Adaptive Light Scattering Data Collection for Precision
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Solution Overview
Problem
Current dynamic light scattering and electrophoretic mobility detection techniques require fixed integration times and a large number of acquisitions, which can lead to inefficient data collection and potential sample degradation, as users must adjust parameters based on standard deviation analysis after multiple measurements, without a priori knowledge of the required accuracy for unknown samples.
Innovation Solution
A computer-implemented method and system that dynamically adjust acquisition parameters, such as integration time and number of acquisitions, based on real-time computation of standard error thresholds, allowing for adaptive data collection to achieve desired statistical accuracy, thereby improving measurement precision and reducing collection time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed integration times and large number of acquisitions are used, then measurement precision is improved, but measurement time and sample degradation increase
Solution Approach 1:
The patent implements dynamic adjustment of integration time and number of acquisitions based on real-time standard error calculations. The system transitions from fixed parameters to adaptive parameters that automatically optimize the balance between measurement precision and acquisition time, stopping data collection when the desired precision threshold is achieved.
Solution Approach 2:
The system continuously monitors the standard error of measurements and uses this feedback to adjust the number of acquisitions and integration time. When the standard error falls below a predetermined threshold, the system automatically stops acquiring data, creating a closed-loop control system that optimizes measurement efficiency.
2Measurement precision
If fixed integration times and large number of acquisitions are used, then measurement precision is improved, but sample degradation increases
Solution Approach 1:
The system dynamically adjusts the number of acquisitions and integration time based on real-time standard error calculations, transitioning from fixed to adaptive parameters. This reduces unnecessary prolonged exposure of samples to measurement conditions, thereby minimizing sample degradation while achieving the required precision.
Solution Approach 2:
The continuous monitoring of standard error provides feedback that allows the system to stop acquisitions once the desired precision is reached, preventing excessive measurement time that would otherwise cause sample degradation. The feedback mechanism ensures precision is achieved with minimal exposure.
3Measurement precision
If users adjust parameters based on standard deviation analysis after multiple measurements, then measurement accuracy can be optimized, but measurement time increases due to lack of a priori knowledge
Solution Approach 1:
The system performs preliminary calculations of required acquisitions based on predetermined standard error thresholds before completing the full measurement sequence. This allows the system to anticipate when the desired precision will be achieved and stop acquisitions accordingly, eliminating the need for post-measurement parameter adjustments.
Solution Approach 2:
Real-time feedback on standard error calculations enables the system to automatically determine when sufficient precision has been achieved, eliminating the need for users to perform multiple test measurements and manually adjust parameters. The feedback loop provides a priori knowledge of when to stop acquisitions.
Data Source
AI summary
The present disclosure describes a method, system, and computer program product comprising a process whereby measurement data collected by an instrument is collected adaptively until a desired relative or absolute accuracy is achieved.