BioSensorLab is a tool to evaluate and predict the performance parameters of a label-free, electronic biosensor (see figure). The sensor basically consist of a field effect device, whose surface is functionalized with capture probe (receptor) molecules. Some of the target molecules, which are introduced to the system, diffuse through the solution and reach the field effect device and get captured by the receptors thereby binding them close to the surface. Many bio-molecules carry an electrostatic charge under normal physiological conditions. For example, DNA is negatively charged while the net charge of a protein molecule depends on the pH of the solution. The coulomb interaction between the charge of the target bio-molecule and the field effect device can result in a change in conductivity of the latter.

The response of a sensor is characterized in terms of its Settling time, Sensitivity and Selectivity. The time taken by the sensor to produce a stable signal change defines the settling time. It is determined by bio-molecule concentration, their diffusion coefficients, and their conjugation affinity to the receptor molecules. Sensitivity corresponds to the relative change in sensor characteristics upon attachment of target molecules on nanowire surface. This is determined mainly by the electrostatics of the system. Finally, Selectivity denotes the ability of receptors to bind with the desired target in the presence of various other (possibly similar) biomolecules and is entirely determined by the functionalizing schemes. For example, to detect DNA, Peptide Nucleic Acid (PNA) receptors are shown to be more selective than their DNA counterparts.

The performance parameters of nanobiosensors (Settling time, Sensitivity and Selectivity) can be estimated using this tool. The theoretical model is based on self-consistent solutions of Diffusion-Capture model (for the time response), Poisson-Boltzmann and Drift-Diffusion Equations (for electrolyte screening and conductance modulation) and the statistical properties of bio-molecule adsorption (Selectivity). However, ONLY the SETTLING TIME module is released for public domain right now and the rest will be made available shortly.

Prof. Alam's lecture on Geometry of Diffusion and the Performance Limits of Nanobiosensors provides an overview on the Diffusion-Capture model and its implications on sensor performance.

National Institute of Health (NIH).
Network for Computational Nanotechnology (NCN).

Settling Time:
P. R. Nair and M. A. Alam, "Dimensionally Frustrated Diffusion towards Fractal Adsorbers," Physical Review Letters, 99, 256101 (2007).
P. R. Nair and M. A. Alam, "Performance Limits of Nanobiosensors," Applied Physics Letters, 88, 233120 (2006).

Sensitivity:
P. R. Nair and M. A. Alam, "Screening-Limited Response of Nanobiosensors," Nano Letters, (2008).
P. R. Nair and M. A. Alam, "Design Considerations of Silicon Nanowire Biosensors," IEEE Transactions on Electron Devices, 54, 3400 (2007).

Researchers should cite this work as follows:

• P. R. Nair and M. A. Alam, Physical Review Letters, 99, 256101 (2007).
  P. R. Nair and M. A. Alam, Applied Physics Letters, 88, 233120 (2006).

• Pradeep Ramachandran Nair, Muhammad A. Alam (2014), "BioSensorLab," https://nanohub.org/resources/senstran. (DOI: 10.4231/D3319S264).

  BibTex | EndNote

1. biosensors/sensing
2. nano/bio
3. NanoBio Node at Illinois
4. NCN Group - Biotechnology and Medicine
5. sensors/sensing