Biosensor Dispersion Calibration for High-Flow Assays
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Solution Overview
Problem
Existing biosensor dispersion methods are limited by low flow rates and do not account for analyte interactions with tubing walls, leading to inaccurate concentration gradient profiles and reduced throughput.
Innovation Solution
A method that calculates an effective diffusion coefficient using a calibration function and incorporates it into a dispersion model to account for tubing wall interactions, allowing higher flow rates and more accurate concentration gradient representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low flow rates are used to comply with Taylor dispersion model, then accurate concentration gradient profile is achieved, but injection time increases and throughput decreases
Solution Approach 1:
The patent changes the flow rate parameter from low (Taylor model requirement) to high (practical throughput requirement) while compensating through calibration. The calibration function adjusts the apparent diffusion coefficient to account for deviations from Taylor model assumptions, enabling accurate concentration gradient measurement even at high flow rates that would otherwise violate model constraints.
2Productivity
If higher flow rates are used to increase throughput, then productivity improves, but dispersion event consistency with Taylor theory deteriorates
Solution Approach 1:
The patent introduces a calibration function that acts as feedback to correct for deviations from Taylor dispersion theory. By measuring actual dispersion behavior and comparing it to theoretical predictions, the calibration function adjusts the apparent diffusion coefficient to compensate for high flow rate effects, maintaining model reliability even when operating conditions exceed traditional theoretical limits.
3Device complexity
If analyte interactions with tubing wall are not accounted for, then model simplicity is maintained, but residence time accuracy and concentration gradient profile deteriorate
Solution Approach 1:
The patent introduces a calibration function as an intermediary element that mediates between the simple Taylor dispersion model and the complex reality of wall interactions. This calibration layer absorbs the complexity of wall effects without requiring fundamental model restructuring, allowing the base model to remain simple while achieving accurate residence time and concentration gradient measurements through the calibration adjustment.
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
This approach enables faster assay run times, increased throughput, and more accurate determination of interaction parameters between analytes and ligands, while maintaining the analytical benefits of previous methods.
Implementation Method 1
the sample undergoing a mathematically-defined dispersion event thereby producing a well-characterized concentration gradient profile
Implementation Method 2
Taylor's theory of dispersion provides the basis for the mathematically-defined dispersion event
Implementation Method 3
The accumulation of the resulting affinity complexes at the sensing region is detected by a label-free detection method selected from the group consisting of evanescent filed-based optical refractometers, surface plasmon resonance (SPR), optical interferometers
Data Source
AI summary
Dispersion injection methods for determining biomolecular interaction parameters in label-free biosensing systems are provided. The methods generally relate to the use of a single analyte injection that generates a smoothly-varying concentration gradient via dispersion en route to a sensing region possessing an immobilized binding partner. The present method incorporates the use of an internal standard which provides a reference as to the dispersion conditions present which can then be used to calculate an effective diffusion coefficient for the analyte of interest based on a universal calibration function. The effective diffusion coefficient can then be incorporated into the appropriate dispersion model to provide a calibrated dispersion model. The calibrated dispersion model can then be incorporated into the desired interaction model to provide a reliable representation of the analyte concentration at the sensing region at any time during the injection. The use of the internal standard and universal calibration function permit use of a wide range of injection conditions which may not otherwise be consistent with a particular dispersion model. Thus, the present methods allow for higher flow rates and lower sample volumes thereby increasing assay speed and decreasing sample consumption.