AIMC Topic: Particle Size

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Predicting PLGA nanoparticle size and zeta potential in synthesis for application of drug delivery via machine learning analysis.

Scientific reports
This study employed multiple machine learning (ML) methods to model and predict key attributes of PLGA nanoparticles, specifically particle size and zeta potential. The predictions were based on input variables, including PLGA polymer type, PLGA conc...

Tracheal Targeted Nanogrid Delivery Systems of Dexamethasone Visualized by Single-Particle Tracing and Multiscale Pathological Mapping.

ACS nano
Current clinical treatment of pulmonary diseases requires an advanced three-dimensional (3D) pathological atlas of the microenvironment, particularly the trachea, which is predominantly affected by lung disorders. In this study, the gridded cyclodext...

Rapid and quantitative detection of Botryosphaeria dothidea by surface-enhanced Raman spectroscopy with size-controlled spherical metal nanoparticles combined with machine learning.

International journal of food microbiology
Botryosphaeria dothidea infection has become a major factor affecting the quality of postharvest fruits, so detection of B. dothidea infection is very important to control the spread of infection and ensure food safety. In this study, we built a moni...

Identification of nanoparticle infiltration in human breast milk: Chemical profiles and trajectory pathways.

Proceedings of the National Academy of Sciences of the United States of America
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...

Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration.

ACS sensors
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...

Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.

Pharmaceutical research
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...

Real-time component-based particle size measurement and dissolution prediction during continuous powder feeding using machine vision and artificial intelligence-based object detection.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
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...

Anticoagulation colloidal microrobots based on heparin-mimicking polymers.

Journal of colloid and interface science
Coagulation within blood vessels is a major cause of cardiovascular disease and global mortality, highlighting the urgent need for effective anticoagulant strategies. In this study, we introduce a dynamic and highly efficient anticoagulant platform, ...

Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al-Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation.

Journal of chemical information and modeling
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...

Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications.

Scientific reports
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...