This paper presents the optimization of surface modification for aptameric graphene nanosensors for the measurement of biomarkers in undiluted physiological media. In these sensors, graphene transduces the binding between an aptamer and the intended target biomarker into a measurable signal while being coated with a polyethylene glycol (PEG) nanolayer to minimize nonspecific adsorption of matrix molecules. We perform a systematic study of the aptamer and PEG attachment schemes and parameters, including the impact of the serial or parallel PEG–aptamer attachment scheme, PEG molecular weight and surface density, and aptamer surface density on the sensor behavior, such as the responsivity to biomarker concentration changes, and importantly, they are used for operation in physiological media and have the ability to reject nonspecific binding to interfering molecules. We then use the understanding from this parametric study to identify graphene nanosensor designs that are optimally functionalized with PEG and aptamers to be strongly responsive to target biomarkers and effectively reduce nonspecific adsorption of interferents, thereby enabling sensitive and specific biomarker measurements in undiluted physiological media. The experimental results show that nanosensors that were optimized via serial modification with 5000 Da PEG at 15 mM and a 94 nt DNA aptamer at 500 nM allowed specific measurement of C-reactive protein (CRP) in undiluted human serum with a limit of detection (LOD) down to 27 pM, representing an up to 1000-fold improvement compared to previously reported CRP measurements.

This paper presents a systematic modeling and design methodology for microfluidic concentration gradient generators. The generator is decomposed into a system of microfluidic elements with relatively simple geometries. Parameterized models for such elements are analytically developed and hold for general sample concentration profiles and arbitrary flow ratios at the element inlet, hence they are valid for concentration gradient generators that rely on both complete and partial mixing. The element models are then linked through an appropriate set of parameters embedded at the element interfaces to construct a lumped-parameter and systematic representation of the entire generator network. The system model is verified by numerical analysis and experimental data and accurately captures the overall effects of network topologies, element sizes, flow rates and reservoir sample concentrations on the generation of sample concentration gradient. Finally, this modeling methodology is applied to propose a novel and compact microfluidic device that is able to create concentration gradients of complex shapes by juxtaposing simple constituent profiles along the channel width