Laser Diffraction Parameter Selection for Reproducible Particle Sizing
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Solution Overview
Problem
Laser diffraction particle characterization is challenging for non-expert users due to potential sources of error, making it difficult to develop an appropriate measurement methodology that minimizes errors in particle size determination.
Innovation Solution
A method and apparatus for automatically selecting an optimal range for measurement parameters in laser diffraction particle characterization, using a processor to analyze particle characteristics and adjust settings such as obscuration, agitation, and sonication, with guidance for sample preparation and dispersion to achieve stable and reproducible results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-expert users perform laser diffraction particle characterization manually, then they can obtain particle size measurements, but the measurements are prone to errors due to difficulty in developing appropriate measurement methodology
Solution Approach 1:
The system automatically selects optimal measurement parameters and prepares measurement protocols without requiring expert user intervention. The processor analyzes sample characteristics and autonomously configures obscuration, agitation, and other parameters, allowing non-expert users to obtain accurate measurements through self-service automation.
Solution Approach 2:
The system automatically adjusts multiple measurement parameters (obscuration, agitation, sonication) based on sample characteristics. By dynamically changing these parameters through automated selection, the system optimizes measurement conditions for each specific sample, thereby improving measurement precision without requiring expert knowledge from the user.
2Measurement precision
If multiple measurement parameters are manually optimized, then measurement accuracy can be improved, but the complexity of the measurement process increases
Solution Approach 1:
The system combines multiple parameter optimization functions (obscuration selection, agitation settings, sonication parameters) into a single automated process. The processor integrates these separate optimization tasks and executes them together, maintaining measurement precision while reducing the apparent complexity for the user.
Solution Approach 2:
The system uses feedback from initial sample analysis to automatically adjust measurement parameters. By analyzing sample characteristics and using this information to configure optimal parameters, the system maintains high measurement accuracy without requiring the user to understand or manually optimize each parameter individually.
3Ease of operation
If automated parameter selection is implemented, then ease of use for non-expert users is improved, but the extent of automation increases system complexity
Solution Approach 1:
The processor acts as an intermediary between the user and the complex measurement parameters. It translates simple user inputs into optimized measurement configurations, providing ease of use while managing the complexity of automation through this intermediate layer that handles parameter selection and system configuration.
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 non-expert users to achieve stable and reproducible particle size measurements by automating the selection of optimal measurement parameters, reducing errors and improving the reliability of laser diffraction analysis.
Implementation Method 1
The particles scatter the light beam to produce scattered light. The pattern of scattered light (i.e. the distribution of intensity of scattered light at different scattering angles with respect to the incident light beam) is characteristic of certain properties of the particles.
Implementation Method 2
Laser diffraction particle characterisation is a technique for characterising particles. In laser diffraction particle characterisation, a light beam (which may come from an LED or laser diode) is incident on a sample comprising particles suspended in a fluid diluent.
Data Source
AI summary
A method of automatically selecting an optimal value or range for at least one measurement parameter for particle characterisation by laser diffraction is disclosed. The method comprises receiving a plurality of measurements and associated measurement parameters, wherein the measurements are performed on at least one particulate sample by using laser diffraction to determine at least one particle characteristic, and wherein the plurality of measurements were obtained using a plurality of values for the at least one measurement parameter. The method further comprises using a processor to automatically select, based on the at least one particle characteristic and the associated measurement parameters, an optimal value or range of at least one measurement parameter for performing particle characterisation by laser diffraction.