Tin dioxide sol-gel derived thin films deposited on porous silicon

Undoped and Sb-doped SnO2 sol¿gel derived thin films have been prepared for the first time from tin (IV) ethoxide precursor and SbCl3 in order to be utilised for gas sensing applications where porous silicon is used as a substrate. Transparent, crack-free and adherent layers were obtained on different types of substrates (Si, SiO2/Si). The evolution of the Sn¿O chemical bonds in the SnO2 during film consolidation treatments was monitored by infrared spectroscopy. By energy dispersive X-ray spectroscopy performed on the cross section of the porosified silicon coupled with transmission electron microscopy, the penetration of the SnO2 sol¿gel derived films in the nanometric pores of the porous silicon has been experimentally proved

Dielectric constants and phonon modes of amorphous hafnium aluminate deposited by metal organic chemical vapor deposition

Dielectric constants and long-wavelength optical phonon modes of amorphous hafnium aluminate films with a maximum aluminum content of 19 at. % are studied by infrared spectroscopic ellipsometry (IRSE). The hafnium aluminate films were prepared by metal organic chemical vapor deposition on silicon substrates. IRSE revealed one polar lattice mode and one impurity-type mode, which show all a systematic shift in frequency with varying Al content. The static dielectric constant decreases from 10.1 for 4.6 at. % Al to 8.1 for 19 at. % Al. The absolute values were found to be between 50% and 70% smaller than the values obtained from electrical measurements

CMOS-compatible SOI micro-hotplate-based oxygen sensor

© 2016 IEEE. The paper reports upon the design and characterization of a resistive O2 sensor, which is fully CMOS-compatible and is based on an ultra-low-power Silicon on Insulator (SOI) micro-hotplate membrane. The microsensor employs SrTi0.4Fe0.6O2.8 (STFO60) as sensing layer. Thermo-Gravimetric Analysis (TGA) Energy-Dispersive X-ray Spectroscopy (EDX), X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM) techniques have been used to assess the quality of both the sensing layer and STFO-SOI interface. At room temperature, the SOI sensor shows good sensitivity and fast response time (≤ 6 seconds) to O2 concentration ranging from 0% to 20% in a nitrogen atmosphere. This is the first experimental result showing the potential of this structure as O2 sensor

Nanostructured metal oxides semiconductors for oxygen chemiresistive sensing

Nanostructured metal oxide semiconductors have been widely investigated and are commonly used in gas sensing structures. After a brief review which will be focused on chemiresistive oxygen sensing employing this type of sensing materials, for both room temperature and harsh environment applications (particularly, at high ambient temperature and high relative humidity levels), paper reports new results concerning O2detection of a structure using a sensing layer comprising nanostructured (typical grain size of 50 nm) SrTi0.6Fe0.4O2.8(STFO40), synthesized by sonochemical methods, mixed with single wall carbon nanotubes. The structure is a Microelectromechanical System (MEMS), based on a Silicon-on-Insulator (SOI), Complementary Metal-Oxide-Semiconductor (CMOS)-compatible micro-hotplate, comprising a tungsten heater which allows an excellent control of the sensing layer working temperature. Oxygen detection tests were performed in both dry (RH = 0%) and humid (RH = 60%) nitrogen atmosphere, varying oxygen concentrations between 1% and 20% (v/v), at a constant heater temperature of 650 °C

A Kernel-Based Membrane Clustering Algorithm

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms

Robotic swarm deployment task implemented using P colonies and Finite state machines

In this record we present two distinct robot control models designed to control a swarm of robots (in a distributed manner) with the objective of moving the robots in a random direction as long as they have neighbors nearby. Once there are no more neighbor robots nearby, each robot is programmed to stop and color itself in white. The first control model uses P colonies and is described in detail in Florea, A. G., & Buiu, C. (2016). Development of a software simulator for P colonies. Applications in robotics. International Journal of Unconventional Computing, 12(2-3), 189–205. The second control model uses the concept of State-less Event-driven Finite State Machine and the structure of this model is presented in diagram.png. In the following sections, we present a small description for each of the attached videos. 1_clone_10_circle This video demonstrates the use of the robot cloning function of the vrep_bridge script in order to create 9 distinct copies of the source robot and distribute them on a circle around the source robot. The copies are so positioned by a distribution function that can be adapted to other forms. This cloning function allows one to generate large swarms of robots with ease. 2_one_pcolony_for_three_kilobots In this video, we simulate a simple P minus colony using Lulu_Kilobot, on three different robots. At each subtraction, the robots move one step forward. At the beginning of the clip one can see the robot - P colony association table, where each robot has a distinct copy of the original P colony. 3_pswarm_5_robots_3_colonies This video demonstrates the flexibility offered by the config file of Lulu_Kilobot. From the config file we explicitly specify that the first two robots should use the go straight P colony. For the other colonies, we specify the number of robots that should be assigned, go left = 1 and go right = 2. From the robot - P colony association table, one can see that the first robot that is assigned a P colony uses the original P colony while the others use an independent copy of the P colony. 4_pcolony_10_robots_disperse This video demonstrates dispersion, which is a typical deployment scenario in swarm robotics. The robots should position themselves away from one another, so that each robot is at least at a minimum distance from each of its neighbours. All decisions are taken by the command module, on the basis of the received input data from msg_distance. A new direction of motion (and corresponding color) is randomly chosen if there are other robots closer than a pre-set threshold distance and otherwise the robots stop and set their color to white. 5_fsm_10_robots_disperse In this video we present the same dispersion algorithm as presented in the previous video but implemented using the Event-driven Finite State Machine control model