Nanoparticle Tracking Detection Depth Without Calibration
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Solution Overview
Problem
Nanoparticle tracking analysis (NTA) devices require instrument calibration to determine the detection region, which is time-consuming and requires precise calibration samples, posing a significant disadvantage.
Innovation Solution
An apparatus and method for determining the detection region in NTA without calibration by tracking particle movement and analyzing the change in measured properties as the size of video subsections is varied, using computational techniques to automatically determine the detection region depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instrument calibration is used to determine the detection region, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The system performs self-calibration by automatically determining the detection region depth using particle tracking analysis on the sample itself, without requiring external calibration samples or manual intervention. The computer processes video data of particles undergoing Brownian motion to computationally determine the detection region, enabling the instrument to calibrate itself during normal operation.
Solution Approach 2:
The method changes the approach from physical calibration using reference samples to computational calibration by analyzing particle movement parameters. By tracking particle trajectories and analyzing their displacement statistics across multiple frames, the system computes the detection region depth through mathematical modeling rather than physical measurement comparisons.
2Measurement precision
If instrument calibration is used to determine the detection region, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-calibration by automatically determining the detection region depth using particle tracking analysis on the sample itself, without requiring external calibration samples or manual intervention. The computer processes video data of particles undergoing Brownian motion to computationally determine the detection region, enabling the instrument to calibrate itself during normal operation.
Solution Approach 2:
The patent replaces the mechanical/physical calibration process (using calibration samples and manual adjustment) with a computational approach. The computer analyzes particle trajectories, applies mathematical models of Brownian motion, and calculates the detection region depth through algorithms, substituting physical calibration procedures with digital processing.
3Measurement precision
If calibration samples are used, then measurement precision is improved, but quantity of substance and cost increase
Solution Approach 1:
The system performs self-calibration by automatically determining the detection region depth using particle tracking analysis on the sample itself, without requiring external calibration samples or manual intervention. The computer processes video data of particles undergoing Brownian motion to computationally determine the detection region, enabling the instrument to calibrate itself during normal operation.
Solution Approach 2:
The patent makes the sample serve multiple functions: it is both the object of analysis and the calibration reference. The particles in the sample undergo Brownian motion which is used for both measuring particle properties and determining the detection region depth, eliminating the need for separate calibration samples and reducing overall material requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient determination of the detection region without the need for calibration, reducing time and resource requirements.
Implementation Method 1
collecting light scattered or fluoresced by the particles
Implementation Method 2
collecting light scattered or fluoresced by the particles
Implementation Method 3
The video is analysed frame-by-frame to track the movement of the particles, the movement caused by Brownian motion and/or bulk flow
Data Source
AI summary
An apparatus is provided for characterising particles using nanoparticle tracking analysis. The apparatus comprises: a cell for containing a sample comprising a plurality of particles suspended in a fluid; a light source configured to illuminate the sample; an imaging system configured to collect light scattered or fluoresced by particles moving within the cell and within a detection region of the imaging system and capture a video of the particles moving within the detection region; and a computer. The computer is configured to process the video to automatically determine a depth of the detection region. Determining the depth of the detection region comprises: tracking the particles moving within the detection region, thereby producing a track for each of the particles; and analysing how a measured property of the tracks within a subsection of the video changes as the size of the subsection is varied.