Throughout the application of enhanced oil recovery (EOR), surfactant adsorption is considered the leading constraint on both the successful implementation and economic viability of the process. In this study, a comprehensive investigation on the adsorption behaviour of nonionic and anionic individual surfactants; namely, alkyl polyglucoside (APG) and alkyl ether carboxylate (AEC) was performed using static adsorption experiments, isotherm modelling using (Langmuir, Freundlich, Sips, and Temkin models), adsorption simulation using a state-of-the-art method, binary mixture prediction using the modified extended Langmuir (MEL) model, and artificial neural network (ANN) prediction. Static adsorption experiments revealed higher adsorption capacity of APG as compared to AEC, with sips being the most fitted model with R2 (0.9915 and 0.9926, for APG and AEC respectively). It was indicated that both monolayer and multilayer adsorption took place in a heterogeneous adsorption system with non-uniform surfactant molecules distribution, which was in remarkable agreement with the simulation results. The (APG/AEC) binary mixture prediction depicted contradictory results to the experimental individual behaviour, showing that AEC had more affinity to adsorb in competition with APG for the adsorption sites on the rock surface. The adopted ANN model showed good agreement with the experimental data and the simulated adsorption values for APG and AEC showed a decreasing trend as temperature increases. Simulating the impact of binary surfactant adsorption can provide a tremendous advantage of demonstrating the binary system behaviour with less experimental data. The utilization of ANN for such prediction procedure can minimize the experimental time, operating cost and give feasible predictions compared to other computational methods. The integrated workflow followed in this study is quite innovative as it has not been employed before for surfactant adsorption studies.
Monitoring the COVID-19 virus through patients' saliva is a favorable non-invasive specimen for diagnosis and infection control. In this study, salivary samples of COVID-19 patients collected from 6 patients with the median age of 58.5 years, ranging from 34 to 72 years (2 females and 4 males) were analyzed using an Au/fiber Bragg grating (FBG) probe decorated with GO. The probe measures the prevalence of positivity in saliva and the association between the virus density and changes to sensing elements. When the probe is immersed in patients' saliva, deviation of the detected light wavelength and intensity from healthy saliva indicate the presence of the virus and confirms infection. For a patient in the hyperinflammatory phase of desease, who has virus density of 1.2 × 108 copies/mL in saliva, the maximum wavelength shift and intensity changes after 1600 s were shown to be 1.12 nm and 2.01 dB, respectively. While for a patient in the early infection phase with 1.6 × 103 copies/mL, these values were 0.98 nm and 1.32 dB. The precise and highly sensitive FBG probe proposed in this study was found a reliable tool for quick detection of the COVID-19 virus within 10 s after exposure to patients' saliva in any stage of the disease.