# A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

arXiv: https://arxiv.org/abs/2410.01213 · HTML: https://mlsanity.com/paper/2410.01213/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2308.07624/) (2023)
- [Overview: Computer vision and machine learning for microstructural characterization and analysis](https://mlsanity.com/paper/2005.14260/) (2020)
- [High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel](https://mlsanity.com/paper/1805.08693/) (2018)
- [Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments](https://mlsanity.com/paper/2312.07935/) (2023)
- [Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data](https://mlsanity.com/paper/2001.05022/) (2020)
- [mathrmSAMMed: A medical image annotation framework based on large vision model](https://mlsanity.com/paper/2307.05617/) (2023)
- [Prompt-Based Segmentation at Multiple Resolutions and Lighting Conditions using Segment Anything Model 2](https://mlsanity.com/paper/2408.06970/) (2024)
- [A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials](https://mlsanity.com/paper/2201.07342/) (2022)
- [Segment Anything Model for Grain Characterization in Hard Drive Design](https://mlsanity.com/paper/2408.12732/) (2024)
- [Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope](https://mlsanity.com/paper/2406.11018/) (2024)

## Abstract (arXiv)

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationships and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology was applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalysts. The model's performance in detecting and segmenting NPs was validated across diverse heterogeneous catalyst systems, including various metals (Cu, Ru, Pt, and PtCo), supports (silica (SiO2), γ-alumina (γ-Al2O3), and carbon black), and particle diameter size distributions with means and standard deviations of 2.9 ± 1.1 nm, 1.6 ± 0.2 nm, 9.7 ± 4.6 nm, and 4 ± 1.0 nm. Additionally, the proposed machine learning (ML) approach successfully detects and segments overlapping NPs anchored on non-uniform catalytic support materials, providing critical insights into their spatial arrangements and interactions. Our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.