS) and poly (methyl methacrylate) (PMMA) layers. This method shows layer retention and thickness control with the use of melt rotation during the extrusion process. Samples were extruded and deformed angularly at different rotation speeds, blow-molded into bottles, and the overall wall thickness and analysis of individual layer thicknesses were performed. Angular rotation leads to weld line deformation and a change in layer thickness above a critical rotation speed in which the weld lines, inherited in the extrusion of the tube structures, are suitably deformed helically leading to uniform deformation during the blowing process. This method has potentially large implications for single cavity blow molding processes where high-performance properties, e.g., high barrier, insulation, mechanical, are of upmost importance, potential industries include gas transport, specialty packaging, and medical.