The challenge of automating and summarizing analysis of particles in multiple AFM datasets

M. Cognard
Digital Surf,

Keywords: AFM, particle analysis, automation, template, classification, statistics, interactive tools, analysis software


Atomic force microscopy (AFM) has emerged as a powerful tool for characterizing particle morphology, size, and shape at the nanoscale. The analysis of AFM images, however, can be time-consuming, error-prone and particularly challenging for large datasets or complex particle populations. Automated particle analysis tools are emerging as a promising solution to these challenges, enabling researchers to efficiently and accurately extract meaningful insights from AFM data. Challenges of automating analysis of particles from multiple AFM datasets Automating particle analysis from multiple AFM datasets presents several significant challenges: 1. Heterogeneity of particle samples: AFM datasets can widely vary terms of particle morphology, size distribution and surface properties, making it difficult to develop a universal automated analysis method. 2. Data complexity and scalability: samples can contain thousands of particles, presenting computational challenges for automated tools to handle such large volumes of data efficiently. 3. Subjectivity and ambiguity of AFM data: AFM images often contain subtle features that can be interpreted differently by human analysts, leading to discrepancies in automated analyses. Addressing the challenges with innovative methods Dedicated, professional AFM analysis software is key in developing innovative approaches to address the challenges of automating particle analysis: 1. Specific tools allow the user to save custom workflows adapted to their application. These can be reapplied to similar datasets, saving precious time. 2. Statistical documents provide a framework for quantifying particle properties. By integrating statistical analyses, analysis software allows a better understanding of particle distribution, interactions and geometric characteristics. 3. Interactive classification techniques enable particles to be grouped based on their common properties. This classification can be used to identify trends and correlations between particle properties and other parameters, such as material formulation or manufacturing process. Advantages of automated particle analysis Automated particle analysis offers several advantages: 1. Enhanced efficiency: automated tools significantly reduce analysis time and labor costs. 2. Improved accuracy and reliability: consistent particle identification and measurement with minimized errors and bias. 3. Quantitative insights and statistical evaluation: automated analysis provides a systematic and quantitative understanding of particle properties. Applications of automated particle analysis Automated particle analysis has wide-ranging applications: • Drug discovery and delivery: particle size, shape, and aggregation analysis optimize drug formulation and bioavailability. • Materials science: characterizing particle properties and interactions guides new material development. • Biology and bioengineering: nanoscale particle interactions reveal biological processes and disease mechanisms. • Environmental science: particle distribution and morphology analysis allow assessment of pollution levels and tracking of environmental changes. • Manufacturing and quality control: automated particle analysis monitors particle size, shape, and distribution for product quality assurance.