Optimization of nanowire DNA sensor sensitivity using self-consistent simulation

Nanowire-based sensor electronics for chemical and biological applications

Detection and recognition of chemical and biological species via sensor electronics are important not only for various sensing applications but also for fundamental scientific understanding. In the past two decades, sensor devices using one-dimensional (1D) nanowires have emerged as promising and powerful platforms for electrical detection of chemical species and biologically relevant molecules due to their superior sensing performance, long-term stability, and ultra-low power consumption. This paper presents a comprehensive overview of the recent progress and achievements in 1D nanowire synthesis, working principles of nanowire-based sensors, and the applications of nanowire-based sensor electronics in chemical and biological analytes detection and recognition. In addition, some critical issues that hinder the practical applications of 1D nanowire-based sensor electronics, including device reproducibility and selectivity, stability, and power consumption, will be highlighted. Finally, challenges, perspectives, and opportunities for developing advanced and innovative nanowire-based sensor electronics in chemical and biological applications are featured.

Molecular dynamics simulation of potentiometric sensor response: the effect of biomolecules, surface morphology and surface charge

The silica-water interface is critical to many modern technologies in chemical engineering and biosensing. One technology used commonly in biosensors, the potentiometric sensor, operates by measuring the changes in electric potential due to changes in the interfacial electric field. Predictive modelling of this response caused by surface binding of biomolecules remains highly challenging. In this work, through the most extensive molecular dynamics simulation of the silica-water interfacial potential and electric field to date, we report a novel prediction and explanation of the effects of nano-morphology on sensor response. Amorphous silica demonstrated a larger potentiometric response than an equivalent crystalline silica model due to increased sodium adsorption, in agreement with experiments showing improved sensor response with nano-texturing. We provide proof-of-concept that molecular dynamics can be used as a complementary tool for potentiometric biosensor response prediction. Effects that are conventionally neglected, such as surface morphology, water polarisation, biomolecule dynamics and finite-size effects, are explicitly modelled.

InP Nanowire Biosensor with Tailored Biofunctionalization: Ultrasensitive and Highly Selective Disease Biomarker Detection

Electrically active field-effect transistors (FET) based biosensors are of paramount importance in life science applications, as they offer direct, fast, and highly sensitive label-free detection capabilities of several biomolecules of specific interest. In this work, we report a detailed investigation on surface functionalization and covalent immobilization of biomarkers using biocompatible ethanolamine and poly(ethylene glycol) derivate coatings, as compared to the conventional approaches using silica monoliths, in order to substantially increase both the sensitivity and molecular selectivity of nanowire-based FET biosensor platforms. Quantitative fluorescence, atomic and Kelvin probe force microscopy allowed detailed investigation of the homogeneity and density of immobilized biomarkers on different biofunctionalized surfaces. Significantly enhanced binding specificity, biomarker density, and target biomolecule capture efficiency were thus achieved for DNA as well as for proteins from pathogens. This optimized functionalization methodology was applied to InP nanowires that due to their low surface recombination rates were used as new active transducers for biosensors. The developed devices provide ultrahigh label-free detection sensitivities ∼1 fM for specific DNA sequences, measured via the net change in device electrical resistance. Similar levels of ultrasensitive detection of ∼6 fM were achieved for a Chagas Disease protein marker (IBMP8-1). The developed InP nanowire biosensor provides thus a qualified tool for detection of the chronic infection stage of this disease, leading to improved diagnosis and control of spread. These methodological developments are expected to substantially enhance the chemical robustness, diagnostic reliability, detection sensitivity, and biomarker selectivity for current and future biosensing devices.

Implementing Silicon Nanoribbon Field-Effect Transistors as Arrays for Multiple Ion Detection

Ionic gradients play a crucial role in the physiology of the human body, ranging from metabolism in cells to muscle contractions or brain activities. To monitor these ions, inexpensive, label-free chemical sensing devices are needed. Field-effect transistors (FETs) based on silicon (Si) nanowires or nanoribbons (NRs) have a great potential as future biochemical sensors as they allow for the integration in microscopic devices at low production costs. Integrating NRs in dense arrays on a single chip expands the field of applications to implantable electrodes or multifunctional chemical sensing platforms. Ideally, such a platform is capable of detecting numerous species in a complex analyte. Here, we demonstrate the basis for simultaneous sodium and fluoride ion detection with a single sensor chip consisting of arrays of gold-coated SiNR FETs. A microfluidic system with individual channels allows modifying the NR surfaces with self-assembled monolayers of two types of ion receptors sensitive to sodium and fluoride ions. The functionalization procedure results in a differential setup having active fluoride- and sodium-sensitive NRs together with bare gold control NRs on the same chip. Comparing functionalized NRs with control NRs allows the compensation of non-specific contributions from changes in the background electrolyte concentration and reveals the response to the targeted species.

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.