Copolymer Systems Research Articles

Molecular Dynamics and Self-Assembly in Double Hydrophilic Block and Random Copolymers.

We investigate the self-assembly and dynamics of double hydrophilic block copolymers (DHBCs) composed of densely grafted poly[oligo(ethylene glycol) methacrylate] (POEGMA) and poly(vinyl benzyl trimethylammonium chloride) (PVBTMAC) parent blocks by means of calorimetry, small- and wide-angle X-ray scattering (SAXS/WAXS), and dielectric spectroscopy. A weak segregation strength is evident from X-ray measurements, implying a disordered state and reflecting the inherent miscibility between the host homopolymers. The presence of intermixed POEGMA/PVBTMAC nanodomains results in homogeneous molecular dynamics, as evidenced through isothermal dielectric and temperature-modulated DSC measurements. The intermixed process undergoes a glass transition at a temperature approximately 40 K higher than the vitrification of bulk POEGMA segments, and it shifts to an even higher temperature by increasing the content of the hard block. At temperatures below the intermixed glass transition temperature, the confined POEGMA segments between the glassy intermixed regions contribute to a segmental process featuring (i) reduced glass transition temperature (Tg), (ii) reduced dielectric strength, (iii) broader distribution of relaxation times, and (iv) reduced fragility compared to the POEGMA homopolymer. We also observe two glass transition temperatures of dry PVBTMAC, which we attribute to the backbone and side chain segmental relaxation. To the best of our knowledge, this is the first time in the literature that these glass transitions of dry PVBTMAC have been reported. Finally, this study shows that excellent mixing of the two homopolymers is obtained, and this implies that different properties of this copolymer system can be tailored by adjusting the concentration of each homopolymer.

Designing the next generation of polymers with machine learning and physics-based models

Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( < 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R 2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation

Nanotechnology has opened new avenues for advanced research in various fields of soft materials. Materials scientists, chemists, physicists, and computational mathematicians have begun to take a keen interest in soft materials due to their potential applications in nanopatterning, membrane separation, drug delivery, nanolithography, advanced storage media, and nanorobotics. The unique properties of soft materials, particularly self-assembly, have made them useful in fields ranging from nanotechnology to biomedicine. The discovery of new morphologies in the diblock copolymer system in curved geometries is a challenging problem for mathematicians and theoretical scientists. Structural frustration under the effects of confinement in the system helps predict new structures. This mathematical study evaluates the effects of confinement and curvature on symmetric diblock copolymer melt using a cell dynamic simulation model. New patterns in lamella morphologies are predicted. The Laplacian involved in the cell dynamic simulation model is approximated by generating a 17-point stencil discretized to a polar grid by the finite difference method. Codes are programmed in FORTRAN to run the simulation, and IBM open DX is used to visualize the results. Comparison of computational results with existing studies validates this study and identifies defects and new patterns.

Biradical Initiation in Photoinitiated Copolymerization of Styrene and Maleic Anhydride

AbstractPoly(styrene‐co‐maleic anhydride) (SMAnh) is a copolymer system that is well known for its alternating tendency, which has been thoroughly investigated by many research groups over the decades and is widely used in industry. Investigations have focused almost entirely on thermally initiated systems. However, very few investigations have been conducted on light‐initiated radical copolymerization. In this study, the photoinitiated copolymerization of styrene and maleic anhydride is investigated without the use of an external radical source, such as the classical 2,2‐azobis(2‐methylpropionitrile) (AIBN), benzoyl peroxide (BPO), or any photoinitiators. The initiation of the polymerization is instead found to take place through an excited state biradical generated in situ from a photoexcited charge‐transfer complex (CTC) comprising the donor (styrene) and acceptor (maleic anhydride) monomers. The formation of the complex is found to be solvent dependent. Excitation of the complex results in copolymerization of monomers to afford high‐molecular‐weight (>105 g·mol−1) alternating copolymers.

Conductivity of Diblock Copolymer System Filled with Conductive Fillers

AbstractThe electrical response of the insulating diblock copolymer system (DBC) filled with conductive spherical fillers is investigated to the variation of the following factors: i) composite temperature; ii) affinities of fillers for copolymer blocks; iii) interaction between fillers. By performing the consistent phase‐field and Monte‐Carlo simulations of the filled DBC system, this article quantitatively demonstrates that the distribution of fillers in DBC essentially depends on the complicated interplay of the above three factors. The obtained distribution of filler particles, in turn, proves to be directly related to the electrical response of the DBC‐particle composite, calculated by the developed model. In particular, the order‐disorder transition in the DBC system has been found to co‐occur with the insulator‐conductor transition in the filler network providing a sufficient difference between the affinities of fillers for dissimilar DBC blocks. The order–order transition between the lamella and cylindrical morphologies of DBC has been found to cause a spike in the composite conductivity.

Competing Effects of Temperature and Polymer Concentration on Evolution of Re-entrant Interactions in the Nanoparticle-Block Copolymer System.

An interesting evolution of the re-entrant interaction has been observed in an anionic silica nanoparticle (NP)-block copolymer (P85) dispersion due to mutually competing effects of temperature and polymer concentration. It has been demonstrated that a rise in the temperature leads to an evolution of attraction in the system, which interestingly diminishes on increasing the polymer concentration. Consequently, the system exhibits a re-entrant transition from repulsive to attractive and back to repulsive at a given temperature but with respect to the increasing polymer concentration, within a selected region of concentration and temperature. The intriguing observations have been elucidated based on the temperature/concentration-dependent modifications in the interactions governing the system, as probed by contrast-variation small-angle neutron scattering. The initial transition from the repulsive to attractive system is attributed to the temperature-driven enhancement in the hydrophobicity of the amphiphilic triblock copolymer (P85) adsorbed on nanoparticles. The strength and range of this attraction are found to be more than van der Waals attraction while relatively less than electrostatic interaction. At higher polymer concentrations, the saturation of polymer adsorption on nanoparticles introduces additional steric repulsion along with electrostatic interaction between their conjugates, effectively reducing the strength of the attraction. However, with a significant increase in temperature (>75 °C), the attraction again dominates the system, which eventually leads to the particle aggregation at all the measured polymer concentrations (>0.1 wt %). Our study provides useful inputs to develop smart NP-polymer composites having capabilities to respond to external stimuli such as temperature/concentration variation.

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data.

Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes a machine learning approach that leverages transfer learning and ensemble modeling to efficiently predict the glass transition temperature (Tg) of fluorinated polymers and guide the design of high Tg copolymers. Initially, the quantum machine 9 (QM9) dataset is employed for model pretraining, thus providing robust molecular representations for the subsequent fine-tuning of a specialized copolymer dataset. Ensemble modeling is used to further enhance prediction robustness and reliability, effectively addressing the problems owing to the limited and unevenly distributed nature of the copolymer dataset. Finally, a fine-tuned ensemble model is used to navigate a vast chemical space comprising 61 monomers and identify promising candidates for high Tg fluorinated polymers. The model predicts 247 entries capable of achieving a Tg over 390 K, of which 14 are experimentally validated. This study demonstrates the potential of machine learning in material design and discovery, highlighting the effectiveness of transfer learning and ensemble modeling strategies for overcoming the challenges posed by small datasets in complex copolymer systems.

Interconnected Nanoporous Polysulfone by the Self-Assembly of Randomly Linked Copolymer Networks and Linear Multiblocks.

Porous materials have attracted considerable attention due to their versatile applications, especially in water purification. Interconnected nanoporous structures are distinguished by their high degree of porosity and resistance to clogging, as well as their insensitivity to nanostructural orientation. Previous works on randomly linked copolymer systems have shown that they can effectively produce disordered cocontinuous nanostructures, which upon removal of one component yield interconnected nanoporous materials. However, the cocontinuous nanomaterials previously developed using polystyrene (PS) and poly(d,l-lactic acid) (PLA) strands, and the resulting interconnected nanoporous PS monoliths, were far too brittle to enable practical use as membranes. Here, we study the self-assembly of randomly linked copolymer networks prepared using blocks of the engineering polymer polysulfone (PSU). A wide cocontinuous regime (spanning 40 wt %) was found for randomly end-linked copolymer networks (RECNs) constructed from PSU and PLA strands, via a combination of mechanical testing, gravimetry, small-angle X-ray scattering, and scanning electron microscopy. The PSU/PLA cocontinuous nanomaterial with symmetric composition showed 2.4 times higher Young's modulus and ∼100 times greater toughness than the corresponding PS/PLA sample. The interconnected nanoporous PSU fabricated after etching of PLA even exhibited 1.6 times greater toughness than PS/PLA prior to PLA removal. To facilitate the production of thin films of cocontinuous nanomaterials, we applied solution-processable randomly linked linear PSU/PLA multiblock polymers onto ultrafiltration membranes. The interconnected nanoporous PSU thin film generated by etching PLA was found to effectively reject 50 nm diameter particles without significantly compromising permeability. This discovery presents a valuable addition to the existing techniques used to fabricate PSU membranes. In contrast to traditional methods, which are sensitive to processing conditions, produce a wide range of pore sizes, and offer limited adjustability of pore size, the current technique is anticipated to enable interconnected PSU membranes with more uniform and tailorable porosity.

Anthranilic Acid: A Versatile Monomer for the Design of Functional Conducting Polymer Composites

Polyaniline has been utilized in various applications, yet its widespread adoption has often been impeded by challenges. Composite systems have been proposed as a means of mitigating some of these limitations, and anthranilic acid (2-aminobenzoic acid) has emerged as a possible moderator for use in co-polymer systems. It offers improved solubility and retention of electroactivity in neutral and alkaline media, and, significantly, it can also bestow chemical functionality through its carboxylic acid substituent, which can greatly ease post-polymer modification. The benefits of using anthranilic acid (as a homopolymer or copolymer) have been demonstrated in applications including corrosion protection, memory devices, photovoltaics, and biosensors. Moreover, this polymer has been used as a versatile framework for the sequestration of metal ions for water treatment, and, critically, these same mechanisms serve as a facile route for the production of catalytic metallic nanoparticles. However, the widespread adoption of polyanthranilic acid has been limited, and the aim of the present narrative review is to revisit the early promise of anthranilic acid and assess its potential future use within modern smart materials. A critical evaluation of its properties is presented, and its versatility as both a monomer and a polymer across a spectrum of applications is highlighted.

Patterns of Nanoporous Spherical Packing Emerging under Influence of Curvature and Confinement

Nanoporous membranes are popular in nanotechnology due to biomedical and industrial applications. During the past decade, experimental, theoretical and computational research into porous membranes and soft materials has opened up new mathematical dimensions. In bulk, diblock copolymers exhibit ordered morphologies such as parallel matrices of lamellae, bicontinuous matrices of gyroids, hexagonal matrices of cylinders and body-centred cubic matrices of spheres. In melt, confinement plays an essential role in tuning the frustration of the diblock copolymer system to predict more nanostructures. These nanostructures depend on the composition of the copolymers, their confining geometries and the degree of structural frustration. An isotropic 9-point stencil for Laplacian is constructed. The discrete finite-difference technique is used in polar grids to discretize the macromolecule of the diblock copolymer system to study spherical patterns to study the effect of curvature and confinement with a well-known and efficient cell dynamic simulation model. Intel FORTRAN (IFORT) codes are generated to run the CDS model and visualisation of simulation results is observed with the help of OPENDX. A comparison of the proposed study with existing experimental and computational studies is also presented.

Preparation and Characterization of Guaiacol-Furfuramine Benzoxazine and Its Modification of Bisphenol A-Aniline Oxazine Resin.

A new type of benzoxazine resin has been synthesized using a natural phenol source, guaiacol, and a biomass amines, furfuramine. The synthesis conditions were optimized; when the reaction molar ratio of guaiacol, furfuramine, and polyformaldehyde was 1:1:4, the highest synthetic yield was reached. The product was characterized via testing using transform infrared spectroscopy (FT-IR), gel permeation chromatography (GPC), mass spectrogram (MS), and nuclear magnetic resonance (1H-NMR) to confirm its molecular structure. A differential scanning calorimetry (DSC) test was conducted to analyze the thermodynamic properties of the product, and the results showed that the product decomposed and evaporated at around 180 °C, making it impossible to achieve self-curing. However, the prepared guaiacol-furfuramine benzoxazine resin (GFZ) can be blended and cured in certain proportions with bisphenol A-aniline oxazine resin (BAZ) as a GFZ/BAZ binary system (5:95, 10:90, 20:80, and 40:60). Dynamic mechanical analysis (DMA) test results showed that when the content of GFZ was 10%, the storage modulus of the copolymer resin was greatly improved. After conducting impact strength tests on the copolymer resin, it was found that the toughness of the copolymer resin had improved, and the maximum impact strength had increased by nearly three times. This indicates that the flexible long-chain structure in GFZ can effectively improve the toughness of the cured copolymer system. The reaction of active groups on benzoxazine molecules with other resins can not only improve the mechanical properties of their cured products, but also has important significance in the preparation of low-cost and environmentally friendly sustainable composite materials with excellent comprehensive performance.

Cyclodextrins-block copolymer drug delivery systems: From design and development to preclinical studies

Abstract Cyclodextrin (CD)-block copolymer hybrid systems have recently received great attention from the pharmaceutical and materials research community because this combination can result in new biomaterials and supramolecular structures, which possess the physicochemical and thermotropic properties of both classes of materials. Different structures of CD-block copolymer systems have been described (i.e., micelles, vehicles, core-shell structures, nanospheres, and membranes) and they can encapsulate active pharmaceutical ingredients or other bioactive compounds. The aim of this review is to summarize several examples, the properties, the morphological and physicochemical characteristics, the added value, the techniques used for their preparation and characterization, as well as the limitations of CD-block copolymer systems. Taking into consideration the wide variety of block copolymers and CD materials and the expected beneficial characteristics/behavior following their complexation, we could suggest them as new-generation formulations in the upcoming years.

Nanocomposites of thermoplastic matrices with non-covalent fullerene reinforcement—Structural diversity, physical impact and potential

Fullerene has been acknowledged as a significant nanocarbon nanofiller enhancing the imperative polymer characteristics. Since, thermoplastic polymers constitute a large group of polymeric materials, fullerene has been used as reinforcement in these matrices mostly via non-covalent means. This state-of-the-art review article summarizes thermoplastic polymer nanocomposites reinforced with fullerene nano-additives without involving any covalent interactions. Accordingly, thermoplastic polymer/fullerene nanocomposites of interest have non-covalent or physical interactions in the matrix-nanofiller such as van der Waals forces, electrostatic interactions, hydrogen bonding, and aromatic stacking interactions. Number of thermoplastic polymers including polyamide, polyurethanes, and block copolymers have been non-covalently or physically reinforced with the fullerene molecules. Ensuing high performance thermoplastic polymer/fullerene nanocomposites exhibited improved microstructure, electrical, mechanical, thermal, and other physical properties. Enhancements in the thermal, mechanical, and electrical properties of the thermoplastic/fullerene nanomaterials were found dependent upon the nanofiller contents, orientations, interfacial effects, and processing. Consequently, the polyamide/fullerene systems were found efficient to enhance the glass transition up to 260°C, in addition to optimum mechanical properties. Polyurethane/fullerene systems performed better for improved tensile strength and young’s modulus features up to 90 MPa and 48 GPa, respectively. System based on poly (methyl methacrylate) and fullerene has resulted in high thermal degradation temperature in the range of 501-633°C with fine electrical conductivity of 1.3 Scm−1. Using combination of fullerene and graphene nanofiller (due to synergistic effects) has been found to improve the electrical conductivity considerably in the range of 1.8–2.5 Scm−1 for a polystyrene and block copolymer system. However, attaining fine fullerene nanoparticle dispersion of non-covalently reinforced matrices have been found important affecting the final nanocomposite properties. Consequently, processability and essential characteristics of non-covalently fullerene filled nanocomposites can be influenced due to nanoparticle aggregation. Hence, the physical property enhancement potential of physical linking between the non-covalently linked thermoplastics-fullerene has been portrayed in this article. Research on non-covalently interacted thermoplastic polymer/fullerene nanocomposites revealed technical potential ranging from energy/electronic devices to engineering and biomedical sectors. This review article can be a useful guide for the field researchers towards the development of advanced systems using non-covalently linked polymer/fullerene nanomaterials for future technical applications.

Formulation and assessment of penetration potential of Risedronate chitosan nanoparticles loaded transdermal gel in the management of osteoporosis: In vitro and ex vivo screening

Osteoporosis targeting by transdermal distribution has drawn a lot of attention as a cutting-edge, environmentally friendly platform for sustainable pain management. We investigate a novel biocompatible transdermal gel (RCNGL) containing biodegradable chitosan nanoparticles and Risedronate (RS) to treat osteoporosis. The RCNGL was formulated using the Pluronic F 127 co-polymer system and the ionic gelation process. The formulation showed a robust architecture of nanoparticle dispersion with a nano-size range of 201.8 nm when it was thoroughly analysed for Scanning Electron Microscopy, Dynamic electron Microscopy and transmission electron microscopy. At various temperatures, the entrapment efficiency was 67.53±1.05% with steady rheology and respectable porosity, validating FT-IR spectra that showed structural compatibility of RS with other excipients. The drug release test resulted in an 88% RS gel release, supporting sustained release kinetics. The ex vivo Franz diffusion study and in vitro cell uptake and distribution experiments revealed clear cell uptake and distribution with notable skin permeation displaying >70% RS release in mouse epidermal tissue. Overall, our research shows that biodegradable RCNGL may offer a potential osteoporosis and pain control strategy in clinical platforms using passive targeting.

Self-healing polyacrylates based on dynamic disulfide and quadruple hydrogen bonds.

Herein, a self-healing polyacrylate system was successfully prepared by introducing crosslinking agents containing disulfide bonds and monomers capable of forming quadruple hydrogen bonds through free radical copolymerization. This polymer material exhibited good toughness and self-healing properties through chemical and physical dual dynamic networks while maintaining excellent mechanical properties, which expanded the development path of self-healing acrylate materials. Compared to uncrosslinked and single dynamically crosslinked polymers, its elongation at break was as high as 437%, and its tensile strength was 5.48 MPa. Due to the presence of dual reversible dynamic bonds in the copolymer system, good self-healing was also achieved at 60 °C. In addition, differential scanning calorimetry and thermogravimetric analysis measurements confirmed that the thermal stability and glass transition temperature of the material were improved owing to the presence of physical and chemical cross-linking networks.

Self-assembly of colloids with competing interactions confined in spheres.

At low temperatures, colloidal particles with short-range attractive and long-range repulsive interactions can form various periodic microphases in bulk. In this paper, we investigate the self-assembly behaviour of colloids with competing interactions under spherical confinement by conducting molecular dynamics simulations. We find that the cluster, mixture, cylindrical, perforated lamellar and lamellar structures can be obtained, but the details of the ordered structures are different from those in bulk systems. Interestingly, the system tends to form more perforated structures when confined in smaller spheres. The mechanism behind this phenomenon is driven by the relationship between the energy of the ordered structures and the bending of the confinement wall, which is different from the mechanism in copolymer systems.

Liquid-liquid phase separation induced auto-confinement.

Confinement allows macromolecules and biomacromolecules to attain arrangements typically unachievable through conventional self-assembly processes. In the field of block copolymers, confinement has been achieved by preparing thin films and controlled solvent evaporation through the use of emulsions. A significant advantage of the confinement-driven self-assembly process is its ability to enable block copolymers to form particles with complex internal morphologies, which would otherwise be inaccessible. Here, we show that liquid-liquid phase separation (LLPS) can induce confinement during the self-assembly of a model block copolymer system. Since this confinement is driven by the block copolymers' tendency to undergo LLPS, we define this confinement type as auto-confinement. This study adds to the growing understanding of how LLPS influences block copolymer self-assembly and provides a new method to achieve confinement driven self-assembly.

A computational method for rapid analysis polymer structure and inverse design strategy (RAPSIDY).

Tailoring polymers for target applications often involves selecting candidates from a large design parameter space including polymer chemistry, molar mass, sequence, and architecture, and linking each candidate to their assembled structures and in turn their properties. To accelerate this process, there is a critical need for inverse design of polymers and fast exploration of the structures they can form. This need has been particularly challenging to fulfill due to the multiple length scales and time scales of structural arrangements found in polymers that together give rise to the materials' properties. In this work, we tackle this challenge by introducing a computational framework called RAPSIDY - Rapid Analysis of Polymer Structure and Inverse Design strategY. RAPSIDY enables inverse design of polymers by accelerating the evaluation of stability of multiscale structure for any given polymer design (sequence, composition, length). We use molecular dynamics simulations as the base method and apply a guiding potential to initialize polymers chains of a selected design within target morphologies. After initialization, the guiding potential is turned off, and we allow the chains and structure to relax. By evaluating similarity between the target morphology and the relaxed morphology for that polymer design, we can screen many polymer designs in a highly parallelized manner to rank designs that are likely to remain in that target morphology. We demonstrate how this method works using an example of a symmetric, linear pentablock, AxByAzByAx, copolymer system for which we determine polymer sequences that exhibit stable double gyroid morphology. Rather than trying to identify the global free-energy minimum morphology for a specific polymer design, we aim to identify candidates of polymer design parameter space that are more stable in the desired morphology than others. Our approach reduces computational costs for design parameter exploration by up to two orders-of-magnitude compared to traditional MD methods, thus accelerating design and engineering of novel polymer materials for target applications.

Highly stable color-tunable organic long-persistent luminescence from a single-component exciplex copolymer for in vitro antibacterial.

Developing exciplex-based organic long-persistent luminescence (OLPL) materials with high stability is very important but remains a formidable challenge in a single-component system. Here, we report a facile strategy to achieve highly stable OLPL in an amorphous exciplex copolymer system via through-space charge transfer (TSCT). The copolymer composed of electron donor and acceptor units can not only exhibit effective TSCT for intra/intermolecular exciplex emission but also construct a rigid environment to isolate oxygen and suppress non-radiative decay, thereby enabling stable exciplex-based OLPL emission with color-tunable feature for more than 100 h under ambient conditions. These single-component OLPL copolymers demonstrate robust antibacterial activity against Escherichia coli under visible light irradiation. These results provide a solid example to exploit highly stable exciplex-based OLPL in polymers, shedding light on how the TSCT mechanism may potentially contribute to OLPL in a single-component molecular system and broadening the scope of OLPL applications.

Morphological Investigation of Lamellae Patterns in Diblock Copolymers under Change of Thickness and Confinement in Polar Geometry

Mathematicians collaborate computationally with practical scientists to predict the novel morphologies soft materials, having potential applications in nanotechnology. This work presents the novel lamellae morphology that the confined diblock copolymer system displayed inside the polar geometry in order to achieve this, cell dynamics simulations (CDS) model is employed on a polar mesh to study the special impacts of confinement on the self-assembly behavior of diblock copolymer systems. Under the numerical formulation of the isotropic Laplacian with polar geometry employed in the CDS model, the set of 9-point stencil is considered to discretize the Laplacian operator on polar mesh system. FTCS finite difference mechanism is taken into consideration for discretization of Laplacian operator due to its computational efficiency and accuracy. Cell dynamics simulation is efficient simulation model for large-scale simulation as compared to the other models available in literature. In the simulation, evolutionary lamella morphology is presented with and without confinement for various sizes of the circular annular pores. A comparative review of lamella morphologies achieved by simulation in comparison of experimental study is presented, coupled with a thorough investigation of symmetric and asymmetric morphologies in captivity.