Density functional theory studies demonstrate that defective graphene-supported Cu nanoparticles can modify the structural and electronic properties of copper for enhancing electrochemical reduction of carbon dioxide (CO2) into hydrocarbon fuels (CH4, CO, and HCOOH). We not only provide improved understanding of CO2 conversion mechanisms on both Cu and the Cu nanoparticle system, but also explain a key factor for enhanced CO2 conversion. A promising catalytic material for CO2 conversion into hydrocarbon fuels may allow for geometry flexibility upon interaction with a key intermediate of CHO*.
In this paper, an IoT-based indoor air quality monitoring platform, consisting of an air quality-sensing device called “Smart-Air” and a web server, is demonstrated. This platform relies on an IoT and a cloud computing technology to monitor indoor air quality in anywhere and anytime. Smart-Air has been developed based on the IoT technology to efficiently monitor the air quality and transmit the data to a web server via LTE in real time. The device is composed of a microcontroller, pollutant detection sensors, and LTE modem. In the research, the device was designed to measure a concentration of aerosol, VOC, CO, CO2, and temperature-humidity to monitor the air quality. Then, the device was successfully tested for reliability by following the prescribed procedure from the Ministry of Environment, Korea. Also, cloud computing has been integrated into a web server for analyzing the data from the device to classify and visualize indoor air quality according to the standards from the Ministry. An application was developed to help in monitoring the air quality. Thus, approved personnel can monitor the air quality at any time and from anywhere, via either the web server or the application. The web server stores all data in the cloud to provide resources for further analysis of indoor air quality. In addition, the platform has been successfully implemented in Hanyang University of Korea to demonstrate its feasibility.
This study deals with (a) the development of a prototype 3D printer for concrete structures having a bed size of 1 × 1 × 1 m for a laboratory testing and (b) laboratory testing of cementitious materials with different design mixes to find their suitability and efficacy for the developed 3D printer. In this printer, a program with the concept of computer numerical controlled milling was adopted to control the nozzle motion using an easy graphic user interface program. The experiment was carried out to test mechanical control and proper material properties of the printer. Thus, the optimum values of water-cement ratio of cementitious materials for the 3D concrete printer were determined by experimental trials. Also, the adequate viscosity of the material for layering and dispensing is determined by a slump-flow test. The suitable size of sands for the dispensing system was found through the trials. However, shrinkage cracks occurred during the hardening process for the paste and mortar that polyvinyl alcohol fibers are added to prevent the cracking and build an improved quality 3D printed structure. After suitable and efficient mix ratio is found, compressive strength is measured for the mechanical property. The experiments demonstrated possibility of printing concrete structure using the 3D printer.
Accurate sensing is the key to structural health monitoring of underground coal mines while using fiber Bragg grating (FBG) sensors. However, the previously developed systems for structural monitoring of underground mines have been limited to monitoring without any capability of damage detection. Therefore, this study integrates a highly accurate FBG monitoring system and output-only data-driven approaches on an Internet of things (IoT)-based platform to develop a comprehensive mine structural safety system. This system relies on a Web 2.0 main server that runs data acquisition, data processing, and damage detection algorithms along with real-time information sharing at remote locations. This system was successfully implemented at the Hassan Kishore coal mine, situated in the Salt Range of Pakistan. Wavelength division multiplexing of the FBG strain sensors reliably captured the effects of dynamic and continuous coal excavation on the stability of mine roadway and access galleries. Principal component analysis, along with hierarchical clustering, was used to determine the damage indicator of the mine. The damage index was validated, showing the minimum value for 2% stiffness reduction. Thus, integration of FBG technology with the Internet can be effectively applied for early safety assessment of underground coal mines and information sharing in real time.
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