Nanoparticle detection with imaging techniques

Fig. 1 Identification of single particles.

Fig.2 Once identified, the algorithm tracks each particle’s motion.

At CCM ELECTRONIC ENGINEERING, they develop a method to distinguish nanoparticles from images taken in darkfield microscopy. The aim is to detect the particles’ speed and determine their size according to the Brownian motion model. Ricky Jacobsen describes the procedure for us.  

Particles are prefiltered and, for example, in a microfluidic chip guided to a microscope that takes darkfield images.  The images clearly show particles, but it is not possible to determine the size directly.

At CCM, Ricky develops an algorithm to track the motion of single particles. The first step reduces the background in the images electronically to be able to follow single particles. The second step tracks the particles, considering proximity and similarity between particles in two images (Fig. 1 and Fig. 2).

This particle tracking is particularly challenging because the particles are very similar, and pattern recognition does not work. Ricky needs to determine suitable parameters for selecting the right particles. Once the tracking has a hit, the algorithm can determine the particle speed. Using Brownian Motion theory, one can subsequently determine the particle size. To achieve the required detection accuracy, the algorithm needs to process many images, increasing the necessary amount of stored and handled data.

Interview with Jacek Fiutowski in interviews Jacek Fiutowski in an article about the save use of nanoparticles. He mentions some of the advantages of nanoparticles and some possibly hazardous effects of nanoparticles on cells. He points out that the research in a laboratory is different from the conditions in an open environment. In the project CheckNano, we observed that silver nanoparticles aggregate or dissolve in a complex environment like food within a short time.
It is also important to know how many particles enter the body. It is crucial to know the dose when the particles are hazardous for the body. But it is unknown how many particles enter the body from packaging and how long they survive inside the body.
The interview is complemented by research done at the national centre for the work environment. They studied Titaniumdioxid nanoparticles in the air because they are widely used in paints. It can be said that it is dangerous to work with nanoparticles in spray form.
Read the articles in Danish here.

Lab-on-chip (LoC) nanoparticle sorting

The analysis of nanoparticles (NPs) in biological matrices poses many challenges when trying to sample specific particles. We explored various Lab-on-chip (LoC) filtering components, with a particular focus on Field-flow fractionation (FFF) coupled with 3D dynamic light scattering (DLS) to provide size-related information. The LoC allows us to work on a small scale and with small amounts of sample solvents.

At SDU-NanoSYD we want to optimize nanoparticle detection efficiency. The particles that we want to detect are in a food matrix or other matrix of solvents. For sensitive detection, it is crucial to extract the particles from the matrix and preferably sort them by size.

We explored Field-flow fractionation (FFF) coupled with 3D dynamic light scattering (DLS) to provide size related information. The NPs, to be detected from food or other matrices of solvents, are mechanically pre-filtered by commercial syringe filters. Afterwards, based on hydrodynamic size, the NPs ranging from a few nanometers to an undefined level of micrometres, are separated. In the FFF technique, the NPs are resolved by a hydrodynamic pressure gradient which pushes the particles of different sizes into different channels.

To ensure high experimental throughput and keep the time between a theoretical concept and a real prototype as short as possible, we introduced stereolithography (SL) 3D printing as a simple prototyping method. The technique proposed here offers a low entry barrier for the rapid prototyping of microfluidics (LoC), enabling iterative design for laboratories without access to conventional soft-lithography carried on at cleanroom facilities. We have explored the diversity of photocurable resins to create a fully 3D printed, biocompatible and modular microfluidic platform.

Figure 1a shows an example of a first fully 3D printed LoC filtering unit. To check the performance, a mixture of 50 µL of concentrated Ag nanoparticles (5 mg/mL) of 50 nm and 200 nm diameter, diluted with 200 µL DI water, was processed through the filtering units and examined by a DLS method.
b) Experimental setup with a microfluidic filter in yellow.
c) shows the results of FFF filtering, where the larger particles, namely 200 nm diameter NPs where removed from the solution.