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Beatrice Cipriani

Publications and source records attributed to Beatrice Cipriani.

2 recordsLinked to original sources

Graphene Electric Double-Layer Transistors for Enhanced-Sensitivity Label-Free Detection of Human Serum Albumin

Accurate detection of human serum albumin (HSA) is essential for the early diagnosis and monitoring of renal and hepatic disorders. We present a graphene-based electrolyte-gated field-effect transistor (EGFET) for label-free, real-time quantification of HSA under non-Faradaic operation. Devices exploit the high interfacial capacitance of the electric double layer (EDL) to transduce electrostatic perturbations induced by albumin adsorption into measurable conductance modulation. Negatively charged HSA molecules induce systematic modulation of the graphene channel, producing a concentration-dependent displacement of the Dirac voltage consistent with p-type doping. To establish a molecular-level interpretation of the sensing response, Brownian Dynamics simulations show that HSA adsorbs onto graphene through multiple adsorption orientations associated with heterogeneous interfacial charge distributions and variable dipole alignments relative to the surface. Adsorption is energetically stabilized by van der Waals interactions. Analysis of transfer characteristics across concentrations ranging from 0.01 to 30mgmL-1 reveals a correlation between surface charge density and carrier transport modulation within the electric double layer. Optimized devices exhibit a limit of detection of 0.0087 mg mL-1 and a linear dynamic range extending to 10 mg mL-1. The response remains non-Faradaic under sub-volt operation with reversible and reproducible behavior. The use of an inverse-mobility analytical metric highlights the role of disorder-enhanced carrier scattering in signal amplification, enabling sensitive electrostatic detection while preserving reversible device operation. These results establish liquid-gated graphene EGFETs as a promising platform for quantitative protein sensing and provide insight into disorder-mediated transport mechanisms in graphene bioelectronic devices.

cond-mat.mes-hall

Impact of protein corona morphology on nanoparticle diffusion in biological fluids: insights from a mesoscale approach

Nanoparticles (NPs) demonstrate considerable potential in medical applications, including targeted drug delivery and diagnostic probes. However, their efficacy depends on their ability to navigate through the complex biological environments inside living organisms. In such environments, NPs interact with a dense mixture of biomolecules, which can reduce their mobility and hinder diffusion. Understanding the factors influencing NP diffusion in these environments is key to improving nanomedicine design and predicting toxicological effects. In this study, we propose a computational approach to model NP diffusion in crowded environments. We introduce a mesoscale model that accounts for the combined effects of the Protein Corona (PC) and the crowded medium on NP movement. By including volume-exclusion interactions and modelling the PC both explicitly and implicitly, we identify key macromolecular descriptors that affect NP diffusion. Our results show that the morphology of the PC can significantly affect the diffusion of NPs, and the role of the occupied volume fraction and the size ratio between tracers and crowders are analysed. The results also show that approximating large macromolecular assemblies with a hydrodynamic single-sphere model leads to inexact diffusion estimates. To overcome the limitations of single-sphere representations, a strategy for an accurate parametrization of NP-PC systems using a single-sphere model is presented.

cond-mat.soft