SOUTHERN JOURNAL OF SCIENCES

A new phase of the (CuAlSe₂)_1-x(TaSe)_x alloy system was synthesized by the melt and annealingtechnique and studied by SEM, DTA, and XRPD techniques. Its structure has been refined by the Rietveld methodusing X-ray powder diffraction data. The new alloy corresponds with the stoichiometry Cu_0.4Al_0.3TaSe₂. Thiscompound crystallizes in the hexagonal space group 𝑃6ത𝑚2 (Nº 187) with a MoS₂-type structure, and unit cellparameters a = 3.455(2) Å, c = 13.423(4) Å, V = 138.7(1) Å3, Z =2. The crystal structure is based on the MoS₂-type of stacking of TaSe₂ layers with a partial ordering of Cu and Al cations over the tetrahedral sites. The powderpattern was composed of 63.1% of the principal phase Cu_0.4Al_0.3TaSe₂ and 29.9% of CuAlSe₂, 7.0% of TaSe₃, asthe secondary phases. 

Background: MQ-series gas sensors belong to the metal oxide semiconductor (MOS) family of sensors that can sense the presence of many gases. These sensors find their application in gas alarm systems as key components. While necessary sensor circuit output voltage value for alarm point in a stand-alone gas alarm system is desirable, but what exact combination of the sensor circuit parameters is required? Hitherto, the determination of these circuit parameters has not been given much attention in the research community. Aim: the purpose of this work is to explore a structured graphical approach of determination of MQ series gas sensor circuit parameters for a stand-alone gas alarm system that yields desired sensor circuit output voltage value for the alarm point; the main objective of the study was to develop mathematical model equations that relate the: (i) sensor resistance (RS) with the gas concentration (x) and the sensor resistance at standard calibration concentration of the sensor base gas in the clean air (Ro) and (ii) sensor circuit output voltage (VRL), load resistance (RL) and sensor resistance (RS). It is expected from the model equations developed that graphical correlations of the sensor circuits parameters will be generated. Using these graphs for a particular case of an MQ-4 gas sensor under the influence of LPG, the parameters that yield desired sensor circuit output voltage of 2V for 1000 ppm of LPG alarm point will be determined. Methods: Model equations were developed for the sensor dynamics, and based on these model equations, graphs for the determination of required sensor parameters were plotted for a case of MQ-4 gas sensor response to LPG. Results and Discussion: The results yielded optimal values for R_O,R_S and R_L of 20 kΩ, 30 kΩ and 20 kΩ respectively, for alarm settings of 1000 ppm and a desired sensor circuit output voltage of 2 V. Based on determined parameters, the calibration equation for determination of best concentration value for a given value of emulated LPG concentration was developed. Using the method proposed in this study makes the process of determining the MQ-series gas sensor circuit parameters less cumbersome as their value can easily be obtained from the resulting graphs. Conclusions: a structured graphical approach for determination of MQ-series gas sensor circuit parameters for alarm points in a stand-alone gas alarm system showed that using MQ-4 gas sensor and LPG as the target gas, and for a sensor circuit output voltage of 2 V for alarm point at 1000 ppm of LPG, the corresponding value of R_O, R_S and R_L obtained were 20 kΩ, 30 kΩ, and 20 kΩ respectively. Hence, a structured graphical approach is suitable for determining MQ series gas sensor circuit parameters for a stand-alone gas alarm system under the influence of its associated gases.

Background: A new management model for construction and demolition waste (CDW) was proposed for the city of Rio de Janeiro, hence waste the city planning area estimated generation; the results were compared with the data published by COMLURB and SNIS through the statistics tests was proved that CDW had been dispersed, which support the hypothesis that the current model cause dispersion of the rubble. Aim: This study proposes a new management model for CDW to the city, according to regions where occur the higher production of rubble. Methods: The estimation of rubble was obtained through the issued licenses from 2006 to 2020 published by SMPU. The quantification of rubble in the city was carried out by adopting the generation indicator from licensed construction activities. The amount of CDW published by COMLURB and SNIS was compared through statistics tests ANOVA and T-Test, the second period from 2006 to 2020 and 2011 to 2020. To measure the area to implant a CDW recycling plant, criteria from production capacity were used. Results: ANOVA test to data from 2006 to 2020 and 2011 to 2020, according to a confidence interval of 95%, found the P value 0.589 and 0.022, respectively, it was verified that the significant difference is between data from COMLURB and Estimated. The T-test was applied from the same period in data from SNIS and estimated the P values 0.399 and 0.014. Discussion: The data from Estimating between 2006 and 2020 showed the best representation. The Área de Planejamento – AP (Planning Area) 4 was where 50.70% of the total rubble, after the AP 3 with 17.66%, the AP 5 was estimated at 16.59%, last of all, AP 2 and AP 1 were calculated the generation of 8.28% and 6.77%, respectively. Therefore, sizing the CDW recycling producing 361,99.00 tons per year demands an area of 32,397.50 m². Conclusions: It was concluded that the receipt of CDW in transfer stations managed by COMLURB is not allowed; therefore, the need to create a construction waste recycling plant was presented, to this end, it is necessary to have an available area of approximately 32,397.50 m².