Neurocomputational Electrochemical Sensor Drift Compensation
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Solution Overview
Problem
Current biochemical analytical systems face challenges with sensor drift, cross-sensitivities, and miniaturization limitations, leading to unreliable and costly monitoring devices that are not suitable for real-time, on-site applications due to increased noise and decreased stability with downscaling.
Innovation Solution
A neurocomputational electrochemical sensing device integrating solid-state electrochemical sensors and a readout circuit with an artificial neural network processor for self-calibration and miniaturization, enabling efficient prediction of biochemical properties and reducing maintenance costs through local signal processing and automated fluidics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are miniaturized and packed into arrays for real-time monitoring, then measurement capability and sensitivity are improved, but noise increases and stability decreases
Solution Approach 1:
The system divides the sensing function into multiple independent sensors arranged in arrays, where each sensor contributes to the overall measurement capability. This segmentation allows parallel processing of multiple measurement streams, improving sensitivity while distributing the stability challenges across individual sensor elements that can be independently managed through the neural network compensation.
Solution Approach 2:
The artificial neural network implements continuous feedback compensation for sensor drift and cross-sensitivities. The system monitors sensor outputs and dynamically adjusts measurements by compensating for temperature effects, aging drift, and cross-sensitivity to interfering species, thereby maintaining stability despite miniaturization challenges.
2Measurement precision
If sensor density is increased for better measurement coverage, then measurement precision is improved, but device complexity and processing burden increase
Solution Approach 1:
The artificial neural network serves multiple functions simultaneously: it compensates for sensor drift, corrects cross-sensitivity errors, performs data fusion from multiple sensors, and enables prediction of biochemical parameters. This multi-functionality consolidates what would otherwise require separate processing systems, managing the complexity inherent in high-density sensor arrays.
Solution Approach 2:
The system transforms raw sensor signals into compensated measurements by applying parameter adjustments based on neural network predictions. The neural network learns optimal compensation parameters for drift and cross-sensitivity, dynamically adjusting measurement parameters to maintain accuracy across varying operational conditions without increasing hardware complexity.
3Measurement precision
If classical chemometric solutions are used for offline analytics, then measurement accuracy is improved, but response time and energy consumption increase
Solution Approach 1:
The artificial neural network is pre-trained offline using classical chemometric approaches and large datasets to learn optimal compensation strategies and prediction models. Once trained, the network executes predictions in real-time with minimal computational overhead, combining the accuracy benefits of offline chemometric analysis with the speed required for real-time monitoring applications.
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
The device provides reliable, cost-effective, and energy-efficient multiparametric analysis capable of real-time monitoring in tiny/mobile locations, overcoming sensor non-idealities and enabling precise sensing of multiple parameters with reduced latency and energy consumption.
Implementation Method 1
The device comprises an artificial neural network processor that is configured to be trained to compensate for sensor drift and cross-sensitivities
Implementation Method 2
Conceived to sense and qualify biological and chemical information beyond human capabilities, biochemical analytical systems are in rising demand
Data Source
AI summary
A neurocomputational electrochemical sensing device (1) is proposed for predicting properties of a substance. The device (1) comprises: a plurality of electrochemical sensors constituting a sensor array (3), the sensors being sensitive to sensed attributes advantageous to predict a set of properties of interest of the substance, each sensor being configured to output a sensor output signal indicative of a sensor response of the respective sensor to measurable changes in the sensed attributes of the substance; a readout circuit (5) for biasing the sensors and for conditioning the sensor output signals into readout circuit output signals to facilitate further processing of the sensor responses; and an artificial neural network processor (7) for processing the readout circuit output signals, the processor (7) comprising neurons interconnected by synapses, the processor (7) being configured to output a set of processor output signals whose signal values are indicative of the properties to predict. The sensor array (3) comprises first electrochemical sensors selective to properties correlated with the desired properties to predict, and second electrochemical sensors sensitive primarily to the main interferents of the substance. The neurons and/or the synapses are configured to be trained to compensate for sensor drift and/or cross-sensitivities upon generating the processor output signals.