Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.     

Query-based learning for aerospace applications

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.     

Synthesis and characterization of first‐ and second‐generation polyamide pyridylimine nickel dihalide metallodendrimers and their uses as catalysts for ethylene polymerization

First‐ and second‐generation pyridylimine‐terminated dendrimeric ligands were prepared by the reaction of the corresponding amine‐terminated aromatic polyamide dendrimers with 2‐acetylpyridine. The pyridylimine terminal groups were used as bidentate N,N ligands of nickel halide to prepare the corresponding first‐generation and second‐generation nickel dihalide metallodendrimers C1 and C2 , respectively. The synthesized dendrimers and metallodendrimers were characterized by elemental and spectral analyses. C1 and C2 were evaluated as catalyst precursors for ethylene oligomerization after being activated with methylaluminoxane (MAO) and diethylaluminum chloride (Et₂AlCl) under 1 atm and 5 atm pressure of ethylene. In both cases, the use of 1 atm or 5 atm pressure of ethylene and a 1500:1 Al:Ni molar ratio for C1 and C2 resulted in high catalytic activities toward ethylene polymerization. Upon activation with MAO and Et₂AlCl, C1 exhibited promising activities toward ethylene polymerization and produced linear chain structures that were associated with high density polyethylene. In contrast, C2 produced a polymer with the branching nature of low density polyethylene under similar conditions. © 2014 Society of Chemical Industry     

Synthesis and dielectric relaxation behavior of 55B2O3–15SiO2– 30Na2O: WO3 glass system

55 B₂O₃ – 15 SiO₂ – 30 Na₂O: x WO₃, (x ranging from 0 to 5 wt%) glass composites were prepared according to the melt-quenching procedure. The prepared samples were characterized via X-ray diffraction and broad band dielectric spectroscopy (viz., dielectric constant (ε₁), tanδ and AC conductivity (σ_AC) over a wide range of frequency and temperature). No sharp peaks were shown in the XRD pattern and is evidence of the amorphous nature of the samples. It turns out that the values of ε₁ and ε₂ are increasing at higher temperatures particularly at lower frequencies. The energy barrier height, W_M values decreased as 0.27, 0.25 and 0.22 while showed value of 0.29 eV for 5.0 wt% WO₃. In the dielectric modulus plots, two relaxation processes are found especially on the higher temperature side. It also shifted a higher frequency with increasing temperature. The values of exponent s have been found to lie between 0.48 and 0.74, which confirms that the conduction mechanism in the glass samples follows the correlated barrier hopping model. By calculating the activation energy associated with the relaxation processes and DC conductivity, it was found that the values are close, which indicates that the same charges contribute to the two processes.     

Formal probabilistic analysis of a surgical robot control algorithm with different virtual fixtures

Surgical robots are increasingly being used in operation theaters involving normal or laparoscopic surgeries. The working of these surgical robots is highly dependent on their control algorithms, which require very rigorous analysis to ensure their correct functionality due to the safety-critical nature of surgeries. Traditionally, safety of control algorithms is ensured by simulations, but they provide incomplete and approximate analysis results due to their inherent sampling-based nature. We propose to use probabilistic model checking, which is a formal verification method, for quantitative analysis, to verify the control algorithms of surgical robots in this paper. As an illustrative example, the paper provides a formal analysis of a virtual fixture control algorithm, implemented in a neuro-surgical robot, using the PRISM model checker. In particular, we provide a formal discrete-time Markov chain-based model of the given control algorithm and its environment. This formal model is then analyzed for multiple virtual fixtures, like cubic, hexagonal and irregular shapes. This verification allowed us to discover new insights about the considered algorithm that allow us to design safer control algorithms.     

Significant G peak temperature shift in Raman spectra of graphene on copper

Linear G peak position temperature dependence in Raman spectra of graphene synthesized by chemical vapor deposition from decane on copper (220) with a shift factor of ?(5.4 ± 0.4) × 10^?2 cm^?1K^?1 was observed. The obtained value substantially exceeds values previously obtained for graphene by other authors, and was associated with the substrate interaction effects.     

Synthesis and characterization of monomeric and polymeric pyridinylimine‐based Ni(II) complexes and their catalytic activities in ethylene oligomerization

A new polymeric ligand was synthesized through the reaction of 4‐(pyridinylimine)phenol and formaldehyde in a basic medium, and its corresponding polymer–nickel complexes were formed in a 1:1 molar ratio. The synthesized compounds were characterized using elemental and spectral analyses. The monomeric and polymeric Ni(II) complexes (C₁ and C₂, respectively) were evaluated as catalyst precursors for ethylene oligomerization, using methylaluminoxane as an activator at two different ethylene pressures. C₂ was found to be a more effective pre‐catalyst than C₁, with the co‐catalyst having a similar effect in both cases. C₂ exhibited an activity of 1.282 × 10⁶ g (mol Ni)^?1 h^?1 bar^?1, with an Al:Ni ratio of 2000:1 at room temperature and 1 atm ethylene pressure. Meanwhile C₁ exhibited an activity of 1.126 × 10⁶ g (mol Ni)^?1 h^?1 bar^?1 under similar experimental conditions. At 5 atm ethylene pressure, C₁ favoured the formation of high‐density polyethylene, whereas C₂ favoured the formation of branched low‐density polyethylene. Copyright © 2012 Society of Chemical Industry