16 research outputs found
Characterization and modelling of differential sensitivity of nanoribbon-based pH-sensors
No full textWe report accurate characterization, modelling and simulation of SOI nanoribbon-based pH sensors and we compare operation in air (dry) and electrolyte (wet) environments. We find remarkably different current density distributions and geometry scaling rules, but similar series resistances and active trap state densities in the two configurations. Calibrated TCAD based simulations implementing an original approach to model the site-binding harge, and in good agreement with experiments, provide the necessary insights to interpret the non trivial dependence of the threshold voltage and current sensitivity on pH
Technological development of high-k dielectric FinFETs for liquid environment
No full textThis work presents the technological development and characterization of n-channel fully depleted high-k dielectric FinFETs (Fin Field Effect Transistor) for applications in a liquid environment. Herein, we provide a systematic approach based on Finite Element Analysis for a high-control fabrication process of vertical Si-fins on bulk and we provide many useful fabrication expedients. Metal gate FinFETs have been successfully electrically characterized, showing excellent subthreshold slope SS = 72 mV/dec and high Ion/Ioff
Graded Lie algebras of maximal class in characteristic p, generated by two elements of degree 1 and p
No full textLie algebras of maximal class (or filiform Lie algebras) are the Lie-theoretic analogue of pro-p-groups of maximal class. In particular, they are 2-generated. If one further assumes that the algebras are graded over the positive integers, then over a field of characteristic p it has been shown that a classification is possible provided one generator has
degree 1 and the other has either degree 1 or 2. In this thesis I give a classification of graded Lie algebras of maximal class with generators of degree 1 and p, respectively
Impact of different receptor binding modes on surface morphology and electrochemical properties of PNA-based sensing platforms.
No full textSilicon-based field-effect devices have been widely studied for label-free DNA detection in recent years. These devices rely on the detection of changes in the electrical surface potential during the DNA recognition event and thus require a reliable and selective immobilization of charged biomolecules on the device surface [1]. The preparation of self-assembled monolayers of phosphonic acids (SAMPs) on metal oxide surfaces is an efficient approach to generate well-defined organic interfaces with a high density of receptor binding sites close to the sensing surface [2,3]. In this work, we report the functionalization and characterization of silicon/silicon nitride surfaces with different types of peptide nucleic acid (PNA), a synthetic analogue to DNA [4]. Differently modified PNA molecules are covalently immobilized on the underlying SAMPs either in a multidentate or monodentate fashion to investigate the effect of different binding modes on receptor density and morphology important for PNA-DNA hybridization (Scheme 1). Multidentate immobilization of the bioreceptors via C6-SH attachment groups at the γ-points along the PNA backbone provides a rigid, lying configuration on the device surface (PNA 1), whereas a monodentate immobilization by Cys-capped PNA molecules (PNA 2) results in more flexible and more accessible receptor binding sites. Our results indicate that the presented functionalization scheme can be successfully applied to produce morphologically and electrochemically different PNA bioreceptor binding sites on silicon/silicon nitride surfaces. Consequently, a well-chosen modification of the PNA backbone is a valid approach to influence the sensing properties of surface-immobilized PNA bioreceptors, which might provide an additional parameter to further tune and tailor the sensing capabilities of PNA-based biosensing devices
Unified framework for a side-by-side comparison of different multicomponent algorithms: Lattice Boltzmann vs. phase field model
No full textLattice Boltzmann models (LBM) and phase field models (PFM) are two of the most widespread approaches for the numerical study of multicomponent fluid systems. Both methods have been successfully employed by several authors but, despite their popularity, still remains unclear how to properly compare them and how they perform on the same problem. Here we present a unified framework for the direct (one-to-one) comparison of the multicomponent LBM against the PFM. We provide analytical guidelines on how to compare the Shan–Chen (SC) lattice Boltzmann model for non-ideal multicomponent fluids with a corresponding free energy (FE) lattice Boltzmann model. Then, in order to properly compare the LBM vs. the PFM, we propose a new formulation for the free energy of the Cahn–Hilliard/Navier–Stokes equations. Finally, the LBM model is numerically compared with the corresponding phase field model solved by means of a pseudo-spectral algorithm. This work constitute a first attempt to set the basis for a quantitative comparison between different algorithms for multicomponent fluids. We limit our scope to the few of the most common variants of the two most widespread methodologies, namely the lattice Boltzmann model (SC and FE variants) and the phase field model
Bayesian estimation of physical and geometrical parameters for nanocapacitor array biosensors
No full textMassively parallel nanosensor arrays fabricated with low-cost CMOS technology represent powerful platforms for biosensing in the Internet-of-Things (IoT) and Internet-of-Health (IoH) era. They can efficiently acquire “big data” sets of dependable calibrated measure-ments, representing a solid basis for statistical analysis and parameter estimation.
In this paper we propose Bayesian estimation methods to extract physical parameters and interpret the statistical variability in the measured outputs of a dense nanocapacitor array biosensor. Firstly, the physical and mathematical models are presented. Then, a simple 1D-symmetry structure is used as a validation test case where the estimated parameters are also known a-priori. Finally, we apply the methodology to the simultaneous extraction of multiple physical and geometrical parameters from measurements on a CMOS pixelated nanocapacitor biosensor platform
Graded Lie algebras of maximal class of type
No full textThe algebras of the title are infinite-dimensional graded Lie algebras , over a field of positive characteristic ,
which are generated by an element of degree and an element of degree ,
and satisfy for . %of maximal class in the sense
that has dimension for all . In case such algebras were
classified by Caranti and Vaughan-Lee in 2003. We announce an extension of that
classification to arbitrary prime characteristic, and prove several major steps
in its proof.Comment: 40 page
Graded Lie algebras of maximal class of type p
No full textThe algebras of the title are infinite-dimensional graded Lie algebras , over a field of positive characteristic , which are generated by an element of degree and an element of degree , and satisfy for . In case such algebras were classified by Caranti and Vaughan-Lee in 2003. We announce an extension of that classification to arbitrary prime characteristic, and prove several major steps in its proof.</p
