Proceedings of the National Academy of Sciences of the United States of America
May 12, 2025
Breast milk is crucial for infant health, offering essential nutrients and immune protection. However, despite increasing exposure risks from nanoparticles (NPs), their potential infiltration into human breast milk remains poorly understood. This stu...
As an emerging atmospheric monitoring technology, low-cost sensors for particulate matter of diameters below 2.5 μm (PMLCSs) supplement traditional air quality monitoring instruments. Because their stability and accuracy are typically low, they requi...
PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, id...
Traditional cloudy goji berry beverages (CGBs) preparation methods often cause irreversible phase separation during sterilization and require high homogenization pressures. However, combined ultrasound and homogenization (US-HPH) technology achieves ...
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Mar 20, 2025
This work presents a system, in which machine vision combined with artificial intelligence-based image analysis was used to determine the component-based particle size distribution of pharmaceutical powder blends. The blends consisted of acetylsalicy...
Journal of chemical information and modeling
Mar 14, 2025
Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomi...
This study investigates utilization of machine learning for the regression task of predicting the size of PLGA (Poly lactic-co-glycolic acid) nanoparticles. Various inputs including category and numeric were considered for building the model to predi...
BACKGROUND: FTIR microspectroscopy is a popular non-destructive technique for chemical analysis and identification of microparticles, such as microplastics, pollen, spores, microplankton organisms, sediments and microfossils. Unfortunately, measured ...
International journal of pharmaceutics
Feb 15, 2025
We propose a novel approach for predicting the solid fraction after roller compaction processes. It is crucial to predict and control the solid fraction, as it has a significant impact on the product quality. The solid fraction can be theoretically p...
To further reduce atmospheric particulate matter concentrations, there is a need for a more precise identification of their sources. The SEM-EDS technology (scanning electron microscopy and energy-dispersive X-ray spectroscopy) can provide high-resol...
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