Ultrasensitive Displacement Noise Measurement of Carbon Nanotube Mechanical Resonators

Mechanical resonators based on a single carbon nanotube are exceptional sensors of mass and force. The force sensitivity in these ultra-light resonators is often limited by the noise in the detection of the vibrations. Here, we report on an ultra-sensitive scheme based on a RLC resonator and a low-temperature amplifier to detect nanotube vibrations. We also show a new fabrication process of electromechanical nanotube resonators to reduce the separation between the suspended nanotube and the gate electrode down to $\sim 150$~nm. These advances in detection and fabrication allow us to reach $0.5~\mathrm{pm}/\sqrt{\mathrm{Hz}}$ displacement sensitivity. Thermal vibrations cooled cryogenically at 300~mK are detected with a signal-to-noise ratio as high as 17~dB. We demonstrate $4.3~\mathrm{zN}/\sqrt{\mathrm{Hz}}$ force sensitivity, which is the best force sensitivity achieved thus far with a mechanical resonator. Our work is an important step towards imaging individual nuclear spins and studying the coupling between mechanical vibrations and electrons in different quantum electron transport regimes.Comment: 9 pages, 5 figure

A fast cardiac electromechanics model coupling the Eikonal and the nonlinear mechanics equations

We present a new model of human cardiac electromechanics for the left ventricle where electrophysiology is described by a Reaction-Eikonal model and which enables an off-line resolution of the reaction model, thus entailing a big saving of computational time. Subcellular dynamics is coupled with a model of tissue mechanics, which is in turn coupled with a Windkessel model for blood circulation. Our numerical results show that the proposed model is able to provide a physiological response to changes in certain variables (end-diastolic volume, total peripheral resistance, contractility). We also show that our model is able to reproduce with high accuracy and with a considerably lower computational time the results that we would obtain if the monodomain model should be used in place of the Eikonal model

Investigation on the composition of agarose–collagen i blended hydrogels as matrices for the growth of spheroids from breast cancer cell lines

Three-dimensional (3D) cell culture systems mimic the structural complexity of the tissue microenvironment and are gaining increasing importance as they resemble the extracellular matrix (ECM)–cell and cell–cell physical interactions occurring in vivo. Several scaffold-based culture systems have been already proposed as valuable tools for large-scale production of spheroids, but they often suffer of poor reproducibility or high costs of production. In this work, we present a reliable 3D culture system based on collagen I-blended agarose hydrogels and show how the variation in the agarose percentage affects the physical and mechanical properties of the resulting hydrogel. The influence of the different physical and mechanical properties of the blended hydrogels on the growth, size, morphology, and cell motility of the spheroids obtained by culturing three different breast cancer cell lines (MCF-7, MDA-MB-361, and MDA-MB-231) was also evaluated. As proof of concept, the cisplatin penetration and its cytotoxic effect on the tumor spheroids as function of the hydrogel stiffness were also investigated. Noteworthily, the possibility to recover the spheroids from the hydrogels for further processing and other biological studies has been considered. This feature, in addition to the ease of preparation, the lack of cross-linking chemistry and the high re-producibility, makes this hydrogel a reliable biomimetic matrix for the growth of 3D cell structures

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Topological field theories in n-dimensional spacetimes and Cartan's equations

Action principles of the BF type for diffeomorphism invariant topological field theories living in n-dimensional spacetime manifolds are presented. Their construction is inspired by Cuesta and Montesinos' recent paper where Cartan's first and second structure equations together with first and second Bianchi identities are treated as the equations of motion for a field theory. In opposition to that paper, the current approach involves also auxiliary fields and holds for arbitrary n-dimensional spacetimes. Dirac's canonical analysis for the actions is detailedly carried out in the generic case and it is shown that these action principles define topological field theories, as mentioned. The current formalism is a generic framework to construct geometric theories with local degrees of freedom by introducing additional constraints on the various fields involved that destroy the topological character of the original theory. The latter idea is implemented in two-dimensional spacetimes where gravity coupled to matter fields is constructed out, which has indeed local excitations.Comment: LaTeX file, no figure

Linear constraints from generally covariant systems with quadratic constraints

How to make compatible both boundary and gauge conditions for generally covariant theories using the gauge symmetry generated by first class constraints is studied. This approach employs finite gauge transformations in contrast with previous works which use infinitesimal ones. Two kinds of variational principles are taken into account; the first one features non-gauge-invariant actions whereas the second includes fully gauge-invariant actions. Furthermore, it is shown that it is possible to rewrite fully gauge-invariant actions featuring first class constraints quadratic in the momenta into first class constraints linear in the momenta (and homogeneous in some cases) due to the full gauge invariance of their actions. This shows that the gauge symmetry present in generally covariant theories having first class constraints quadratic in the momenta is not of a different kind with respect to the one of theories with first class constraints linear in the momenta if fully gauge-invariant actions are taken into account for the former theories. These ideas are implemented for the parametrized relativistic free particle, parametrized harmonic oscillator, and the SL(2,R) model.Comment: Latex file, revtex4, 18 pages, no figures. This version includes the corrections to many misprints of v1 and also the ones of the published version. The conceptual and technical parts of the paper are not altere