Micro

Dy@C₈₂ is one of the metallofullerenes studied as dopants for improvements of stability and performance of solar cells. Calculations should help in formulating rules for selections of fullerene endohedrals for such new applications in photovoltaics. Structure, energetics, and relative equilibrium populations of two potential-energy-lowest IPR (isolated pentagon rule) isomers of Dy@C₈₂ under high synthetic temperatures are calculated using the Gibbs energy based on molecular characteristics at the B3LYP/6-31G*∼SDD level. Dy@$C_{2v}\left(9\right)$-C₈₂ and Dy@$C_s\left(6\right)$-C₈₂ are calculated as 58 and 42%, respectively, of their equilibrium mixture at a synthetic temperature of 1000 K, in agreement with observations. The Dy@$C_{2v}\left(9\right)$-C₈₂ species is found as lower in the potential energy by 1.77 kcal/mol compared to the Dy@$C_s\left(6\right)$-C₈₂ isomer.

Hydrogel microneedles (HMNs) act as non-invasive devices that can effortlessly merge with the human body for drug delivery and diagnostic purposes. Nonetheless, their improvement is limited by intricate and repetitive issues related to material composition, structural geometry, manufacturing accuracy, and performance enhancement. At present, there are only a limited number of studies accessible since artificial intelligence and machine learning (AI/ML) for HMN are just starting to emerge and are in the initial phase. Data is distributed across separate research efforts, spanning different fields. This review aims to tackle the disjointed and narrowly concentrated aspects of current research on AI/ML applications in HMN technologies by offering a cohesive, comprehensive synthesis of interdisciplinary insights, categorized into five thematic areas: (1) material and microneedle design, (2) diagnostics and therapy, (3) drug delivery, (4) drug development, and (5) health and agricultural sensing. For each domain, we detail typical AI methods, integration approaches, proven advantages, and ongoing difficulties. We suggest a systematic five-stage developmental pathway covering material discovery, structural design, manufacturing, biomedical performance, and advanced AI integration, intended to expedite the transition of HMNs from research ideas to clinically and commercially practical systems. The findings of this review indicate that AI/ML can significantly enhance HMN development by addressing design and fabrication constraints via predictive modeling, adaptive control, and process optimization. By synchronizing these abilities with clinical and commercial translation requirements, AI/ML can act as key facilitators in converting HMNs from research ideas into scalable, practical biomedical solutions.

Size is a surface coating applied to glass fibres during manufacture, and it is arguably the most important component in a glass-reinforced composite. Research and development on sizings and composite interfaces are severely limited, because conventional laboratory- scale glass fibre sizing analysis commonly involves sample preparation by dip coating, resulting in a size layer up to two orders of magnitude thicker than industrially produced glass fibre products. This makes it difficult to make useful comparisons between industrial and lab-scale-prepared samples when investigating size performance. This paper presents a novel, but simple, use of laboratory spin coating to apply a size layer to glass fibres that are similar to industrial-sized fibres. Thermogravimetric analysis and electron microscopy were used to investigate the size layers of glass fibres spin-coated with two chemically different sizing formulations, under a range of conditions. The average size layer thickness on spin-coated glass fibres could be easily and simply controlled in a range from 0.05 to 0.6 µm, compared to 0.4–1.3 µm on samples dip coated with the same size formulation and 0.06–0.10 µm on industrial reference samples. This novel application of the spin coating method offers the potential of improved research sample preparation, as it eliminates the need to alter the concentration of the sizing formulations to unacceptably low levels to obtain normal size layer thicknesses.