An algorithm for three-dimensional Monte-Carlo simulation of charge distribution at biofunctionalized surfaces

An innovative experimental and mathematical approach in electrochemical sensing for mapping a drug sensor landscape

Our study delves into the examination of an electrochemical sensor through both experimentation and mathematical analysis. The sensor demonstrates the ability to identify a specific antipsychotic medication, namely Chlorpromazine Hydrochloride (CPH), even at incredibly low concentrations, reaching the picomolar level. The identification process relies on the utilization of a Glassy Carbon Electrode (GCE) that has been modified with a ceria-doped zirconia (CeO2/ZrO2) nanocomposite. The nanocomposite was synthesized using the co-precipitation technique and extensively characterized through various analytical methods. It is crucial to detect the presence of CPH as an overdose can result in hyperactivity and severe bipolar disorders among both children and adults. The average size of the nanocomposite was estimated to be 10 nm. The electrode surface area after CeO2/ZrO2 modification of the GCE was found to be 0.059 cm2, which was significantly higher than the electrode surface area of the bare GCE (0.0307 cm2). The limit of detection and limit of quantification for CPH were calculated to be 99.3 pM and 3.010 nM, respectively, with the linear dynamic range of CPH detection found to be between 0.10 and 1.90 μM. The modified sensor electrode was tested on human urine samples with good recoveries and exhibited high selectivity, repeatability, reproducibility, and long-term stability. The experimental voltammograms and the simulated stochastic voltammograms exhibited a fair amount of agreement. Examination of the experimental findings alongside analytical and numerical solutions enables a comprehensive analysis of the factors influencing the outcome of electrochemical measurements. The precise findings can be leveraged for the development of efficient sensing devices for medical diagnostics and environmental monitoring.

Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks

Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.

Optimal design of nanowire field-effect troponin sensors

We propose a design strategy for affinity-based biosensors using nanowires for sensing and measuring biomarker concentration in biological samples. Such sensors have been shown to have superior properties compared to conventional biosensors in terms of LOD (limit of detection), response time, cost, and size. However, there are several parameters affecting the performance of such devices that must be determined. In order to solve the design problem, we have developed a comprehensive model based on stochastic transport equations that makes it possible to optimize the sensing behavior.

Brownian-motion based simulation of stochastic reaction-diffusion systems for affinity based sensors

In this work, we develop a 2D algorithm for stochastic reaction-diffusion systems describing the binding and unbinding of target molecules at the surfaces of affinity-based sensors. In particular, we simulate the detection of DNA oligomers using silicon-nanowire field-effect biosensors. Since these devices are uniform along the nanowire, two dimensions are sufficient to capture the kinetic effects features. The model combines a stochastic ordinary differential equation for the binding and unbinding of target molecules as well as a diffusion equation for their transport in the liquid. A Brownian-motion based algorithm simulates the diffusion process, which is linked to a stochastic-simulation algorithm for association at and dissociation from the surface. The simulation data show that the shape of the cross section of the sensor yields areas with significantly different target-molecule coverage. Different initial conditions are investigated as well in order to aid rational sensor design. A comparison of the association/hybridization behavior for different receptor densities allows optimization of the functionalization setup depending on the target-molecule density.

Fluctuations due to association and dissociation processes at nanowire-biosensor surfaces and their optimal design

In this work, we calculate the effect of the binding and unbinding of molecules at the surface of a nanowire biosensor on the signal-to-noise ratio of the sensor. We model the fluctuations induced by association and dissociation of target molecules by a stochastic differential equation and extend this approach to a coupled diffusion-reaction system. Where possible, analytic solutions for the signal-to-noise ratio are given. Stochastic simulations are performed wherever closed forms of the solutions cannot be derived. Starting from parameters obtained from experimental data, we simulate DNA hybridization at the sensor surface for different target molecule concentrations in order to optimize the sensor design.

Kinetic parameter estimation and fluctuation analysis of CO at SnO2 single nanowires

In this work, we present calculated numerical values for the kinetic parameters governing adsorption/desorption processes of carbon monoxide at tin dioxide single-nanowire gas sensors. The response of such sensors to pulses of 50 ppm carbon monoxide in nitrogen is investigated at different temperatures to extract the desired information. A rate-equation approach is used to model the reaction kinetics, which results in the problem of determining coefficients in a coupled system of nonlinear ordinary differential equations. The numerical values are computed by inverse-modeling techniques and are then used to simulate the sensor response. With our model, the dynamic response of the sensor due to the gas-surface interaction can be studied in order to find the optimal setup for detection, which is an important step towards selectivity of these devices. We additionally investigate the noise in the current through the nanowire and its changes due to the presence of carbon monoxide in the sensor environment. Here, we propose the use of a wavelet transform to decompose the signal and analyze the noise in the experimental data. This method indicates that some fluctuations are specific for the gas species investigated here.

Predictive simulations and optimization of nanowire field-effect PSA sensors including screening

We apply our self-consistent PDE model for the electrical response of field-effect sensors to the 3D simulation of nanowire PSA (prostate-specific antigen) sensors. The charge concentration in the biofunctionalized boundary layer at the semiconductor-electrolyte interface is calculated using the propka algorithm, and the screening of the biomolecules by the free ions in the liquid is modeled by a sensitivity factor. This comprehensive approach yields excellent agreement with experimental current-voltage characteristics without any fitting parameters. Having verified the numerical model in this manner, we study the sensitivity of nanowire PSA sensors by changing device parameters, making it possible to optimize the devices and revealing the attributes of the optimal field-effect sensor.

Determination of surface concentrations of individual molecule-layers used in nanoscale biosensors by in situ ATR-FTIR spectroscopy

For the development of nanowire sensors for chemical and medical detection purposes, the optimal functionalization of the surface is a mandatory component. Quantitative ATR-FTIR spectroscopy was used in situ to investigate the step-by-step layer formation of typical functionalization protocols and to determine the respective molecule surface concentrations. BSA, anti-TNF-α and anti-PSA antibodies were bound via 3-(trimethoxy)butylsilyl aldehyde linkers to silicon-oxide surfaces in order to investigate surface functionalization of nanowires. Maximum determined surface concentrations were 7.17 × 10(-13) mol cm(-2) for BSA, 1.7 × 10(-13) mol cm(-2) for anti-TNF-α antibody, 6.1 × 10(-13) mol cm(-2) for anti-PSA antibody, 3.88 × 10(-13) mol cm(-2) for TNF-α and 7.0 × 10(-13) mol cm(-2) for PSA. Furthermore we performed antibody-antigen binding experiments and determined the specific binding ratios. The maximum possible ratio of 2 was obtained at bulk concentrations of the antigen in the μg ml(-1) range for TNF-α and PSA.

Optimization of nanowire DNA sensor sensitivity using self-consistent simulation

In order to facilitate the rational design and the characterization of nanowire field-effect sensors, we have developed a model based on self-consistent charge-transport equations combined with interface conditions for the description of the biofunctionalized surface layer at the semiconductor/electrolyte interface. Crucial processes at the interface, such as the screening of the partial charges of the DNA strands and the influence of the angle of the DNA strands with respect to the nanowire, are computed by a Metropolis Monte Carlo algorithm for charged molecules at interfaces. In order to investigate the sensing mechanism of the device, we have computed the current–voltage characteristics, the electrostatic potential and the concentrations of electrons and holes. Very good agreement with measurements has been found and optimal device parameters have been identified. Our approach provides the capability to study the device sensitivity, which is of fundamental importance for reliable sensing.