Fine Texture Detection Based on a Solid-Liquid Composite Flexible Tactile Sensor Array.

Surface texture information plays an important role in the cognition and manipulation of an object. Vision and touch are the two main methods for extracting an object's surface texture information. However, vision is often limited since the viewing angle is uncertain during manipulation. In this article, we propose a fine surface texture detection method based on a stochastic resonance algorithm through a novel solid-liquid composite flexible tactile sensor array. A thin flexible layer and solid-liquid composite conduction structure on the sensor effectively reduce the attenuation of the contact force and enhance the sensitivity of the sensor. A series of ridge texture samples with different heights (0.9, 4, 10 µm), different widths (0.3, 0.5, 0.7, 1 mm), but the same spatial period (2 mm) of ridges were used in the experiment. The experimental results prove that the stochastic resonance algorithm can significantly improve the signal characteristic of the output signal of the sensor. The sensor has the capability to detect fine ridge texture information. The mean relative error of the estimation for the spatial period was 1.085%, and the ridge width and ridge height, respectively, have a monotonic mapping relationship with the corresponding model output parameters. The sensing capability to sense a fine texture of tactile senor surpasses the limit of human fingers.

Ambient electric arc ionization for versatile sample analysis using mass spectrometry.

A novel, convenient ambient electric arc ionization (AEAI) device was developed as a mass spectrometry ion source for versatile sample analysis. AEAI could be considered as a soft ionization technique in which the protonated ion ([M + H]+) is the main ion species with little or no in-source fragmentation for most analytes. Coupled with a high-resolution Orbitrap mass spectrometer, AEAI could be applied to the analysis of a variety of organic compounds having a wide range of polarities, ranging from non-polar species such as polybenzenoid aromatic hydrocarbons (PAHs) to highly polar species such as amino acids. With its versatile capabilities in the mass spectrometric analysis of small molecules, AEAI has the potential to be an alternative to traditional ionization methods such as electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and electron impact (EI) ionization. The limitations of AEAI are also discussed.

Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array.

Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect.