PEI-Functionalized Carbon Nanotube Thin Film Sensor for CO2 Gas Detection at Room Temperature

In this study, a polyethyleneimine (PEI)-functionalized carbon nanotube (CNT) sensor was fabricated for carbon dioxide detection at room temperature. Uniform CNT thin films prepared using a filtration method were used as resistive networks. PEI, which contains amino groups, can effectively react with CO2 gas by forming carbamates at room temperatures. The morphology of the sensor was observed, and the properties were analyzed by scanning electron microscope (SEM), Raman spectroscopy, and fourier transform infrared (FT-IR) spectroscopy. When exposed to CO2 gas, the fabricated sensor exhibited better sensitivity than the pristine CNT sensor at room temperature. Both the repeatability and selectivity of the sensor were studied

A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets

A neural recording amplifier based on adaptive SNR optimization technique for long-term implantation

Long-term neural recording which can consistently provide good signal-to-noise ratio (SNR) performance over time is important for stable operation of neuroprosthetic systems. This paper presents an analysis for the SNR optimization in a changing environment which causes variations in the tissue-electrode impedance, Zte. Based on the analysis result, a neural recording amplifier (NRA) is developed employing the SNR optimization technique. The NRA can adaptively change its configuration for in situ SNR optimization. The SNR is improved by 4.69% to 23.33% as Zte changes from 1.59 MQ to 31.8 MQ at 1 kHz. The NRA is fabricated in a 0.18-μm standard CMOS process and operates at 1.8-V supply while consuming 1.6 μA It achieves an input-referred noise of 4.67 μVrms when integrated from 1 Hz to 10 kHz, which leads to the NEF of 2.27 and the NEF2VDD of 9.28. The frequency reponse is measured with a high-pass cutoff frequency of 1 Hz and a low-pass cutoff frequency of 10 kHz. The midband gain is set to 40 dB while occupying 0.11 mm2 of a chip area. © 2017 IEEE

Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis

Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis

Programmable Writing of Integrated Circuits on a Two-Dimensional van der Waals Semiconductor with a Scanning Light Probe

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Writing monolithic integrated circuits on a two-dimensional semiconductor with a scanning light probe

The development of complex electronics based on two-dimensional (2D) materials will require the integration of a large number of 2D devices into circuits. However, a practical method of assembling such devices into integrated circuits remains elusive. Here we show that a scanning visible light probe can be used to directly write electrical circuitry onto the 2D semiconductor molybdenum ditelluride (2H-MoTe2). Laser light illumination over metal patterns deposited onto 2D channels of 2H-MoTe2 can convert the channels from an n-type semiconductor to a p-type semiconductor, by creating adatom–vacancy clusters in the host lattice. With this process, diffusive doping profiles can be controlled at the submicrometre scale and doping concentrations can be tuned, allowing the channel sheet resistance to be varied over four orders of magnitudes. Our doping method can be used to assemble both n- and p-doped channels within the same atomic plane, which allows us to fabricate 2D device arrays of n–p–n (p–n–p) bipolar junction transistor amplifiers and radial p–n photovoltaic cells.11Nsciescopu

Reconfigurable doping of atomically thin van der Waals semiconductors by light colours

We report reversible photo-induced doping on atomically thin van der Waals (vdW) semiconductors, whose channel polarities can be repeatedly reconfigured from n-type to p-type and vice versa with light colours. Evidently, this reconfigurable doping is explained by selective light-lattice interactions, such as the characteristic point defect generations and the charged ion incorporation. The precise doping tunability in a reconfigurable manner of this work may provide a key technological solution toward realisation of new types of monolithic integrated circuitry on atomically thin vdW semiconductors. We demonstrate such a proof-of-concept device, a reconfigurable complementary metal-oxide-semiconductor (CMOS) device on a single vdW semiconductor channel, in which the circuit functions can be dynamically reset from a CMOS inverter to a CMOS switch with the choice of light colours.2

Programmable writing of integrated circuits on a two-dimensional van der Waals semiconductor

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