Single Particle Analysis of Engineered Nanomaterials

Single particle analysis has emerged as a cornerstone of nanometrology, enabling direct measurement of individual engineered nanomaterials in diverse matrices. By resolving discrete nanoparticle events, modern techniques quantify particle size, number concentration and elemental composition with high sensitivity and specificity. These approaches address critical needs in environmental monitoring, food safety, biomedical research and product quality control, where ensemble measurements often obscure heterogeneity and trace-level components. Developments in instrumentation, data processing and sample‐preparation protocols now permit detection limits below 10 nm in favourable systems, while distinguishing dissolved species from particulate forms. Spatially resolved variants further allow imaging of nanoparticles in solid and biological substrates, linking distribution patterns to uptake pathways or material performance. Collectively, single particle methodologies underpin regulatory compliance, risk assessment and the design of next-generation nanomaterials with tailored functionalities.

Research from Nature Portfolio

Recent studies have demonstrated the power of inherent isotopic fingerprints combined with machine learning to trace silica nanoparticles back to their sources. By mapping two-dimensional silicon–oxygen isotope distributions, researchers achieved discrimination accuracies exceeding 90 % between engineered and naturally occurring particles. This methodology not only differentiates manufacturing routes and suppliers but also offers a robust framework for environmental forensics and supply-chain verification, enhancing the traceability of nanomaterials in complex systems.

Single Particle Analysis of Engineered Nanomaterials publication trend

The graph below shows the total number of articles in single particle analysis of engineered nanomaterials across all publications each year (not limited to Nature Index journals).

Technical terms

Single particle ICP-MS (spICP-MS): Technique detecting and quantifying individual nanoparticles by measuring transient ion signals in a plasma mass spectrometer.

Time-of-flight mass spectrometry (ICP-TOFMS): Mass analyser providing simultaneous multi-element detection of nanoparticles by recording ion flight times.

Laser ablation sampling (LA-SP-ICP-MS): Solid-sampling approach in which laser pulses liberate particles for direct spICP-MS analysis, enabling spatial mapping.

Isotopic fingerprinting: Method using distinctive isotope ratios to attribute nanoparticles to specific sources or synthesis methods.

Unsupervised clustering: Machine-learning algorithm that groups nanoparticles by elemental composition without pre-assigned labels.

References

1. Analytical methods for identification, characterization, and quantification of metal-containing nanoparticles in biological and biomedical samples, food and personal care products. TrAC Trends in Analytical Chemistry (2024).
2. Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning. Nature Communications (2019).
3. Emerging investigator series: automated single-nanoparticle quantification and classification: a holistic study of particles into and out of wastewater treatment plants in Switzerland. Environmental Science Nano (2021).
4. Nanoparticle Analysis in Biomaterials Using Laser Ablation−Single Particle−Inductively Coupled Plasma Mass Spectrometry. Analytical Chemistry (2019).