scholarly journals Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors

Biosensors ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 194
Author(s):  
Ivana Jokić ◽  
Zoran Djurić ◽  
Katarina Radulović ◽  
Miloš Frantlović ◽  
Gradimir V. Milovanović ◽  
...  
Keyword(s):  
Performance Metrics ◽  
False Negative ◽  
Measurement Data ◽  
Stochastic Approach ◽  
Sensor Response ◽  
Time Response ◽  
Response Analysis ◽  
Noise Performance ◽  
Analyte Detection ◽  
Adsorption Desorption

In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.

scholarly journals Analysis of Stochastic Time Response of Microfluidic Biosensors in the Case of Competitive Adsorption of Two Analytes

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 991
Author(s):  
Ivana Jokić ◽  
Zoran Djurić ◽  
Katarina Radulović ◽  
Miloš Frantlović
Keyword(s):  
Mass Transfer ◽  
Competitive Adsorption ◽  
Sensor Response ◽  
Time Response ◽  
Response Model ◽  
Desorption Process ◽  
Complex Samples ◽  
Low Concentrations ◽  
Adsorption Desorption ◽  
Stochastic Time

A model of stochastic time response of adsorption-based microfluidic biosensors is presented, that considers the competitive adsorption-desorption process coupled with mass transfer of two analytes. By using the model we analyze the expected value of the adsorbed particles number of each analyte, which determine the sensor response kinetics. The comparison with the case when only one analyte exists is used for investigation of the influence of competitive adsorption on the sensor response. The response kinetics analyzed by using the stochastic model is compared with the kinetics predicted by the deterministic response model. The results are useful for optimization of micro/nanosensors intended for detection of substances in ultra-low concentrations in complex samples.

scholarly journals Microfluidic Adsorption-Based Biosensors: Mathematical Models of Time Response and Noise, Considering Mass Transfer and Surface Heterogeneity

2021 ◽  
Author(s):  
Ivana Jokić
Keyword(s):  
Mass Transfer ◽  
Mathematical Models ◽  
Surface Heterogeneity ◽  
Time Response ◽  
Sensor Performance ◽  
Surface Areas ◽  
Microfluidic Chamber ◽  
Analyte Detection ◽  
Adsorption Desorption ◽  
Sensing Performance

Adsorption-based microfluidic sensors are promising tools for biosensing. Advanced mathematical models of time response and noise of such devices are needed in order to improve the interpretation of measurement results, and to achieve the optimal sensor performance. Here the mathematical models are presented that take into account the coupling of processes that generate the sensor signal: adsorption–desorption (AD) of the target analyte particles on the heterogeneous sensing surface, and mass transfer (MT) in a microfluidic chamber. The response kinetics and AD noise (which determines the ultimate sensing performance) of protein biosensors are analyzed, assuming practically relevant analyte concentrations, sensing surface areas and MT parameters. The condition is determined under which MT significantly influences the sensor characteristics relevant for reliable analyte detection and quantification. It is shown that the development of improved mathematical models of sensor temporal response and noise can be used as one of strategies for achieving better sensing performance.

scholarly journals Detection of Fracture Bones in X-ray Images Categorization

2020 ◽  
pp. 1-11
Author(s):  
Adigun Oyeranmi ◽  
Babatunde Ronke ◽  
Rufai Mohammed ◽  
Aigbokhan Edwin
Keyword(s):  
Feature Extraction ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
False Negative ◽  
Support Vector ◽  
Kappa Statistics ◽  
Fracture Detection ◽  
True Negative ◽  
Research Attention ◽  
X Ray

Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images.  The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool.  The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms;  K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.

Prediction of Aerodynamically Induced Blade Vibrations in a Radial Turbine Rotor Using the Nonlinear Harmonic Approach

2018 ◽  
Author(s):  
Nikola Kovachev ◽  
Christian U. Waldherr ◽  
Jürgen F. Mayer ◽  
Damian M. Vogt
Keyword(s):  
Structural Model ◽  
Measurement Data ◽  
Turbine Rotor ◽  
Forced Response ◽  
Response Analysis ◽  
Blade Vibration ◽  
Computational Costs ◽  
Alternative Approach ◽  
Turbomachinery Blades ◽  
Predicted Values

Resonant response of turbomachinery blades can lead to high cycle fatigue (HCF) if the vibration amplitudes are excessive. Accurate and reliable simulations of the forced response phenomenon require detailed CFD and FE models that may consume immense computational costs. In the present study, an alternative approach is applied, which incorporates nonlinear harmonic (NLH) CFD simulations in a one-way fluid-structure interaction (FSI) workflow for the prediction of the forced response phenomenon at reduced computational costs. Five resonance crossings excited by the stator in a radial inflow turbocharger turbine are investigated and the aerodynamic excitation and damping are predicted using this approach. Blade vibration amplitudes are obtained from a subsequent forced response analysis combining the aerodynamic excitation with aerodynamic damping and a detailed structural model of the investigated turbine rotor. A comparison with tip timing measurement data shows that all predicted values lay within the range of the mistuned blade response underlining the high quality of the utilized workflow.

Fractional-order linear time invariant swarm systems: asymptotic swarm stability and time response analysis

Open Physics ◽  
2013 ◽  
Vol 11 (6) ◽  
Author(s):  
Mojtaba Soorki ◽  
Mohammad Tavazoei
Keyword(s):  
Fractional Order ◽  
Linear Time ◽  
Sufficient Conditions ◽  
Time Response ◽  
Response Analysis ◽  
Order Linear ◽  
Linear Time Invariant ◽  
Time Response Analysis ◽  
Necessary And Sufficient ◽  
Time Invariant

AbstractThis paper deals with fractional-order linear time invariant swarm systems. Necessary and sufficient conditions for asymptotic swarm stability of these systems are presented. Also, based on a time response analysis the speed of convergence in an asymptotically swarm stable fractional-order linear time invariant swarm system is investigated and compared with that of its integer-order counterpart. Numerical simulation results are presented to show the effectiveness of the paper results.

A Hybrid Method for Measuring Heat Flux

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
David O. Hubble ◽  
Tom E. Diller
Keyword(s):  
Heat Flux ◽  
Hybrid Method ◽  
Sensor Response ◽  
Time Response ◽  
Heat Flux Sensor ◽  
Response Accuracy ◽  
Temporal Response ◽  
Heat Flux Sensors ◽  
Flux Measurements ◽  
Flux Sensor

The development and evaluation of a novel hybrid method for obtaining heat flux measurements is presented. By combining the spatial and temporal temperature measurements of a heat flux sensor, the time response, accuracy, and versatility of the sensor is improved. Sensors utilizing the hybrid method are able to make heat flux measurements on both high and low conductivity materials. It is shown that changing the thermal conductivity of the backing material four orders of magnitude causes only an 11% change in sensor response. The hybrid method also increases the time response of heat flux sensors. The temporal response is shown to increase by up to a factor of 28 compared with a standard spatial sensor. The hybrid method is tested both numerically and experimentally on both high and low conductivity materials and demonstrates significant improvement compared with operating the sensor as a spatial or temporal sensor alone.

Sign in / Sign up

Export Citation Format

Share Document