Micro, Volume 5, Issue 4 (December 2025) – 6 articles

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. Full article

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. Full article

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. Full article

Raman spectroscopy is a rapid, label-free, and non-destructive technique for probing molecular structures, making it a powerful tool for clinical pathogen identification. However, interpreting its complex spectral data remains challenging. In this study, we evaluate and compare a suite of machine learning models—including Support Vector Machines (SVM), XGBoost, LightGBM, Random Forests, k-nearest Neighbors (k-NN), Convolutional Neural Networks (CNNs), and fully connected Neural Networks—with and without Principal Component Analysis (PCA) for dimensionality reduction. Using Raman spectral data from 30 clinically important bacterial and fungal species that collectively account for over 90% of human infections in hospital settings, we conducted rigorous hyperparameter tuning and assessed model performance based on accuracy, precision, recall, and F1-score. The SVM with an RBF kernel combined with PCA emerged as the top-performing model, achieving the highest accuracy (0.9454) and F1-score (0.9454). Ensemble methods such as LightGBM and XGBoost also demonstrated strong performance, while CNNs provided competitive results among deep learning approaches. Importantly, interpretability was achieved via SHAP (Shapley Additive exPlanations), which identified class-specific Raman wavenumber regions critical to prediction. These interpretable insights, combined with strong classification performance, underscore the potential of explainable AI-driven Raman analysis to accelerate clinical microbiology diagnostics, optimize antimicrobial therapy, and improve patient outcomes. Full article

Introduction: Hospital-acquired infections remain a significant healthcare concern due to the persistence of pathogens such as Staphylococcus aureus and Escherichia coli on frequently touched surfaces. Conventional TiO₂ coatings are limited to UV activation, which restricts their application under normal indoor light. Combining TiO₂ with ZnO and employing green synthesis methods may overcome these limitations. Methodology: Biogenic TiO₂ and ZnO nanoparticles were synthesized using Bacillus subtilis under mild aqueous conditions. The nanoparticles were characterized by SEM, XRD, UV-Vis, and FTIR, confirming nanoscale size, crystalline phases, and organic capping. A multilayer TiO₂/ZnO coating was fabricated on glass substrates through layer-by-layer deposition. Antibacterial activity was tested against S. aureus and E. coli using disk diffusion, direct contact assays, ROS quantification (FOX assay), and scavenger experiments. Statistical significance was evaluated using ANOVA. Results: The TiO₂/ZnO multilayer exhibited superior antibacterial activity under visible light, with inhibition zones of ~15 mm (S. aureus) and ~12 mm (E. coli), significantly outperforming single-component coatings. Direct contact assays confirmed strong bactericidal effects, while scavenger tests verified ROS-mediated mechanisms. FOX assays detected elevated H₂O₂ generation, correlating with antibacterial performance. Discussion: Synergistic effects of band-gap narrowing, Zn²⁺ release, and ROS generation enhanced visible-light photocatalysis. The multilayer structure improved light absorption and charge separation, providing higher antimicrobial efficacy than individual oxides. Conclusion: Biogenic TiO₂/ZnO multilayers represent a sustainable, visible-light-activated antimicrobial strategy with strong potential for reducing nosocomial infections on hospital surfaces and surgical instruments. Future studies should assess long-term durability and clinical safety. Full article

The enhancement of drug delivery into the lung surfactant is facilitated by research on the interaction between drugs and the lung surfactant. Drug designers must have a thorough theoretical understanding of a drug before performing clinical tests to reduce the experimental cost. The current study uses a coarse-grained molecular dynamics (MD) approach with the MARTINI force field to parameterize the corticosteroid drug mometasone furoate, which is used to treat lung inflammation. Here, we investigate the accurate parametrization of drug molecules and validate the parameters with the help of umbrella sampling simulations. A collection of thermodynamic parameters was studied during the parametrization procedure. The Gibbs free energy gradient was used to calculate the partition coefficient value of mometasone furoate, which was approximately 10.49 based on our umbrella sampling simulation. The value was then matched with the experimental and predicted the partition coefficient of the drug, showing good agreement. The drug molecule was then delivered into the lung surfactant monolayer membrane at the alveolar air–water interface, resulting a concentration-dependent drop in surface tension while controlling the underlying continual compression–expansion of alveoli that maintains the exhalation–inhalation respiratory cycle. The dynamical properties of the monolayer demonstrate that the drug’s capacity to diffuse into the monolayer is considerably diminished in larger clusters, and this effect is intensified when there are more drug molecules present in the monolayer. The monolayer microstructure analysis shows that the drug concentration controls monolayer morphology. The results of this investigation may be helpful for corticosteroid drug delivery into the lung alveoli, which can be applied to comprehend how the drug interacts with lung surfactant monolayers or bilayers. Full article