The study reports the development of a nanostructured mixed-valence Fe(II)Fe(III)BTC metal-organic framework (BTC = 1,3,5-benzenetricarboxylate) modified carbon paste electrode as a novel sensor for the amoxicillin detection in aqueous solutions using square wave voltammetry. The physicochemical properties of FeBTC were characterized using X-ray diffraction spectroscopy, Fourier-transform infrared spectroscopy, Brunauer-Emmett-Teller analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy. These techniques revealed that FeBTC has a surface area of 1211 m2/g, a total pore volume of 1.46 cm3/g, particle sizes ranging from 10 to 20 nm, and a mixed-valence structure. Furthermore, SEM, TEM, and energy-dispersive X-ray spectroscopy showed homogeneous distribution of FeBTC throughout the modified electrode. The electrochemical study showed that the mixed-valence FeBTC improved the electron transfer capabilities of the electrode. Under optimal conditions, the modified electrode exhibited a significant increase in peak height compared to the unmodified electrode (4.6 times higher), with an acceptable reproducibility of 4.88% relative standard deviation. The linear range of the sensor was 1-100 µM with a good coefficient of determination of 0.9985, and a detection limit of 0.107 µM. Additionally, the sensor demonstrated excellent performance with the satisfactory recoveries and a good correlation with LC-MS/MS analysis.

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Obtaining the maximum Rate of Penetration (ROP) by optimization drilling parameters is the aim of every drilling engineer. This is because it could save time, reduce cost and minimize drilling problems. However, ROP depends on a lot of parameters which lead to difficulties in its prediction. Therefore, it is necessary and important to investigate a solution predicting ROP with high accuracy to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real – time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as the weight on bit (WOB), weight of mud (MW), rotary speed (RPM), standpipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when compared to actual ROP, therefore it can be recommended as an effective and suitable method to predict the ROP of other wells in the research area. Besides, base on the proposed ANN model, authors carried out experiments and determind the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in Nam Rong Doi Moi field