SO_x emissions are primarily caused by compounds containing sulfur in petroleum and fuels, which lead to severe air pollution. For this reason, it is necessary to develop a fast and simple desulfurization method in order to comply with ever-increasing environmental regulations. The newly discovered piezo-catalyst nanocomposite Ce_xO_y/SrO can convert mechanical energy directly into chemical energy, thereby enabling mechanically oxidative sulfur desulfurization. 320 W of bath sonication were used to polarize and activate the prepared piezo-catalyst nanocomposite Ce_xO_y/SrO for sulfur removal from thiophene and dibenzothiophene as model fuels and kerosene as a real fuel. Using uniform and spherical CeO₂/SrO nanocomposites resulted in the highest desulfurization rates of 95.4 %, 97.3 %, and 59.7 %, respectively, for thiophene and dibenzothiophene. This study examined the effect of several parameters, such as sulfur concentration, pH of fuel, dosage of CexOy/SrO nanocomposite, power and time of ultrasonic, and shaking time, on the piezo-desulfurization of thiophene (TP) and dibenzothiophene (DBTP). To identify the major active species in piezo desulfurization, radical trapping experiments were conducted. This study investigated the possibility of reusing the catalyst, and the piezo-desulfurization activity that was demonstrated in the removal of TP and DBTP after 11 cycles as well as the ability of the catalyst to remove real fuel even after 14 cycles was promising. As the kinetic results show, the reaction follows the second order with K = 0.0050. Also, thermodynamic results showed the oxidation of sulfide to sulfoxide and sulfoxide is endothermic. Activation energy for second order rate constant is (3.824 Kj/mole). 0.0236 mol^-1. Sec⁻¹ was calculated for Arrhenius Constant.

This study investigates the effects of piezo‐catalysts on sterilizing surfaces. The memory effects in three piezo‐catalysts, ZnO, CuO, and SiO₂ are discovered, which are produced by a calcination process. After applying mechanical force to these materials, they retain an antibacterial effect for a period of days. With this discovery, it is possible to combat antibiotic‐resistant bacteria by using piezo materials on hospital floors or operating rooms that can kill bacteria just by walking on them. The results show that ZnO, CuO, and SiO₂ are capable of killing bacteria even after being subjected to mechanical force for 9 days. The memory effect duration can be influenced by a variety of factors, including the calcination temperature, the storage condition after ultrasonication, the drying temperature after ultrasonication, and the solvent in which the piezo‐catalyst is ultrasonicated. When ZnO, CuO, and SiO₂ are kept under a vacuum in a dark environment, the piezo effect remains almost constant for 11 days after sonication.

This study explores the effect of cement chemical composition on the compressive strength (CS) of pure cement, cement modified with Nano Silica (NS), and Silica Fume (SF) across various mix proportions, curing times, and temperatures. Three new parameters, Silicate Modulus (SM), Aluminate Modulus (AM), and Hydraulic Modulus (HM), were introduced to evaluate cement composition’s impact on CS. SM combines SiO₂, Al₂O₃, and Fe₂O₃; AM integrates Al₂O₃ and Fe₂O₃; and HM includes CaO, SiO₂, Al₂O₃, and Fe₂O₃. Using 355 datasets, two mathematical models, Cubic (CUB) and linear and inverse (L&I), predicted CS, considering factors like cement composition, water-to-binder ratio, NS and SF content, curing time, and temperature. Statistical tools such as R 2 , RMSE, MAE, and SI validated model accuracy. The study assessed three scenarios: (1) multi-variate analysis of individual cement oxides; (2) bi-variate analysis using SM and AM, and (3) mono-variate analysis using HM. Introducing SM, AM, and HM improved model accuracy and reduced input parameters. Optimal NS and SF replacement percentages were 4.64% and 14.6%, respectively. SM, AM, and HM optimal values were 2.62, 1.38, and 2.21. Although initial costs increase with these replacements, they offer long-term benefits and align with sustainable construction practices.

Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model’s effectiveness. A good correlation is found between the measured lab data and the model’s predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R²) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes.