AI-based nanoparticle classification from standard tracking analysis without new hardware

On July 7, 2026, researchers from the University of Tokyo and the Innovation Center of NanoMedicine unveiled a groundbreaking artificial intelligence (AI) method for classifying nanoparticle morphology using standard nanoparticle tracking analysis (NTA). This innovative approach achieved classification accuracies exceeding 80% without requiring modifications to existing hardware.

Advancements in Nanoparticle Morphology Analysis

The study, published in ACS Applied Nano Materials, highlights the challenges of characterizing nanoparticle morphology in liquid environments. Traditional methods like transmission electron microscopy offer detailed insights but necessitate drying or immobilizing samples, rendering them unsuitable for rapid evaluations. In contrast, NTA focuses on measuring particle size by recording the Brownian motion of individual nanoparticles suspended in liquid.

Despite its widespread use, most NTA analyses primarily rely on trajectory information, often overlooking valuable data contained in scattered-light intensity signals. The research team addressed this gap by developing a deep-learning framework that integrates these two types of information to enhance nanoparticle characterization.

Deep Learning Framework for Enhanced Classification

The AI framework employs a combination of a one-dimensional convolutional neural network and a bidirectional long short-term memory network. This architecture enables the simultaneous learning of motion-related and optical time-series patterns, allowing for a more nuanced understanding of nanoparticle morphology. The researchers tested the method using gold nanoparticles with three distinct morphologies: spheres, rods, and plates.

In binary classification tasks involving pairs of particle types, the integrated approach consistently outperformed models reliant on a single information source. Using merely one second of measurement data, equating to 100 frames, the binary classifiers achieved accuracies exceeding 0.82. Furthermore, the multiclass classification reached an average correctness of approximately 80% for all three particle types.

Implications for Practical Applications

One of the most significant advantages of this AI approach is that it does not necessitate new hardware. Instead, it enhances the analytical value of data produced by existing NTA systems, paving the way for improved nanoparticle measurement workflows through software-based analysis. Professor Takanori Ichiki of the University of Tokyo and iCONM expressed optimism about the technology's potential, stating, "Our goal is to translate this technology into practical nanoparticle analysis tools by incorporating it into future NTA systems."

This method could be particularly beneficial in scenarios where sample availability is limited, such as in quality control of nanomedicines and nanoparticle-based therapeutics, characterization of extracellular vesicles, and monitoring of engineered nanoparticles. The research signifies a critical step toward more efficient and accurate nanoparticle analysis, which is vital in various fields including medicine, environmental monitoring, and advanced materials development.