PhD Project by Yuxuan Zhang
Project Title: Effective Characterization of Nanoscale Airborne Particles
Group: Microstructural Analysis of Materials Processes in the SEM
Supervisor: Alice Bastos Da Silva Fanta, Jakob Schiøtz
Project Description
Exposure to fine airborne particles is estimated to cause 3 million deaths globally every year. Moreover, the advancement and increasing usage of nanomaterials in various sectors are driving the size of these harmful particles to a even smaller dimension. To study and understand their interactions with the environment and human body, it is crucial to be able to characterize the size, morphology, composition, phase and structure of these particles to prevent and control their exposures.
Currently, there exists multiple approaches to obtain these information, yet none of them can serve as an holistic solution on its own to characterize the pollutants and harmful particles across the broad range of sizes and compositions. Out of these approaches, one prominent technique is to extract information from the electron diffraction (ED) pattern, which can be done in both transmission electron microscope (TEM) and scanning electron microscope (SEM).
Traditionally, the ED signal in SEM is mainly composed of the Kikuchi lines generated from a thick sample (e.g. >200 nm metallic particles); while in TEM, the ED signal consists mainly of the spot patterns from thin/light sample (e.g. organic crystal). The two distinct patterns originate from the inherent difference in their formation mechanism and the instrument/sample constrains. Recently, the implementation and advancement of on-axis transmission Kikuchi diffraction (TKD) technique ushers in a new future, where the acquisition and analysis of both Kikuchi and spot patterns become possible in SEM.
In this project, with the advanced microscopy facility from DTU Nanolab and the super computer from DTU Physics, we aim at combining the characterization techniques from both TEM and SEM to produce a reliable high-throughput solution inside a single SEM instrument with the aid of machine learning to enable the indexing and characterization of nanoscale airborne particles across all ranges, facilitating the research to protect human health.
Group: Microstructural Analysis of Materials Processes in the SEM
Supervisor: Alice Bastos Da Silva Fanta, Jakob Schiøtz
Project Description
Exposure to fine airborne particles is estimated to cause 3 million deaths globally every year. Moreover, the advancement and increasing usage of nanomaterials in various sectors are driving the size of these harmful particles to a even smaller dimension. To study and understand their interactions with the environment and human body, it is crucial to be able to characterize the size, morphology, composition, phase and structure of these particles to prevent and control their exposures.
Currently, there exists multiple approaches to obtain these information, yet none of them can serve as an holistic solution on its own to characterize the pollutants and harmful particles across the broad range of sizes and compositions. Out of these approaches, one prominent technique is to extract information from the electron diffraction (ED) pattern, which can be done in both transmission electron microscope (TEM) and scanning electron microscope (SEM).
Traditionally, the ED signal in SEM is mainly composed of the Kikuchi lines generated from a thick sample (e.g. >200 nm metallic particles); while in TEM, the ED signal consists mainly of the spot patterns from thin/light sample (e.g. organic crystal). The two distinct patterns originate from the inherent difference in their formation mechanism and the instrument/sample constrains. Recently, the implementation and advancement of on-axis transmission Kikuchi diffraction (TKD) technique ushers in a new future, where the acquisition and analysis of both Kikuchi and spot patterns become possible in SEM.
In this project, with the advanced microscopy facility from DTU Nanolab and the super computer from DTU Physics, we aim at combining the characterization techniques from both TEM and SEM to produce a reliable high-throughput solution inside a single SEM instrument with the aid of machine learning to enable the indexing and characterization of nanoscale airborne particles across all ranges, facilitating the research to protect human health.
Contact
Alice Bastos da Silva Fanta Senior Researcher
Contact
Yuxuan Zhang PhD Student