Computational Modeling Techniques Research Articles

Unveiling the potential of lead-free Cs2AgBi0.75Sb0.25Br6 double perovskite solar cells with multilayer charge transport for 30% efficiency

The present study delves into the utilization of computer modeling techniques to analyze a photovoltaic (PV) design incorporating a lead (Pb)-free perovskite absorber material. This innovative structure employs a cesium-based double perovskite material, specifically Cs2AgBi0.75Sb0.25Br6, characterized by an energy bandgap of 1.80 eV. The Cs2AgBi0.75Sb0.25Br6 exhibits a range of advantageous properties, including heightened stability in ambient conditions and appropriately aligned bandgaps. Given these considerations, an extensive exploration of the FTO/CdS/Cs2AgBi0.75Sb0.25Br6 /HTL/Au configuration was undertaken to assess the impact of ten distinct hole transport layers (HTLs) on the solar cell's efficiency. The simulation and analysis of all devices were conducted employing the one-dimensional SCAPS-1D (Solar Cell Capacitance Simulator) tool. Furthermore, the efficiency of the FTO/ETL/Cs2AgBi0.75Sb0.25Br6 /MoO3/Au architecture was probed, employing six different electron transport layers (ETLs) (BaSnO3, PC61BM, SnS2, Ti2O3:Nb, CdS, ZnOS), to discern the optimal combination. Post-optimization, adjustments were made to the absorber and ETL thickness, as well as the acceptor doping and defect density of the absorber, and the donor and defect density of the ETL, for the six combinations assessed. These topologies were also examined for their impacts on series and shunt resistance, capacitance, the Mott-Schottky effect, generation and recombination processes, current–voltage density, and quantum efficiency. Eventually, the most efficient cell in this study achieved a power conversion efficiency (PCE) of 30.30 % and featured the FTO/ZnOS/Cs2AgBi0.75Sb0.25Br6/MoO3/Au configuration. The aforementioned findings hold significant potential for advancing lead-free, double perovskite solar cells (PSCs) that are both more efficient and environmentally sustainable, paving the way for their widespread adoption in the future.

МОДЕЛИРОВАНИЕ РЕЖИМОВ ЭЛЕКТРИЧЕСКИХ СЕТЕЙ, ПИТАЮЩИХ ТЯГОВЫЕ ПОДСТАНЦИИ С ДВЕНАДЦАТИПУЛЬСНЫМИ ПРЕОБРАЗОВАТЕЛЯМИ

A technique for computer modeling of the modes of electrical networks feeding traction substations with twelve-pulse converters in phase coordinates is proposed. The developed models can be used when choosing measures to increase the energy efficiency of transportation processes and improve the quality of electricity

Weight Optimization for missing data prediction of Landslide Susceptibility Mapping in Remote sensing Analysis

To keep track of changes to the Earth's surface, extensive time series of data from remote sensing using image processing is required. This research is motivated by the effectiveness of computational modelling techniques; however, the problem of missing data is multifaceted. When data at numerous a-periodic timestamps are absent during multi-temporal analysis, the issue becomes increasingly problematic. To make remote sensing time series analysis easier, weight optimised machine learning is used in this study to rebuild lost data. Keeping the causality restriction in mind, this method makes use of data from previous and subsequent timestamps. The architecture is based on an ensemble of numerous forecasting modules, built on the observed data in the time-series order. Dummy data is used to connect the forecasting modules, which were previously linked by the earlier half of the sequence. After that, iterative improvements are made to the dummy data to make it better fit the next segment of the sequence. On the basis of Landsat-7 TM-5 satellite imagery, the work has been proven to be accurate in forecasting missing images in normalised difference vegetation index time series. In a performance evaluation, the proposed forecasting model was shown to be effective.

SYMULACJA I MODELOWANIE KOMPUTEROWE DYNAMIKI KONSTRUKCJI MOSTÓW Z WYKORZYSTANIEM ANSYS

This study focuses on utilizing computer modeling and simulation techniques, specifically the ANSYS software, to analyze the dynamics of bridge structures. The primary objective was to study the vibrations of a riverbed metal bridge structure and determine their characteristics. The research involved theoretical dynamic calculations considering the design features of the bridge components and the materials used in their construction. The obtained results enabled the determination of resonance frequencies for the vibration modes. By utilizing the ANSYS software, a three-dimensional virtual model of the bridge structure was created, allowing for a detailed analysis of its dynamic behavior. The first three vibration modes of the riverbed metal bridge structure were calculated, and numerical results were obtained for six modes. The findings of this research have practical significance as they provide informed decision-making support during the construction, maintenance, and modernization of bridge structures. The study of bridge dynamics using advanced technologies contributes to enhancing the safety, reliability, and longevity of these vital infrastructure assets.

Numerical simulations and mathematical models in laser welding: a review based on physics and heat source models

The dominant phenomenon in laser welding processes is heat transfer by conduction, making it crucial to gain insights into energy distribution within the heat-affected region, including the melt pool. Thermal analysis enables the description of thermo-mechanical, metallurgical aspects, and also addresses studies related to fluid flow and energy transfer. As research in welding processes has advanced, these models have evolved. This is why it is now efficient to use computational modeling techniques as it allows us to analyze the behavior of laser welding during the process. This underlines the importance of this work which has carried out an exhaustive theoretical literature review with the objective of classifying and describing the numerical simulations of laser welding based on the physics involved. In that sense, the mathematical models and strategies used in laser welding are explored in a general way. Therefore, two types of laser welding by conduction and deep penetration are defined from this point and they are categorized according to the phenomena involved in Model Heat Conduction and Model Integral Multiphysics. This comprehensive review article serves as a valuable resource for higher education students by providing a structured and detailed exploration of laser welding and its mathematical modeling. By classifying and describing numerical simulations based on the physics involved, it offers a framework for students to understand the complexities of this field. Additionally, this innovative approach to organizing and presenting research contributes to educational innovation by facilitating a more efficient and effective learning experience, helping students acquire the knowledge and research skills necessary for advancements in the laser welding domain.

Structural insights into the C-terminus of the histone-lysine N-methyltransferase NSD3 by small-angle X-ray scattering.

NSD3 is a member of six H3K36-specific histone lysine methyltransferases in metazoans. Its overexpression or mutation is implicated in developmental defects and oncogenesis. Aside from the well-characterized catalytic SET domain, NSD3 has multiple clinically relevant potential chromatin-binding motifs, such as the proline-tryptophan-tryptophan-proline (PWWP), the plant homeodomain (PHD), and the adjacent Cys-His-rich domain located at the C-terminus. The crystal structure of the individual domains is available, and this structural knowledge has allowed the designing of potential inhibitors, but the intrinsic flexibility of larger constructs has hindered the characterization of mutual domain conformations. Here, we report the first structural characterization of the NSD3 C-terminal region comprising the PWWP2, SET, and PHD4 domains, which has been achieved at a low resolution in solution by small-angle X-ray scattering (SAXS) data on two multiple-domain NSD3 constructs complemented with size-exclusion chromatography and advanced computational modeling. Structural models predicted by machine learning have been validated in direct space, by comparison with the SAXS-derived molecular envelope, and in reciprocal space, by reproducing the experimental SAXS profile. Selected models have been refined by SAXS-restrained molecular dynamics. This study shows how SAXS data can be used with advanced computational modeling techniques to achieve a detailed structural characterization and sheds light on how NSD3 domains are interconnected in the C-terminus.

The Evaluation of Ordinal Regression Model's Performance Through the Implementation of Multilayer Feed-Forward Neural Network: A Case Study of Hypertension.

Background Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases (CVD), stroke, and kidney disorders. Objective The objective of this study is to create and validate a model that combines bootstrapping, ordered logistic regression, and multilayer feed-forward neural networks (MLFFNN) to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and methods A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The RStudio (Posit, Boston, MA) software and syntax were implemented to establish the relationship between hypertension status and the predictors. Results The statistical analysis showed that the developed methodology demonstratesgoodmodel fitting through the value of predicted mean square error (MSE), mean absolute deviance (MAD), and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It is important to note that all of them affect the hazard ratio: marital status (β1, -17.12343343; p < 0.25), smoking status (β2, 1.86069121; p < 0.25), systolicblood pressure (β3, 0.05037332; p < 0.25), fasting blood sugar (β4, -0.53880322; p < 0.25), and high-density lipoprotein (β5, 5.38065556; p < 0.25). Conclusion This research aims to develop and extensively evaluate the hybrid approach. The statistical methods employed in this study using R language show that regression modeling surpasses R-squared values in predicting the mean square error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.

Wave Energy Conversion through Oscillating Water Columns: A Review

An oscillating water column (OWC) is designed for the extraction and conversion of wave energy into usable electrical power, rather than being a standalone renewable energy source. This review paper presents a comprehensive analysis of the mathematical modeling approaches employed in OWC systems, aiming to provide an in-depth understanding of the underlying principles and challenges associated with this innovative technology. A prominent classification within the realm of wave energy devices comprises OWC systems, which exhibit either fixed or floating configurations. OWC devices constitute a significant proportion of the wave energy converter prototypes currently operational offshore. Within an OWC system, a hollow structure, either permanently fixed or floating, extends below the water’s surface, creating an enclosed chamber where air is captured over the submerged inner free surface. This comprehensive study offers a thorough assessment of OWC technology in conjunction with air turbines. Additionally, the investigation delves into theoretical, computational, and experimental modeling techniques employed for analyzing OWC converters. Moreover, this review scrutinizes theoretical, computational, and experimental modeling methodologies, providing a holistic understanding of OWC converters. Ultimately, this work contributes a thorough assessment of OWC technology’s current state, accentuating its potential for efficient wave energy extraction and suggesting future research avenues.

Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

The escalating use of novel chemicals and nanomaterials (NMs) across diverse sectors underscores the need for advanced risk assessment methods to safeguard human health and the environment. Traditional labor‐intensive approaches have given way to computational methods. This review integrates recent developments in chemical and nano‐quantitative structure‐activity relationship (QSAR) with machine learning and computational modeling, offering a comprehensive predictive assessment of NMs and chemicals. It explores nanodescriptors, their role in predicting toxicity, and the amalgamation of machine learning algorithms with chemical and nano‐QSAR for improved risk assessment accuracy. The article also investigates computational modeling techniques like molecular dynamics simulations, molecular docking, and molecular mechanics/quantum mechanics for predicting physical and chemical properties. By consolidating these approaches, the review advocates for a more accurate and efficient means of assessing risks associated with NMs/chemicals, promoting their safe utilization and minimizing adverse effects on human health and the environment. A valuable resource for researchers and practitioners, informed decision‐making, advancing our understanding of potential risks, is facilitated. Beyond studying systems at various scales, computational modeling integrates data from diverse sources, enhancing risk assessment accuracy and fostering the safe use of NMs/chemicals while minimizing their impact on human health and the environment.

Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis

The study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the research aimed to uncover the correlations between the chemical structures of these medications and their unique properties. The results illuminated the quantitative relationships between structural characteristics and pharmacological traits, advancing our predictive capabilities. This research significantly contributes to medication discovery and design by providing essential insights into the structure-property connections of these medicinal compounds. Notably, certain spectrum-based descriptors, such as positive inertia energy, adjacency energy, arithmetic-geometric energy, first zegrab energy, and the harmonic index, exhibited strong correlation coefficients above 0.999. In contrast, well-known descriptors like the Extended adjacency, Laplacian and signless Laplacian spectral radii, and the first and second Zagreb Estrada indices showed weaker performance. The article emphasizes the application of graph energies and a linear regression model to predict pharmacological features effectively, enhancing the drug discovery process and aiding in targeted drug design by elucidating the relationship between molecular structure and pharmacological characteristics.

Assessment of cutting tool wear using a numerical FEM simulation model

The advancement of computational modeling techniques, such as FEM, has allowed to simulate complex machining processes with improved accuracy. Wear prediction is a crucial aspect in understanding and optimizing machining processes, as it directly impacts tool life, surface quality and overall machining efficiency. This work focuses on the FEM simulation, specially utilizing the DEFORM software, in conjunction with the Usui wear model, for wear prediction in machining operations. The Usui wear model, a well-established and widely used wear prediction approach, accounts for multiple wear mechanisms that include adhesion, abrasion, and diffusion. By incorporating the Usui wear model into the FEM simulation framework within the DEFORM software, it is possible to understand wear phenomena in machining processes. The integration of Usui wear model algorithms into DEFORM enables the simulation to accurately predict wear rates, distribution patterns, and progression of tool deterioration. This predictive capability facilitates the identification of critical wear zones and guides proactive measures to improve tool life, reduce production costs, and optimize machining productivity. This work presents research focused on wear prediction in cutting processes, utilizing FEM simulation with DEFORM software and incorporating the Usui wear model. Through a comprehensive analysis of wear phenomena, this research aims to optimize cutting parameters, improve tool life, and contribute to the advancement of machining and manufacturing technologies.

Performance of personalised prosthesis under static pressure: Numerical analysis and experimental validation

This study investigates the performance of personalised middle ear prostheses under static pressure through a combined approach of numerical analysis and experimental validation. The sound transmission performances of both normal and reconstructed middle ears undergo changes under high positive or negative pressure within the middle ear cavity. This pressure fluctuation has the potential to result in prosthesis displacement/extrusion in patients. To optimise the design of middle ear prostheses, it is crucial to consider various factors, including the condition of the middle ear cavity in which the prosthesis is placed. The integration of computational modelling techniques with non-invasive imaging modalities has demonstrated significant promise and distinct prospects in middle ear surgery. In this study, we assessed the efficacy of Finite Element (FE) analysis in modelling the responses of both normal and reconstructed middle ears to elevated static pressure within the ear canal. The FE model underwent validation using experimental data derived from human cadaveric temporal bones before progressing to subsequent investigations. Afterwards, we assessed stapes and umbo displacements in the reconstructed middle ear under static pressure, with either a columella-type prosthesis or a prosthetic incus, closely resembling a healthy incus. Results indicated the superior performance of the prosthetic incus in terms of both sound transmission to the inner ear and stress distribution patterns on the TM, potentially lowering the risk of prosthesis displacement/extrusion. This study underscores the potential of computational analysis in middle ear surgery, encompassing aspects such as prosthesis design, predicting outcomes in ossicular chain reconstruction (OCR), and mitigating experimental costs.

Environmentally Benign Grape Seed Oil for Corrosion Inhibition: Cutting-Edge Computational Modeling Techniques Revealing the Intermolecular and Intramolecular Synergistic Inhibition Action

The growing interest in eco-friendly alternatives has sparked research into essential oils as corrosion inhibitors, offering an innovative approach. Investigating their unique properties, researchers aim to advance corrosion engineering for a sustainable future. Despite promising lab results, the exact mechanism of their action in corrosion engineering is not fully understood, highlighting the need for further exploration. Using computational modeling, we explored how grape seed oil (GSO) compounds interact with carbon steel (C38) surfaces, unraveling the inhibitive properties against corrosion. Employing various simulation methods, such as density functional theory (DFT), density functional-based tight-binding (DFTB), and molecular dynamics (MD), this study validates experimental findings and unveils novel insights into the underlying mechanisms of these interactions. Quantitative analysis quantifies the inter- and intramolecular synergistic effect and suggests that the LA@OA promotes the charge-transfer process. DFTB calculations reveal that the synergistic action in the parallel adsorption configuration of LA and OA molecules is sufficiently strong to form a stable adsorption layer on the Fe surface with a large negative value of Eads (6.74 eV). Experimental results demonstrated that the inhibition performance of GSO extract exhibited a notable increase with increasing concentrations, reaching a higher efficiency of 79% at 0.5 g/L of GSO. EIS results demonstrated that the existence of the GSO inhibitor film increases the resistance of the charge transfer (about 80 Ω cm2 at 0.5 g/L), indicating the superior barrier anticorrosion properties of the formed film. The theoretical results validate the exceptional anticorrosion performance and provide compelling evidence of the remarkable ability to prevent corrosion of C38 substrate. The findings offer potential pathways for the development of eco-friendly alternatives and interestingly provide a foundation understanding in the field.

Unveiling the relationship between polymorphism and ionic conductivity in Na3−xSb1−xWxS4 solid electrolyte for Na-ion batteries

Computational modelling and experimental techniques are used to elucidate local ordering and diffusion mechanisms in the W-substituted Na3SbS4 solid electrolyte.

A digital twin concept for optimizing the use of high-temperature heat pumps to reduce waste in industrial renewable energy systems

In light of the global industrial sector's steadfast pursuit of sustainable solutions to mitigate carbon emissions and efficiently minimize energy inefficiencies, the significance of novel approaches to energy optimization becomes increasingly evident. This research presents a novel digital twin concept that is designed to enhance the efficiency and effectiveness of high-temperature heat pumps in industrial renewable energy systems. Through the utilization of real-time data and complex computer modeling techniques, our proposed digital twin model seamlessly presents a comprehensive perspective on energy flows. This approach identifies inefficiencies and delivers practical insights to mitigate waste. The incorporation of this approach into pre-existing eco-conscious renewable energy systems has the potential to greatly enhance the effectiveness, predictability, and long-term viability of industrial processes. The empirical findings, obtained from multiple case studies conducted in industrial settings, provide evidence of the potential benefits in terms of energy waste reduction, and maximize the durability of systems. The results of our initial studies provide a foundation for the utilization of digital twin technologies in the field of industrial renewable energy systems optimization, representing a significant advancement towards a more environmentally sustainable industrial landscape.

Use of Surface Features with Controlled Kinetics to Verify Fits of Scanning Electrochemical Microscopy Images

Scanning Electrochemical Microscopy (SECM) is a promising technique for measuring kinetics of redox reactions at surfaces. The conversion of the current map - produced by rastering an electrode over the surface - to a maps of the rate constant of reactions at the surface requires computer modelling and statistical techniques. Experimentally obtained SECM images are necessary to validate computer models that will be used to test these fitting procedures.The largest challenge was finding or creating samples that have reactive sites of known kinetics that were sufficiently slow to be activation-limited. This was achieved by producing small precious metal electrodes embedded in an insulating surface. The exchange currents of these electrodes were characterized using cyclic voltammetry at high scan rates[1,2] so that the rate constant of the electrodes could be effectively controlled by the potential at which they were poised. SECM images of these electrodes can compared to computer simulations to verify that they produced correct results. [1] Nicholson, R.S. Theory and Application of Cyclic Voltammetry for Measurement of Electrode Reaction Kinetics. Anal. Chem. 1965, 37, 11, 1351–1355[2] Mirkin, M.V.; Bard, A.J. Simple Analysis of Quasi-Reversible Steady-State Voltammograms. Anal. Chem. 1992, 64, 19, 2293–2302

Application of the weighted histogram method for calculating the thermodynamic parameters of the formation of oligodeoxyribonucleotide duplexes

To date, many derivatives and analogs of nucleic acids (NAs) have been developed. Some of them have found uses in scientific research and biomedical applications. Their effective use is based on the data about their properties. Some of the most important physicochemical properties of oligonucleotides are thermodynamic parameters of the formation of their duplexes with DNA and RNA. These parameters can be calculated only for a few NA derivatives: locked NAs, bridged oligonucleotides, and peptide NAs. Existing predictive approaches are based on an analysis of experimental data and the consequent construction of predictive models. The ongoing pilot studies aimed at devising methods for predicting the properties of NAs by computational modeling techniques are based only on knowledge about the structure of oligonucleotides. In this work, we studied the applicability of the weighted histogram analysis method (WHAM) in combination with umbrella sampling to the calculation of thermodynamic parameters of DNA duplex formation (changes in enthalpy ∆H°, entropy ∆S°, and Gibbs free energy ∆G37° ). A procedure was designed involving WHAM for calculating the hybridization properties of oligodeoxyribonucleotides. Optimal parameters for modeling and calculation of thermodynamic parameters were determined. The feasibility of calculation of ∆H°, ∆S°, and ∆G37° was demonstrated using a representative sample of 21 oligonucleotides 4–16 nucleotides long with a GC content of 14–100 %. Error of the calculation of the thermodynamic parameters was 11.4, 12.9, and 11.8 % for ∆H°, ∆S°, and ∆G37° , respectively, and the melting temperature was predicted with an average error of 5.5 °C. Such high accuracy of computations is comparable with the accuracy of the experimental approach and of other methods for calculating the energy of NA duplex formation. In this paper, the use of WHAM for computation of the energy of DNA duplex formation was systematically investigated for the first time. Our results show that a reliable calculation of the hybridization parameters of new NA derivatives is possible, including derivatives not yet synthesized. This work opens up new horizons for a rational design of constructs based on NAs for solving problems in biomedicine and biotechnology.

Immunoinformatics-Driven Strategies for Advancing Epitope-Based Vaccine Design for West Nile Virus

The West Nile virus (WNV) is the causative agent of West Nile disease (WND), which poses a potential risk of meningitis or encephalitis. The aim of the study was to design an epitope-based vaccine for WNV by utilizing computational analyses. The epitope-based vaccine design process encompassed WNV sequence collection, phylogenetic tree construction, and sequence alignment. Computational models identified B-cell and T-cell epitopes, followed by immunological property analysis. Epitopes were then modeled and docked with B-cell receptors, MHC I, and MHC II. Molecular dynamics simulations further explored dynamic interactions between epitopes and receptors. The findings indicated that the B-cell epitope QINHHWHKSGSSIG, along with three T-cell epitopes (FLVHREWFM for MHC I, NPFVSVATANAKVLI for MHC II, and NAYYVMTVGTKTFLV for MHC II), successfully passed the immunological evaluations. These four epitopes were further subjected to docking and molecular dynamics simulation studies. Although each demonstrated favorable affinities with their respective receptors, only NAYYVMTVGTKTFLV displayed a stable interaction with MHC II during MDS analysis, hence emerging as a potential candidate for a WNV epitope-based vaccine. This study demonstrates a comprehensive approach to epitope vaccine design, combining computational analyses, molecular modeling, and simulation techniques to identify potential vaccine candidates for WNV.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

Polymersomes in Drug Delivery─From Experiment to Computational Modeling.

Polymersomes, composed of amphiphilic block copolymers, are self-assembled vesicles that have gained attention as potential drug delivery systems due to their good biocompatibility, stability, and versatility. Various experimental techniques have been employed to characterize the self-assembly behaviors and properties of polymersomes. However, they have limitations in revealing molecular details and underlying mechanisms. Computational modeling techniques have emerged as powerful tools to complement experimental studies and enabled researchers to examine drug delivery mechanisms at molecular resolution. This review aims to provide a comprehensive overview of the state of the art in the field of polymersome-based drug delivery systems, with an emphasis on insights gained from both experimental and computational studies. Specifically, we focus on polymersome morphologies, self-assembly kinetics, fusion and fission, behaviors in flow, as well as drug encapsulation and release mechanisms. Furthermore, we also identify existing challenges and limitations in this rapidly evolving field and suggest possible directions for future research.