Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Machine Learning-Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers

Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications.

Artificial neural network modelling and experimental investigations of malachite green adsorption on novel carboxymethyl cellulose/β-cyclodextrin/nickel cobaltite composite

This study presents a novel polymer nanocomposite based on carboxymethyl cellulose and β-cyclodextrin crosslinked with succinic acid (CMC-SA-β-CD) containing nickel cobaltite (NCO) nano-reinforcement. Various analytical techniques have been employed to investigate the structural, thermal, and morphological features of the resulting nanocomposite. The CMC-SA-β-CD/NCO nanocomposite has been utilized as an adsorbent for the removal of bisphenol-A (BPA, R% <40 %), malachite green (MG, R% > 75 %)), and Congo red (CR, no adsorption) from the synthetic wastewater. The study systematically explored the impact of various parameters on the adsorption process, and the interactions between MG and CMC-SA-β-CD/NCO were discussed. The adsorption data were fitted to different models to elucidate the kinetics and thermodynamics of the adsorption process. An artificial neural network (ANN) analysis was employed to train the experimental dataset for predicting adsorption outcomes. Despite a low BET surface area (0.798 m2 g-1), CMC-SA-β-CD/NCO was found to exhibit high MG adsorption capacity. CMC-SA-β-CD/NCO exhibited better MG adsorption performance at pH 5.5, 40 mg L-1 MG dye concentration, 170 min equilibrium time, 20 mg CMC-SA-β-CD/NCO dose with more than 90 % removal efficiency. Moreover, the thermodynamic studies suggest that the adsorption of MG was exothermic with ΔH° value -9.93 ± 0.76 kJ mol-1. The isotherm studies revealed that the Langmuir model was the best model to describe the adsorption of MG on CMC-SA-β-CD/NCO indicating monolayer surface coverage with Langmuir adsorption capacity of 182 ± 4 mg g-1. The energy of adsorption (11.4 ± 0.8 kJ mol-1) indicated chemisorption of MG on the composite surface. The kinetics studies revealed that the pseudo-first-order model best described the adsorption kinetics with q e  = 86.7 ± 2.9 mg g-1. A good removal efficiency (>70 %) was retained after five regeneration reuse cycles. The ANN-trained data showed good linearity between predicted and actual data for the adsorption capacity (R-value>0.99), indicating the reliability of the prediction model. The developed nanocomposite, composed predominantly of biodegradable material, is facile to synthesize and exhibited excellent monolayer adsorption of MG providing a new sustainable adsorbent for selective MG removal.

Magnetic Biochar as a Revolutionizing Approach for Diverse Dye Pollutants Elimination: A Comprehensive Review

The term "biomass" encompasses all substances found in the natural world that were once alive or derived from living organisms or their byproducts. These substances consist of organic molecules containing hydrogen, typically oxygen, frequently nitrogen, and small amounts of heavy, alkaline earth and alkali metals. Magnetic biochar refers to a type of material derived from biomass that has been magnetized typically by adding magnetic components such as magnetic iron oxides to display magnetic properties. These materials are extensively applicable in widespread areas like environmental remediation and catalysis. The magnetic properties of these compounds made them ideal for practical applications through their easy separation from a reaction mixture or environmental sample by applying a magnetic field. With the evolving global strategy focused on protecting the planet and moving towards a circular, cost-effective economy, natural compounds, and biomass have become particularly important in the field of biochemistry. The current research explores a comparative analysis of the versatility and potential of biomass for eliminating dyes as a sustainable, economical, easy, compatible, and biodegradable method. The elimination study focused on the removal of various dyes as pollutants. Various operational parameters which influenced the dye removal process were also discussed. Furthermore, the research explained, in detail, adsorption kinetic models, types of isotherms, and desorption properties of magnetic biochar adsorbents. This comprehensive review offers an advanced framework for the effective use of magnetic biochar, removing dyes from textile wastewater.

Green synthesis of carbon nanotubes functionalized with iron nanoparticles and coffee husk biomass for efficient removal of losartan and diclofenac: Adsorption kinetics and ANN modeling studies

The presence of emerging contaminants in wastewater poses a global environmental challenge, requiring the development of innovative materials or methods for their treatment. This study focused on the production of green functionalized carbon nanotubes (CNTs) and using them in the adsorption of the pharmaceuticals Losartan (LOS) and Diclofenac (DIC). The efficiency of the methodology was verified by characterization techniques. Elemental composition analysis indicated a significant increase in the iron content after the green functionalization, proving the effectiveness of the method. Thermogravimetric analysis showed similar thermal degradation profiles for pristine CNTs and functionalized CNTs, indicating better post-functionalization thermal stability. BET analysis revealed mesoporous characteristics of CNTs, with increased surface area and pore volumes after functionalization. X-Ray diffraction confirmed the preservation of the lattice structure of the CNTs post-functionalization and post-adsorption, with changes in peak broadening suggesting surface modifications. LOS and DIC adsorption were evaluated via kinetic studies at four different concentrations (0.1-0.4 mmol/L) that were best represented by the pseudo-second order model, suggesting chemisorption mechanisms, with faster and higher uptakes for DIC (0.084-0.261 mmol/g; teq = 5 min) when compared to LOS (0.058-0.235 mmol/g; teq = 20 min). The curves were also studied via artificial neural networks (ANN) and revealed that the best ANN architecture for representing the experimental data is a network with [3 5 5 2] neurons trained using the Bayesian-Regularization algorithm and the Log-sigmoid (hidden layers) and Linear (output layer) transfer functions. The desorption study showed that CaCl2 had better performance in CNT regeneration, reaching its removal capacity above 50% up to 3 cycles, for both pharmaceuticals. These findings reveal the potential of the developed material as a promising adsorbent for targeted removal of pollutants, contributing to advances in the remediation of emerging contaminants and the application of artificial intelligence in adsorption research.

Adsorption behavior and the potential risk of As(V) in soils: exploring the effects of representative surfactants

Arsenic contamination in soils poses a critical global challenge, yet the influence of surfactants on arsenic adsorption behavior is often underestimated. This study aims to investigate the effects of three representative surfactants, namely cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and polyethylene glycol anhydrous sugar alcohol monooleate (Tween 80), on arsenic adsorption behavior in soils. The adsorption isotherm shifts from a single Temkin model without surfactants to both the Langmuir and Temkin models in the presence of surfactants, indicating the simultaneous occurrence of monolayer and multilayer adsorption for arsenic in soils. Moreover, the surfactants can inhibit the adsorption and hasten the attainment of adsorption equilibrium. SDS displayed the most inhibitory effect on arsenic adsorption, followed by Tween 80 and CTAB, due to the competitive adsorption, electrostatic interaction, and hydrophobic interaction. Variations in zeta potential with different surfactants further elucidate this inhibitory phenomenon. Through orthogonal experiment analyses, pH emerges as a primary factor influencing arsenic adsorption in soils, with surfactant concentration and type identified as secondary factors. Temperature notably affects CTAB, with the adsorption inhibition rate plummeting to a mere 0.88% at 50 °C. Sequential extraction analysis revealed that surfactants enhanced the bioavailability of arsenic. The FTIR, XRD, SEM, and CA analyses further support the mechanism underlying the effect of surfactants on arsenic adsorption in soil. These analyses indicate that surfactants modify the composition and abundance of functional groups, hinder the formation of arsenic-containing substances, and improve soil compactness, smoothness, and hydrophilicity. This study provides valuable insights into the effect of surfactants in arsenic-contaminated soils, which is often ignored in previous work.

Removal of Cr(VI) from aqueous solution using ball mill modified biochar: multivariate modeling, optimization and experimental study

Chromium (Cr(VI)) pollution has attracted wide attention due to its high toxicity and carcinogenicity. Modified biochar has been widely used in the removal of Cr(VI) in water as an efficient and green adsorbent. However, the existing biochar prepared by chemical modification is usually complicated in process, high in cost, and has secondary pollution, which limits its application. It is urgent to explore modified biochar with simple process, low cost and environmental friendliness. Therefore, ball milling wheat straw biochar (BM-WB) was prepared by ball milling technology in this paper. The adsorption characteristics and mechanism of Cr(VI) removal by BM-WB were analyzed by functional group characterization, adsorption model and response surface method. The results showed that ball milling effectively reduced the particle size of biochar, increased the specific surface area, and more importantly, enhanced the content of oxygen-containing functional groups on the surface of biochar. After ball milling, the adsorption capacity of Cr(VI) increased by 3.5-9.1 times, and the adsorption capacity reached 52.21 mg/g. The adsorption behavior of Cr(VI) follows the pseudo-second-order kinetics and Langmuir isotherm adsorption model rate. Moreover, the Cr(VI) adsorption process of BM-WB is endothermic and spontaneous. Under the optimized conditions of pH 2, temperature 45 °C, and adsorbent dosage 0.1 g, the removal rate of Cr(VI) in the solution can reach 100%. The mechanism of Cr(VI) adsorption by BM-WB is mainly based on electrostatic attraction, redox and complexation. Therefore, ball milled biochar is a cheap, simple and efficient Cr(VI) removal material, which has a good application prospect in the field of remediation of Cr(VI) pollution in water.

Comparison between hydrodynamic and ultrasound cavitation on the inactivation of lipoxygenase and physicochemical properties of soy milk

The effects of hydrodynamic cavitation (HC) and ultrasound cavitation (UC) on the lipoxygenase activity and physicochemical properties of soy milk were evaluated. The results revealed that both ultrasound cavitation and hydrodynamic cavitation significantly inactivated the lipoxygenase activity. After the exposure to ultrasound cavitation at 522.5 W/L and 70 °C for 12 min, the lipoxygenase activity was inactivated by 96.47 %. Meanwhile, HC treatment with the cavitation number of 0.0133 for 240 min led to the loss of 79.31 % of lipoxygenase activity. An artificial neural network was used to model and visualize the effects of different parameters after ultrasound cavitation treatment on the inactivation efficiency of soy milk. Turbiscan test results showed that hydrodynamic and ultrasound cavitation decreased the instability index and particle size of soy milk. Moreover, the total free amino acid content was significantly increased after hydrodynamic and ultrasound cavitation treatment. Gas chromatography-mass spectrometry showed that the total content of beany flavor compounds decreased after acoustic cavitation and HC treatment. Acoustic cavitation and HC affected the tertiary and secondary structure of soy milk, which was related to the inactivation of lipoxygenase. We aim to explore a potential and effective way of the application in soy milk processing by comparing the ultrasound equipped with heat treatment and hydrodymic cavitation.

Nano-silica from white silica sand functionalized with PANI-SDS (SiO2/PANI-SDS) as an adsorbent for the elimination of methylene blue from aqueous media

Natural resources including sand are one of the best approaches for treating dye-polluted wastewater. The SiO2/PANI-SDS nanocomposite was synthesized by self-assembly and intermolecular interaction. The physicochemical features of the SiO2/PANI-SDS nanocomposite were explored by FT-IR, XRD, SEM, TEM, EDX, and N2 adsorption-desorption techniques to be evaluated as an adsorbent for the MB. The surface area of the SiO2/PANI-SDS is 23.317 m2/g, the pore size is 0.036 cm3/g, and the pore radius is 1.91 nm. Batch kinetic studies at different initial adsorbate, adsorbent and NaCl concentrations, and temperatures showed excellent pseudo-second-order. Several isotherm models were applied to evaluate the MB adsorption on the SiO2/PANI-SDS nanocomposite. According to R2 values the isotherm models were fitted in the following order: Langmuir > Dubinin-Radushkevich (D-R) > Freundlich. The adsorption/desorption process showed good reusability of the SiO2/PANI-SDS nanocomposite.

Extensive sorption of Amoxicillin by highly efficient carbon-based adsorbent from palm kernel: Artificial neural network modeling

In the present study, a new sorbent was fabricated from Palm kernel (PK) by dry thermochemical activation with NaOH and characterized by FTIR, X-ray diffraction, FE-SEM and BET, which was used for the Amoxicillin (AMX) sorption from aqueous solution. The influence of effective parameters such as pH, reaction time, adsorbent dosage, AMX concentration and ionic strength on the sorption efficacy of AMX removal were evaluated. The main functional groups on the surface of the magnetic activated carbon of Palm Kernel (MA-PK) were C-C, C-O, C[bond, double bond]O and hydroxyl groups. The specific surface of char, activated carbon Palm Kernel (AC-PK) and MA-PK were 4.3, 1648.8 and 1852.4 m2/g, respectively. The highest sorption of AMX (400 mg/L) was obtained by using 1 g/L of sorbent at solution pH of 5 after 60 min contact time, which corresponding to 98.77%. Non-linear and linear models of isotherms and kinetics models were studied. The data fitted well with Hill isotherm (R2 = 0.987) and calculated maximum sorption capacity were 719.07 and 512.27 mg/g from Hill and Langmuir, respectively. A study of kinetics shows that the adsorption of AMX follows the Elovich model with R2 = 0.9998. Based on the artificial neural network (ANN) modeling, the MA-PK dosage and contact time showed the most important parameters in the removal of AMX with relative importance of 36.5 and 25.7%, respectively. Lastly, the fabricated MA-PK was successfully used to remove the AMX from hospital wastewater.

Synthesis of Polyether Carboxylate and the Effect of Different Electrical Properties on Its Viscosity Reduction and Emulsification of Heavy Oil

Heavy oil exploitation needs efficient viscosity reducers to reduce viscosity, and polyether carboxylate viscosity reducers have a significant viscosity reduction effect on heavy oil. Previous work has studied the effect of different side chain lengths on this viscosity reducer, and now a series of polyether carboxylate viscosity reducers, including APAD, APASD, APAS, APA, and AP5AD (the name of the viscosity reducer is determined by the name of the desired monomer), with different electrical properties have been synthesized to investigate the effect of their different electrical properties on viscosity reduction performance. Through the performance tests of surface tension, contact angle, emulsification, viscosity reduction, and foaming, it was found that APAD viscosity reducers had the best viscosity reduction performance, reducing the viscosity of heavy oil to 81 mPa·s with a viscosity reduction rate of 98.34%, and the worst viscosity reduction rate of other viscosity reducers also reached 97%. Additionally, APAD viscosity reducers have the highest emulsification rate, and the emulsion formed with heavy oil is also the most stable. The net charge of APAD was calculated from the molar ratio of the monomers and the total mass to minimize the net charge. While the net charge of other surfactants was higher. It shows that the amount of the surfactant's net charge affects the surfactant's viscosity reduction effect, and the smaller the net charge of the surfactant itself, the better the viscosity reduction effect.

Novel Anionic-Nonionic Surfactant Based on Water-Solid Interfacial Wettability Control for Residual Oil Development

Irreversible colloidal asphaltene adsorption layers are formed on formation rock surfaces due to long-term contact with crude oil, and large amounts of crude oil adhere to these oil-wet layers to form residual oil films. This oil film is difficult to peel off due to the strong oil-solid interface effect, which seriously restricts further improvement in oil recovery. In this paper, the novel anionic-nonionic surfactant sodium laurate ethanolamide sulfonate (HLDEA) exhibiting strong wetting control was synthesized by introducing sulfonic acid groups into the nonionic surfactant laurate diethanolamide (LDEA) molecule through the Williamson etherification reaction. The introduction of the sulfonic acid groups greatly improved the salt tolerance and the absolute value of the zeta potential of the sand particles. The experimental results showed that HLDEA altered the wettability of the rock surface from oleophilic to strongly hydrophilic, and the underwater contact angle increased substantially from 54.7 to 155.9°. In addition, compared with LDEA, HLDEA exhibited excellent salt tolerance and enhanced oil recovery performance (the oil recovery was improved by 19.24% at 2.6 × 10⁴ mg/L salinity). Based on nanomechanical experimental results, HLDEA was efficiently adsorbed on the core surfaces and regulated microwetting. Moreover, HLDEA effectively reduced the adhesion force between the alkane chains and the core surface, which facilitated residual oil stripping and oil displacement. This new anionic-nonionic surfactant affording great oil-solid interface wetting control has practical significance for the efficient development of residual oil.

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models' predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand-brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

Prediction of Oil Sorption Capacity on Carbonized Mixtures of Shungite Using Artificial Neural Networks

Using the mixture of carbonized rice husk and shungite from the Kazakhstan Koksu deposit and the experimentally determined oil sorption capacity from contaminated soil with oil originating in the Karazhanbas oil field, a set of Artificial Neural Network (ANN) models were built for sorption predictions. The ANN architecture design, training, validation and testing methodology were performed, and the sorption capacity prediction was evaluated. The ANN models were successfully trained for capturing the sorption capacity dependence on time and on a carbonized rice husk and shungite mixture ratio for the 10% and 15% oil-contaminated soil. The best trained ANNs revealed a very good prediction capability for the testing data subset, demonstrated by the high coefficient of the determination values of R2 = 0.998 and R2 = 0.981 and the mean absolute percentage errors ranging from 1.60% to 3.16%. Furthermore, the ANN sorption models proved their interpolation ability and utility for predicting the sorption capacity for any time moments in the investigated time interval of 60 days and for new values of the shungite and rice husk mixture ratios. The ANN developed models open opportunities for planning new experiments, maximizing the sorption performance and for the design of dedicated equipment.

Performance and mechanism of GO removal by gypsum from aqueous solution

The widespread production and application of graphene oxide (GO) may lead to its dispersion throughout natural water systems, with potential negative effects on living organisms and the ecological environment. This study used gypsum (G) as an adsorbent and examined different conditions (pH, adsorbent dosage, GO initial concentration) for the removal effect of GO by G. The results showed the best adsorption effect for a solution pH of 8.0, gypsum dosage of 60 mg, initial GO concentration of 80 mg·L^-1, and temperature of 303 K; at this time, the maximum removal rate of graphene oxide by gypsum was 93.3%. It could be obtained by isotherm and thermodynamic analysis that the GO adsorption by gypsum conforms to the Langmuir isotherm model, it does not easily occur in high-temperature environments, and is a spontaneous exothermic process. In addition, experiments such as SEM, AFM, TGA, XRD, XPS, FTIR, Raman, and Zeta were used to adsorb graphene oxide by gypsum composites (G/GO), through which the mineral interactions with graphene oxides were microscopically characterized. The impact on the adsorption properties of contaminants provides new insights into contaminant removal by gypsum.

Predictive capability evaluation and mechanism of Ce (III) extraction using solvent extraction with Cyanex 572

Owing to the high toxicity of cerium toward living organisms, it is necessary to remove cerium from aqueous solutions. In this regard, the extraction of cerium (Ce (III)) from nitrate media by Cyanex 572 under different operating conditions was examined in this study. The effect of contact time, pH, extractant concentration, and nitrate ion concentration were investigated to characterize the extraction behavior of cerium and based on these outcomes, an extraction mechanism was suggested. The analysis of infrared spectra of Cyanex 572 before and after the extraction of cerium indicated that cerium extraction was performed via a cation-exchange mechanism. Then, the predictive models based on intelligent techniques [artificial neural network (ANN) and hybrid neural-genetic algorithm (GA-ANN)] were developed to predict the cerium extraction efficiency. The GA-ANN model provided better predictions that resulted higher R² and lower MSE compared to ANN model for predicting the extraction efficiency of cerium by Cyanex 572. The interactive effects of each process variable on cerium extraction were also investigated systematically. pH was the most influential parameter on cerium extraction, followed by extractant concentration, nitrate ion concentration and contact time. Finally, the separation of cerium from other rare earth elements like La (III), Nd (III), Pr (III), and Y (III) was conducted and observed that the present system provides a better separation of cerium from rare heavy earth than light rare earths.

Preparation of spiramycin fermentation residue derived biochar for effective adsorption of spiramycin from wastewater

Spiramycin (SPI) fermentation residue (SFR) is classified as hazardous waste in China because of the residual antibiotics in it. SFR disposal in the traditional way is costly and wasteful of resources. In this study, pyrolysis method was adopted to covert SFR to biochar for SPI removal from wastewater, and the SPI adsorption performance was investigated. The results showed that the optimal pyrolysis temperature was 700 °C as the prepared biochar BC700 exhibited the highest SPI removal efficiency. The specific surface area of BC700 was 451.68 m²/g, and the maximum adsorption capacity was 147.28 mg/g. The adsorption mechanism involved electrostatic interaction, pore filling, π-π interaction, hydrogen bonding, and the participation of C-C and O-CO functional groups in the adsorption. No residual SPI was detected in BC700 indicating the detoxification of SFR was achieved. Moreover, after recycling for 5 times, the SPI removal efficiency was still higher than 80.0%. Therefore, this study could provide a promising method for SFR disposal.

Adsorption of benzene on soils under different influential factors: an experimental investigation, importance order and prediction using artificial neural network

The adsorption of benzene on soils is specifically associated with its migration and transformation. Although previous studies have proved that the adsorption of benzene is affected by various factors, studies simultaneously considering the effects of multiple factors are rare. This study aimed to identify the qualitative and quantitative relationships between multiple influential factors and the adsorption capacity of benzene (BC). Batch adsorption experiments considering different influential factors, including initial concentration (IC), pH, temperature (T), ion strength (IS) and organic matter content (OMC), were conducted in three kinds of soils collected in a chemical industry park. The correlation analysis between different influential factors and BC was carried out based on the experimental data. The artificial neural network (ANN) was applied to predict BC. The results showed that BC increased with the increase of T. As the pH increased, BCs on silty loam and loam increased, while that on sandy loam decreased. Besides, BCs on silty loam and loam raised with increasing OMC, while that on sandy loam remained unchanged. BCs on all three kinds of soils attained their peaks when IS was small and then become stable with an increase in IS. The sequence of correlation between BC and influential factors is listed as IC > OMC > T > IS > pH for silty loam, OMC > IC > T > IS > pH for loam and IC > T > IS > pH > OMC for sandy loam. ANN analysis showed satisfactory accuracy in predicting BC under different influential factors. These results help us understand the important factors affecting benzene adsorption and provide a tool to get the adsorption information easily in complex site conditions.

How to Build a Microplastics-Free Environment: Strategies for Microplastics Degradation and Plastics Recycling

Microplastics are an emergent yet critical issue for the environment because of high degradation resistance and bioaccumulation. Unfortunately, the current technologies to remove, recycle, or degrade microplastics are insufficient for complete elimination. In addition, the fragmentation and degradation of mismanaged plastic wastes in environment have recently been identified as a significant source of microplastics. Thus, the developments of effective microplastics removal methods, as well as, plastics recycling strategies are crucial to build a microplastics-free environment. Herein, this review comprehensively summarizes the current technologies for eliminating microplastics from the environment and highlights two key aspects to achieve this goal: 1) Catalytic degradation of microplastics into environmentally friendly organics (carbon dioxide and water); 2) catalytic recycling and upcycling plastic wastes into monomers, fuels, and valorized chemicals. The mechanisms, catalysts, feasibility, and challenges of these methods are also discussed. Novel catalytic methods such as, photocatalysis, advanced oxidation process, and biotechnology are promising and eco-friendly candidates to transform microplastics and plastic wastes into environmentally benign and valuable products. In the future, more effort is encouraged to develop eco-friendly methods for the catalytic conversion of plastics into valuable products with high efficiency, high product selectivity, and low cost under mild conditions.

Performance and mechanisms of fly ash for graphene oxide removal from aqueous solution

The potential wide use of graphene oxide in various fields results in the possibility of its dispersion throughout natural water systems, with a negative impact on organisms and ecosystems. This study evaluated the removal of graphene oxide (GO) from water by fly ash (FA). The effects of various conditions (including the initial concentration of graphene oxide, the pH of the initial solution, the amount of absorbent, and temperature) on the removal rate of GO were investigated in detail. The results show that the maximum removal rate of graphene oxide by fly ash is 93%; the isotherm adsorption process conforms to a Langmuir model; the adsorption reaction is a spontaneous exothermic process. Under optimal conditions, the pH of the solution was adjusted to 6, the amount of fly ash was 5 mg, the initial concentration of GO was 60 mg·L^-1, and the temperature was 303 K. Using X-ray diffraction (XRD), Raman spectroscopy (Raman), Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), transmission electron microscopy (TEM), Zeta potential and X-ray electron spectroscopy (XPS), the adsorption mechanism was characterized. The experimental results demonstrate that fly ash is a good material for GO removal from aqueous solution.

Surfactant Adsorption Isotherms: A Review

The need to minimize surfactant adsorption on rock surfaces has been a challenge for surfactant-based, chemical-enhanced oil recovery (cEOR) techniques. Modeling of adsorption experimental data is very useful in estimating the extent of adsorption and, hence, optimizing the process. This paper presents a mini-review of surfactant adsorption isotherms, focusing on theories of adsorption and the most frequently used adsorption isotherm models. Two-step and four-region adsorption theories are well-known, with the former representing adsorption in two steps, while the latter distinguishes four regions in the adsorption isotherm. Langmuir and Freundlich are two-parameter adsorption isotherms that are widely used in cEOR studies. The Langmuir isotherm is applied to monolayer adsorption on homogeneous sites, whereas the Freundlich isotherm suites are applied to multilayer adsorption on heterogeneous sites. Some more complex adsorption isotherms are also discussed in this paper, such as Redlich-Peterson and Sips isotherms, both involve three parameters. This paper will help select and apply a suitable adsorption isotherm to experimental data.

Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network

The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils' complex structure and components. In the present study, the influences of different parameters, including silt content (m_si), sand content (m_sa), clay content (m_cl), quartz content (m_qu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters ofm_si,m_sa,m_cl, andm_quhave a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy andR²= 0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.

Effects of Micellization Behavior on the Interfacial Adsorption in Binary Anionic/Nonionic Surfactant Systems: A Molecular Simulation Study

A surfactant interfacial adsorption process is highly associated with its micellization behaviors in the water phase, which is of great fundamental and practical significance in enhanced oil recovery. In this paper, the typical anionic surfactant 1-dodecanesulfonic acid sodium (DAS) and nonionic surfactants octylphenol polyoxyethylene ether-n (OP-n, n = 1, 5, and 10) are introduced to investigate their micellization behavior and interfacial adsorption process via molecular dynamics simulation. Number density profiles reveal that the additional OP5 molecules in the water phase generate the mixed micelle with DAS molecules and greatly promote its interfacial adsorption. Interaction energy calculation is employed to confirm the interaction of anionic/nonionic surfactants in the mixed micelle. Then, the radial distribution function, solvent-accessible surface area, and solvation free energy are calculated to further explore and verify the adsorption mechanism of the mixed micelle. It is found that the nonionic surfactant obviously decreases the hydrophilicity of the mixed micelle in the water phase, which should be responsible for its intensive tendency of the interfacial adsorption.

Ultrasensitive Sensing of Volatile Organic Compounds Using a Cu-Doped SnO2 -NiO p-n Heterostructure That Shows Significant Raman Enhancement*

Surface enhanced Raman scattering (SERS) based on chemical mechanism (CM) attracts tremendous attention for great selectivity and stability. However, low enhancement factor (EF) limits its practical applications for trace detection. Here, a novel sponge-like Cu-doping SnO₂ -NiO p-n semiconductor heterostructure (SnO₂ -NiO_x /Cu), was first created as a CM-based SERS substrate with a significant EF of 1.46×10¹⁰ . This remarkable EF was mainly attributed to the enhanced charge-separation efficacy of p-n heterojunction and charge transfer resonance resulted from Cu doping. Moreover, the porous structure enriched the probe molecules, resulting in further SERS signals magnification. By immobilizing CuPc as an inner-reference element, SnO₂ -NiO_x /Cu was developed as a SERS nose for selective recognition of multiple lung cancer related VOCs down to ppb level. The information of VOCs was recorded in a barcode, demonstrating practical potential of a desktop SERS device for biomarker screening.

Application of statistical modeling for the production of highly pure rhamnolipids using magnetic biocatalysts: Evaluating its efficiency as a bioremediation agent

In the present study, highly pure rhamnolipids (RLs) was produced using biocatalysts immobilized on amino-functionalized chitosan coated magnetic nanoparticles. Upon immobilizing naringinase and Candida antarctica lipase B (CaLB) under the optimized conditions, an enhanced operational stability with biocatalytic loads of 935 ± 2.4 U/g (naringinase) and 825 ± 4.1 U/g (CaLB) were achieved. Subsequently, the immobilized biocatalysts were utilized sequentially in a two-step RLs synthesis process. The key parameters involved in RLs production were optimized using artificial neural network (ANN) coupled genetic algorithm (GA) and were compared with composite central design (CCD). On validating the efficiency of both models, mean square errors of 1.58% (CCD) and 1.04% (ANN) were obtained. Optimization of parameters by ANN-GA resulted in 1.2-fold increase in experimental RLs yield (80.53%), which was 1.05-fold higher when compared to CCD model. Further, to establish the efficiency of RLs as a bioremediation agent, it was utilized as washing agent. It was observed that at a soil to RLs volume of 1:05, RLs concentration of 0.4 mg/mL, a 95.35 ± 1.33% removal of Total Petroleum Hydrocarbons (TPHs) was obtained at 35 ℃ and 160 rpm in 75 min. Thus, this strategy provides an efficient biocatalytic toolbox for RLs synthesis, which can be effectively used as a bioremediation agent.