Functionalization of Molybdenum Disulfide via Plasma Treatment and 3-Mercaptopropionic Acid for Gas Sensors.

Monolayer and multilayer molybdenum disulfide (MoS2) materials are semiconductors with direct/indirect bandgaps of 1.2-1.8 eV and are attractive due to their changes in response to electrical, physicochemical, biological, and mechanical factors. Since the desired electrical properties of MoS2 are known, research on its electrical properties has increased, with focus on the deposition and growth of large-area MoS2 and its functionalization. While research on the large-scale production of MoS2 is actively underway, there is a lack of studies on functionalization approaches, which are essential since functional groups can help to dissolve particles or provide adequate reactivity. Strategies for producing films of functionalized MoS2 are rare, and what methods do exist are either complex or inefficient. This work introduces an efficient way to functionalize MoS2. Functional groups are formed on the surface by exposing MoS2 with surface sulfur vacancies generated by plasma treatment to 3-mercaptopropionic acid. This technique can create 1.8 times as many carboxyl groups on the MoS2 surface compared with previously reported strategies. The MoS2-based gas sensor fabricated using the proposed method shows a 2.6 times higher sensitivity and much lower detection limit than the untreated device.

Neural network modeling of deposition rate characteristics of low temperature silicon nitride deposited by inner two parallel coil inductively coupled plasma chemical vapor deposition.

In this study, a neural network model for silicon nitride (SiN) deposition process is proposed. SiN thin films were deposited by a direct inner two parallel inductively coupled plasma chemical vapor deposition (ICPCVD) system that can control the activated radical and charged species in plasma. This system can produce SiN thin films at low temperature for flexible displays. The input parameters considered for deposition conditions were the N2 gas flow rate, NH3 gas flow rate, and substrate temperature. These were varied within the ranges of 0-20 sccm, 0-20 sccm, and 100-300 degrees C, respectively. For those of input parameters and the output of deposition rate, we developed a back propagation neural network model with a pre-processor. It is shown that the model accuracy and learning speed of the proposed model are better than those of a conventional neural network model. In the experiments conducted, it was found that the deposition rate increased as the flow rates of ammonia (NH3) and nitrogen (N2) increased up to a certain amount. On the contrary, when the flow rates of NH3 and N2 went over a certain amount, the deposition rate decreased. It was also found that an increase in temperature resulted in an increase in the deposition rate.

Design and fabrication of a PZT cantilever for low frequency vibration energy harvesting.

In this study, a PZT cantilever with a Si proof mass is designed and fabricated for a low frequency energy harvesting application. A mathematical model of a multi-layer composite beam was derived and applied in a parametric analysis of the piezoelectric cantilever. Finally, the dimensions of the cantilever were determined for the resonant frequency of the cantilever. Our cantilever design was based on MATLAB and ANSYS simulations. For this simulation, the proof mass volumes were varied from 0 to 0.5 mm3 and resonant frequencies were calculated from 833.5 Hz to 125.5 Hz, respectively. Based on simulation, we fabricated a device with beam dimensions of about 4.10 mm x 0.48 mm x 0.012 mm, and an integrated Si proof mass with dimensions of about 0.481 mm x 0.48 mm x 0.45 mm. The resonant frequency, maximum peak voltage, and highest average power of the cantilever device were 224.8 Hz, 4.8 mV, and 2.24 nW, respectively.