First Principles in Action

FIDA: Flow Induced Dispersion Analysis
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FIDA technology embodies first-principles thinking.

It uses first principles of physics and fluid mechanics to analyze the movement of particles in a fluid.

1. Laminar Flow

Laminar flow is a smooth, non-turbulent flow of a fluid pushed through a capillary. In a laminar flow regime, the fluid moves in parallel streams.

2. Taylor Dispersion

Taylor dispersion describes the behaviour of small particles in the flow. As the flow is laminar, the particles will not mix evenly, but instead experience a fluctuating motion. Such a motion results in a dispersion of the particles over time and space, as they diffuse and move along the flow.

FIDA technology takes advantage of these two principles by measuring fluorescence of particles in the laminar flow and analysing their dispersion over time, which allows for calculation of the hydrodynamic radius of a particle of interest.

Would you like to dive into details? Here’s how it works:



The sample of interest is passed through a thin capillary. Due to the difference in velocity between the walls and center of the capillary, the sample shapes into a parabolic profile.



Molecules diffuse radially, away from the flow axis. The fluorescence emitted by the molecules is acquired as a Gaussian signal by a high sensitivity detection system and is plotted against time.



The size of the molecules in the sample determines their radial diffusivity, which in turn defines the extent of sample’s dispersion.

Small molecules diffuse faster and create a more compact dispersion profile. Large molecules diffuse slower, which results in a more extended dispersion profile.



This enables FIDA to detect size changes smaller than 5%. The math behind the software calculations is based on Taylor dispersion phenomena and the Stokes-Einstein equation, which provide a firm base for further calculations.

How do FIDA users benefit from the first principles?


Since FIDA is not dependent on a priori assumption or on empirical calibration, it ensures accuracy and provides flexibility in analysing diverse samples.

Data Transparency

On top of that, having direct measurements translates into high data transparency, necessary for quality control or even machine learning.


Finally, the chance that your measurements do not reflect reality, are imprecise, or are a false positive/negative is much lower in first principle technologies. To be frank, we think that first principle technologies are the future.
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