MIT World Peace University

Poly(2-oxazoline)s (POx) are becoming increasingly valuable for various uses, especially in the biomedical industry. POx are innovative biomaterials with similar properties to poly(ethylene glycol). These water-soluble or amphiphilic polymers are non-toxic and attractive for biomedical applications. According to various papers and patents, they are safe for intravenous use in rats and are favorable biopolymers. Catalytic ring-opening polymerisation (CROP) produces POx polymers, which are ideal for drug delivery, gene therapy, tissue engineering, and antimicrobial surfaces due to their biocompatibility, stealth behaviour, stability, and solubility. They also provide precise molecular structure and functionalisation options. Living cationic ring-opening polymerization of 2-oxazolines is used to create POx. The range of easily accessible or produced 2-oxazoline monomers makes it possible to introduce various functions, adjust polymer characteristics, and access various polymer topologies. Well-defined polymers with a limited molar mass distribution and great end-group integrity are made feasible by CROP’s living nature. They can be refined at the CROP stage through suitable comonomer selection and ratio, microstructure and chain topology, or the introduction of functional end groups via a suitable initiator or terminating molecule. POx is being researched as a possible platform for designing biomaterials, especially polymer treatments. Research on POx-based polymer therapeutics has revived as a hot topic. As it develops, POx is expected to play a key role in next-generation biomaterials that accurately and effectively address critical healthcare demands. This review aims to overview POx types, synthesis methods, functionalization methods, applications in drug delivery and patents.

Field emission (FE) is an important electron emission mechanism for vacuum microelectronic devices; however, conventional emitters often exhibit high turn-on fields, limited emission stability, and degradation during prolonged operation. Two-dimensional MXenes have recently emerged as promising FE materials due to their remarkable electrical conductivity, comparatively low work function, and tunnelable surface terminations. Nevertheless, most studies have focused on Ti3C2T x MXene, while other compositions such as Ti2CT x remain largely unexplored, particularly regarding the influence of surface terminal groups on their electronic and emission properties. Herein, Ti2CT x (T x = -F2, -O2, -OF) MXene was synthesized via selective chemical etching of the Ti2AlC MAX phase and investigated for its FE characteristics. The material exhibits a polycrystalline hexagonal layered structure with a high specific surface area of 349.02 m2 g-1. Ti2CT x MXene demonstrates excellent FE performance with a low turn-on field of 1.56 V mu m-1 and a threshold field of 2.0 V mu m-1 (at 10 mu A cm-2), along with a stable emission for 4 h. Density functional theory calculations further reveal that surface terminations strongly influence the electronic structure and work function, with the experimental value of 4.66 eV closely matching the -F2 termination. These results highlight Ti2CT x MXene as a promising material for stable and efficient FE applications.

The paper aims at providing a modeling framework on the barriers, which influence the retail investment decision in the initial public offering (IPO) market. Based on a structured literature review and nominal group-based expert discussions, eight major barriers were identified and they were assessed through the grey-DEMATEL methodology. The findings point out that information asymmetry and higher pricing by issuers are the most prominent barriers. High information asymmetry and a lack of understanding of IPO issuers' future prospects force investors to make decisions based on heuristics rather than on reliable and relevant information. The absence of a standard valuation method compels uninformed investors to perform a subjective valuation of IPO issuers. Investors become unable to distinguish between underpriced and overpriced IPOs. Thus, this study provides a supporting explanation for the "winner's curse" and the "hot issue market" phenomena. The current study suggests that the establishment of a favorable investment environment is an ongoing activity, and the regulators have to intervene frequently in order to achieve optimality.

The incorporation of recycled polymers in additive manufacturing offers a sustainable pathway to reduce plastic waste while enabling cost-effective production of functional components. High-density polyethylene (HDPE) is an attractive material for 3D printing owing to its low density, chemical resistance, and recyclability. However, its high crystallinity and tendency to shrink often lead to warpage and poor interlayer adhesion. This study proposes a blend-based strategy to overcome these limitations by incorporating tough linear low-density polyethylene (LLDPE) into recycled HDPE (rHDPE). The blends were prepared through melt extrusion, and their mechanical, rheological, morphological, and thermal properties, and 3D printability were comprehensively evaluated. rHDPE/LLDPE blends showed a remarkable improvement in elongation at break (from 3.2% to 84.7%) and toughness without compromising tensile strength. Addition of LLDPE led to a moderate reduction in the crystallinity of rHDPE/LLDPE blends (from 69% to 52.5%) but a significant reduction in the warpage of 3D printed samples (from 27% to 7.6%). Rheological and morphological analyses confirmed good miscibility and uniform phase distribution in the blends, with increasing LLDPE content enhancing complex viscosity and melt elasticity. Overall, the rHDPE/LLDPE blends exhibit superior mechanical performance and reduced warpage, establishing their potential for FFF 3D printing applications.

Biomedical image processing plays a crucial role in disease diagnosis, treatment planning, and clinical decision-making. However, the inherent challenges of noise, poor contrast, and complex structures often limit the accuracy of traditional image analysis techniques. Metaheuristic algorithms have emerged as powerful optimisation tools that mimic natural or social phenomena to solve such complex problems efficiently. This review investigates the recent advancements, advantages, and limitations of metaheuristic algorithms applied in biomedical image processing. A systematic literature search was conducted across Scopus, PubMed, IEEE Xplore, and SpringerLink databases for studies published between 2019 and 2025. Articles focusing on the application of metaheuristic algorithms in biomedical image segmentation, feature extraction, image registration, and disease detection were included. Data were extracted and analysed based on algorithm type, biomedical application area, performance metrics, and observed challenges. The review identified over 72 recent studies implementing metaheuristic algorithms such as Genetic Algorithm, Particle Swarm Optimisation, Grey Wolf Optimisation, Firefly Algorithm, Whale Optimisation, and hybrid variants. These algorithms have demonstrated superior robustness and adaptability in handling complex and noisy biomedical images. Metaheuristics significantly improved segmentation accuracy, feature selection efficiency, and disease classification performance across multiple datasets. Nonetheless, challenges remain in computational cost, parameter tuning, scalability, and convergence stability, particularly for high-dimensional or real-time biomedical data. Metaheuristic algorithms offer promising solutions for optimising biomedical image analysis through flexible, adaptive, and data-driven mechanisms. Despite their effectiveness, achieving optimal generalisation and reducing computational complexity remain active research areas. Future work should focus on hybrid frameworks that integrate metaheuristics with deep learning and fuzzy logic for interpretable, accurate, and scalable biomedical image processing applications.