a Field-Effect Transistor sensors (FET-sensors) have been receiving increasing attention for biomolecular sensing over the last two decades due to their potential for ultra-high sensitivity sensing, label-free operation, cost reduction and miniaturisation. Whilst the commercial application of FET-sensors in pH sensing has been realised, their commercial application in biomolecular sensing (termed BioFETs) is hindered by poor understanding of how to optimise device design for highly reproducible operation and high sensitivity. In part, these problems stem from the highly interdisciplinary nature of the problems encountered in this field, in which knowledge of biomolecular-binding kinetics, surface chemistry, electrical double layer physics and electrical engineering is required. In this work, a quantitative analysis and critical review has been performed comparing literature FET-sensor data for pH-sensing with data for sensing of biomolecular streptavidin binding to surface-bound biotin systems. The aim is to provide the first systematic, quantitative comparison of BioFET results for a single biomolecular analyte, specifically streptavidin, which is the most commonly used model protein in biosensing experiments, and often used as an initial proofof-concept for new biosensor designs. This novel quantitative and comparative analysis of the surface potential behaviour of a range of devices demonstrated a strong contrast between the trends observed in pH-sensing and those in biomolecule-sensing. Potential explanations are discussed in detail and surfacechemistry optimisation is shown to be a vital component in sensitivity-enhancement. Factors which can influence the response, yet which have not always been fully appreciated, are explored and practical suggestions are provided on how to improve experimental design.

The protonation or deprotonation of isolated silanols in the presence of H3O(+) or OH(-), respectively, was shown to be a highly rapid, exothermic reaction with no significant activation energy. This process occurred via a concerted motion of the protons through 'water wires'. Geometry optimisations of large water clusters at the silica surface demonstrated proton transfer to the surface occurring via the rarely discussed 'proton holes' mechanism. This indicates that surface protonation is possible even when the hydronium ion is distant (at least 4 water molecules separation) from the surface.

Electronic devices are becoming increasingly used in chemical- and bio-sensing applications and therefore understanding the silica-electrolyte interface at the atomic scale is becoming increasingly important. For example, field-effect biosensors (BioFETs) operate by measuring perturbations in the electric field produced by the electrical double layer due to biomolecules binding on the surface. In this paper, explicit-solvent atomistic calculations of this electric field are presented and the structure and dynamics of the interface are investigated in different ionic strengths using molecular dynamics simulations. Novel results from simulation of the addition of DNA molecules and divalent ions are also presented, the latter of particular importance in both physiological solutions and biosensing experiments. The simulations demonstrated evidence of charge inversion, which is known to occur experimentally for divalent electrolyte systems. A strong interaction between ions and DNA phosphate groups was demonstrated in mixed electrolyte solutions, which are relevant to experimental observations of device sensitivity in the literature. The bound DNA resulted in local changes to the electric field at the surface; however, the spatial- and temporal-mean electric field showed no significant change. This result is explained by strong screening resulting from a combination of strongly polarised water and a compact layer of counterions around the DNA and silica surface. This work suggests that the saturation of the Stern layer is an important factor in determining BioFET response to increased salt concentration and provides novel insight into the interplay between ions and the EDL.

Continuum-based methods are important in calculating electrostatic properties of interfacial systems such as the electric field and surface potential but are incapable of providing sufficient insight into a range of fundamentally and technologically important phenomena which occur at atomistic length-scales. In this work a molecular dynamics methodology is presented for interfacial electric field and potential calculations. The silica-water interface was chosen as an example system, which is highly relevant for understanding the response of field-effect transistors sensors (FET sensors). Detailed validation work is presented, followed by the simulated surface charge/surface potential relationship. This showed good agreement with experiment at low surface charge density but at high surface charge density the results highlighted challenges presented by an atomistic definition of the surface potential. This methodology will be used to investigate the effect of surface morphology and biomolecule addition; both factors which are challenging using conventional continuum models.

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.

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