A simple and versatile method for statistical analysis of the electrical properties of individual double walled carbon nanotubes

Double-walled carbon nanotubes (DWNTs) are potential candidates for new generation of on chip interconnections due to their nearly metallic behaviour. For such large scale integration purpose it is mandatory to characterize their electrical properties in a statistical way. We thus propose a new methodology for characterizing in one step, the electrical properties of a large population of nanotubes. The method enables to obtain histograms of the conductance and maximum current density of individual nanoobjects

Daniel Seichepine, Assistant Professor, Neuropsychology (UNHM) travel to Brazil

Professor Seichepine lectured on the neuropsychology of neurodegenerative disorders and dementia, such as Alzheimer’s disease and chronic traumatic encephalopathy, at the Universidade Estadual do Centro-Oeste (Midwestern State University)

A simple and versatile micro contact printing method for generating carbon nanotubes patterns on various substrates

We present an optimized process for generating at low cost, patterns of carbon nanotubes (CNTs) on a large variety of substrates through a simple micro contact printing method. This method meets the requirements for the integration of CNTs into microdevices, for applications in microelectronics (interconnects), flexible electronics (printed conductive electrodes) and biodevices (biosensors and biosystems for regenerative medicine). We have optimized a new method for inking PolyDiMethylSiloxane (PDMS) stamps with CNTs that turned out to improve significantly the quality of the printed features over large surfaces. This inking step is performed by adapting a spray-coating process leading to a dense and homogeneous coating of the stamp with a thin layer of CNTs. The printing step is performed using a solvent mediation, allowing us to pattern this thin layer of CNTs onto various substrates by contact through a thin film of liquid. We demonstrate that this soft and rapid methodology can lead to the realization of CNTs patterns with versatile geometries onto various substrates at the micron scale. Examples of applications for CNTs interconnects and flexible electronics are rapidly shown

Multi-scale engineering for neuronal cell growth and differentiation

In this paper we investigate the role of micropatterning and molecular coating for cell culture and differentiation of neuronal cells (Neuro2a cell line) on a polydimethylsiloxane substrate. We investigate arrays of micrometric grooves (line and space) capable to guide neurite along their axis. We demonstrate that pattern dimensions play a major role due to the deformation of the cell occasioned by grooves narrower than typical cell dimension. A technological compromise for optimizing cell density, differentiation rate and neurite alignment has been obtained for 20 lm wide grooves which is a dimension comparable with the average cell dimension. This topographical engineered pattern combined with double-wall carbon nanotubes coating enabled us to obtain adherent cell densities in the order of 104 cells/cm2 and a differentiation rate close to 100%

A combination of capillary and dielectrophoresis-driven assembly methods for wafer scale integration of carbon-nanotube-based nanocarpets

The wafer scale integration of carbon nanotubes (CNT) remains a challenge for electronic and electromechanical applications. We propose a novel CNT integration process relying on the combination of controlled capillary assembly and buried electrode dielectrophoresis (DEP). This process enables us to monitor the precise spatial localization of a high density of CNTs and their alignment in a pre-defined direction. Large arrays of independent and low resistivity (4.4 x 10-5 omega m) interconnections were achieved using this hybrid assembly with double-walled carbon nanotubes (DWNT). Finally, arrays of suspended individual CNT carpets are realized and we demonstrate their potential use as functional devices by monitoring their resonance frequencies (ranging between 1.7 and 10.5 MHz) using a Fabry–Perot interferometer

Réalisation d'interconnexions de faible résistivité à base de nanotubes de carbone biparois pour la microélectronique

Les nanotubes de carbone (NTC) possèdent des propriétés électriques pouvant répondre aux futures demandes de la microélectronique. Toutefois, des méthodes d'intégration de ces nano-objets dans des systèmes complexes doivent être développées. Le but de ces travaux de thèse était le développement d'un procédé permettant de déposer sélectivement des NTC doubles parois de manière orientée, et ce, à l'échelle d'un wafer de silicium. Un certain nombre de méthodes ont été développées. L'utilisation d'une pulvérisation de suspension de NTC couplée à différentes méthodes de microstructuration a permis de réaliser, à de grandes échelles, des dépôts de tapis de NTC microstructurés à des résolutions de l'ordre du micron. Bien que ne répondant pas à tous les critères requis pour la microélectronique ces techniques de dépôt ont pu trouver une application dans le domaine de l'ingénierie tissulaire. Ces travaux ont donné lieu à un dépôt de brevet. Afin d'améliorer les méthodes de synthèse de nos échantillons de NTC conducteurs, une technique de caractérisation grande échelle des caractéristiques électriques de NTC a été mise en œuvre. En effet, l'impossibilité d'obtenir une information statistique sur les propriétés des NTC présents dans un échantillon entravait les possibilités d'optimisation. La technique développée se base sur l'étude de la réponse d'un ensemble de NTC soumis à une forte rampe de tension. La destruction successive des NTC permet de mesurer les propriétés de nano-objets individuels et ainsi de rapidement tirer des données statistiques. Finalement, une technique originale basée sur la manipulation de NTC par des champs électriques et des forces capillaires a été développée. Le contrôle des forces capillaires permet de concentrer des NTC dans une cavité où ces derniers seront piégés et alignés par un champ électrique. Cette technique a permis non seulement d'obtenir des connexions en NTC denses et très conductrices mais également de réaliser divers dispositifs fonctionnels tels que des nano-résonateurs ou encore des capteurs.Carbon nanotubes (CNT) are known for their current carrier capability which could be used for microelectronics applications. However, the challenge is to find integration ways to use these CNT at the large scale. This PhD work focus on the development of new ways to manipulate double walled carbon nanotubes at the wafer scale. Several techniques are developed in this work. The use of spray coating with several patterning techniques allows the large scale deposition of microstructured CNT carpet. This work has been patented mainly for cellular engineering purpose. A technique has been developed to characterize the electrical properties of a large number of CNT. The random integration of CNT on an array of electrodes is used to test a low number of parallel CNT. The successive breakdown of these connected CNT is used to study their electrical properties, such as resistivity and maximum current density. Finally, we have developed a technique based on buried electrodes dielectrophoresis and capillary assembly to create large arrays of oriented carbon nanotubes carpet. This technique has been optimized to obtain high density deposition and thus reduce the resistivity. This very versatile technique has been used to create arrays of sensors and nano resonators

A comparison of front-end amplifiers for tetrapolar bioimpedance measurements

Many commercial benchtop impedance analyzers are incapable of acquiring accurate tetrapolar measurements, when large electrode contact impedances are present, as in bioimpedance measurements using electrodes with micrometer-sized features. External front-end amplifiers can help overcome this issue and provide high common-mode rejection ratio (CMRR) and input impedance. Several discrete component-based topologies are proposed in the literature. In this article, these are compared with new alternatives with regard to their performance in measuring known loads in the presence of electrode contact impedance models, to emulate tetrapolar bioimpedance measurements. These models are derived from bipolar impedance measurements taken from the electrodes of a tetrapolar bioimpedance sensor. Comparison with other electrode models used in the literature established that this is a good and challenging model for bioimpedance front-end amplifier evaluation. Among the examined amplifiers, one of the best performances is achieved with one of the proposed topologies based on a custom front-end with no external resistors (AD8066/AD8130). Under the specific testing conditions, it achieved an uncalibrated worst-case absolute measurement deviation of 4.4% magnitude and 4° at 20 Hz, and 2.2% and 7° at 1 MHz accordingly with loads between 10 Ω and 10 kg. Finally, the practical use of the front-end with the impedance analyzer is demonstrated in the characterization of the bioimpedance sensor, in saline solutions of varying conductivities (2.5-20 mS/cm) to obtain its cell constant. This article serves as a guide for evaluating and choosing front-end amplifiers for tetrapolar bioimpedance measurements both with and without impedance analyzers for practical/clinical applications and material/sensor characterization

Relation of Parkinson\u27s Disease Subtypes to Visual Activities of Daily Living

Visual perceptual problems are common in Parkinson\u27s disease (PD) and often affect activities of daily living (ADLs). PD patients with non-tremor symptoms at disease onset (i.e., rigidity, bradykinesia, gait disturbance or postural instability) have more diffuse neurobiological abnormalities and report worse non-motor symptoms and functional changes than patients whose initial symptom is tremor, but the relation of motor symptom subtype to perceptual deficits remains unstudied. We assessed visual ADLs with the Visual Activities Questionnaire in 25 non-demented patients with PD, 13 with tremor as the initial symptom and 12 with an initial symptom other than tremor, as well as in 23 healthy control participants (NC). As expected, the non-tremor patients, but not the tremor patients, reported more impairment in visual ADLs than the NC group, including in light/dark adaptation, acuity/spatial vision, depth perception, peripheral vision and visual processing speed. Non-tremor patients were significantly worse than tremor patients overall and on light/dark adaptation and depth perception. Environmental enhancements especially targeted to patients with the non-tremor PD subtype may help to ameliorate their functional disability

Micro-object pose estimation with sim-to-real transfer learning using small dataset

International audience<span style="color: rgb(34, 34, 34); font-family: -apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Oxygen-Sans, Ubuntu, Cantarell, &quot;Helvetica Neue&quot;, sans-serif; font-size: 18px;"&gtThree-dimensional (3D) pose estimation of micro/nano-objects isessential for the implementation of automatic manipulation inmicro/nano-robotic systems. However, out-of-plane pose estimationof a micro/nano-object is challenging, since the images aretypically obtained in 2D using a scanning electron microscope (SEM)or an optical microscope (OM). Traditional deep learning basedmethods require the collection of a large amount of labeled datafor model training to estimate the 3D pose of an object from amonocular image. Here we present a sim-to-real learning-to-matchapproach for 3D pose estimation of micro/nano-objects. Instead ofcollecting large training datasets, simulated data is generated toenlarge the limited experimental data obtained in practice, whilethe domain gap between the generated and experimental data isminimized via image translation based on a generative adversarialnetwork (GAN) model. A learning-to-match approach is used to mapthe generated data and the experimental data to a low-dimensionalspace with the same data distribution for different pose labels,which ensures effective feature embedding. Combining the labeleddata obtained from experiments and simulations, a new trainingdataset is constructed for robust pose estimation. The proposedmethod is validated with images from both SEM and OM, facilitatingthe development of closed-loop control of micro/nano-objects withcomplex shapes in micro/nano-robotic systems.</span&g