Random forest model for removal of bromophenol blue using activated carbon obtained from Astragalus bisulcatus tree

Optimization of aflatoxin B1 removal efficiency of DNA by resonance light scattering spectroscopy

In this paper, firstly, the resonance light scattering spectra of aflatoxin B₁ (AFB₁) and DNA were measured by resonance light scattering spectroscopy (RLS), and the DNA binding saturation value (DBSV) of AFB₁ was calculated from their spectral results. Then the interaction intensity between DNA and AFB₁ and the effects of some external factors on the interaction between DNA and AFB₁ were evaluated by corresponding DBSVs, so as to establish and optimize a way for removing AFB₁ by DNA. DBSV of AFB₁ was 2.04 at 30℃ and pH 7.40. However, after adding sodium ion, calcium ion, vitamin E, vitamin C and D-glucose, DBSV of AFB₁ was changed to 2.72, 3.17, 2.67, 1.68 and 1.33 respectively. Correspondingly, the removal efficiency of AFB₁ by DNA was changed from 90.05% to 93.25%, 95.48%, 93.08%, 82.36% and 78.90% respectively. These results indicated that the external factors had a significant impact on the interaction between DNA and AFB₁. Among them, some factors enhanced the interaction between DNA and AFB₁, while some factors weakened the interaction between DNA and AFB1. The change of these external factors led to the corresponding changes in DBSV and the removal efficiency of AFB₁. DBSV of AFB₁ could really be used as an index to evaluate the intensity of the interaction between DNA and AFB₁, and to optimize the removal efficiency of AFB₁ by DNA. The experimental data also showed that the adsorption of AFB₁ to DNA was consistent with the pseudo-second-order kinetic model and the Freundlich isothermal model, was an exothermic and spontaneous process. All these results will give good references for establishing and optimizing a way of AFB₁ removal via DNA intercalation.

Machine learning in the evaluation and prediction models of biochar application: A review

This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

Development of a New Eco-Friendly Copolymer Based on Chitosan for Enhanced Removal of Pb and Cd from Water

Worldwide, concerns about heavy metal contamination from manmade and natural sources have increased in recent decades. Metals released into the environment threaten human health, mostly due to their integration into the food chain and persistence. Nature offers a large range of materials with different functionalities, providing also a source of inspiration for scientists working in the field of material synthesis. In the current study, a new type of copolymer is introduced, which was synthesized for the first time by combining chitosan and poly(benzofurane-co-arylacetic acid), for use in the adsorption of toxic heavy metals. Such naturally derived materials can be easily and inexpensively synthesized and separated by simple filtration, thus becoming an attractive alternative solution for wastewater treatment. The new copolymer was investigated by solid-state nuclear magnetic resonance, thermogravimetric analysis, scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray photon electron microscopy. Flame atomic absorption spectrometry was utilized to measure heavy metal concentrations in the investigated samples. Equilibrium isotherms, kinetic 3D models, and artificial neural networks were applied to the experimental data to characterize the adsorption process. Additional adsorption experiments were performed using metal-contaminated water samples collected in two seasons (summer and winter) from two former mining areas in Romania (Roșia Montană and Novăț-Borșa). The results demonstrated high (51–97%) adsorption efficiency for Pb and excellent (95–100%) for Cd, afttr testing on stock solutions and contaminated water samples. The recyclability study of the copolymer indicated that the removal efficiency decreased to 89% for Pb and 58% for Cd after seven adsorption–desorption cycles.

Design, synthesis, and characterization of a novel Zn(II)-2-phenyl benzimidazole framework for the removal of organic dyes

A novel Zn (II) organic framework comprising 2-phenyl benzimidazole (ZPBIF-1) was synthesized by using a solvothermal method. The characterization of the synthesized MOF was performed utilizing XRD, SEM, FT-IR, ¹H-NMR, ¹³C-NMR, MS, XPS, TG/DTA, and N₂ sorption analysis. ZPBIF-1 was successfully utilized to remove Acid red 88, Basic Violet 14, Basic Blue 54, and Congo red dyes in aqueous solutions. In this study, some parameters, including adsorbent dosage, initial dye concentration, contact time, temperature, and pH, were examined. To evaluate the experimental data, Freundlich, Langmuir, Temkin, and Dubinin-Radushkevich isotherm models were used. In this case, Langmuir is the most suitable model. Several kinetic models, including First-order, pseudo-first-order, second-order, and Pseudo-second-order kinetic models, Elovich's, and Weber's intraparticle diffusion models, were utilized to comprehend the detailed adsorption process. According to the pseudo-second-order kinetic model, dye sorption kinetics is best described. In addition, thermodynamic parameters like enthalpy (ΔH°), Gibbs free energy (ΔG°), and entropy (ΔS°) were also achieved and analyzed. The experimental studies thus suggest that Zn (II) metal-organic framework based on 2-phenyl benzimidazole could be a promising candidate for eliminating dyes from aqueous solution. Hence, the experimental studies suggest that a Zn (II) metal-organic framework based on 2-phenylbenzimidazole could be a promising candidate for eliminating dyes from aqueous solution. The maximum adsorption capacity of ZPBIF-1 was 1666.66, 1250, 1000, and 1250 mg/g for Acid red 88, Basic violet 14, Basic blue 54, and Congo red dyes, respectively. Furthermore, this method was used to remove contaminant dyes from textile wastewater, and an acceptable result was obtained.

Parametric Study of Methyl Orange Removal Using Metal–Organic Frameworks Based on Factorial Experimental Design Analysis

Wastewater treatment plants (WWTPs) are one of the most energy-intensive industries. Every stage of wastewater treatment consumes energy, which is the primary contributor to WWTP costs. Adsorbents and process optimization are critical for energy savings. The removal of dyes from industrial wastewater by adsorption using commercially available adsorbents is inefficient. Metal–organic frameworks (MOFs) have outstanding properties that can improve separation performance over current commercial adsorbents, and thus, these materials represent a milestone in improving dye removal in water treatment methods. In this work, three types of metal–organic frameworks (Fe-BTC, Cu-BTC, and ZIF-8) have been investigated as prospective adsorbents for methyl orange removal from water in batch setups. The results showed that at 15 mg/L MO initial concentration and 100 mg dosage, Fe-BTC had the highest removal efficiency of 91%, followed by ZIF-8 (63%), and finally Cu-BTC (35%), which exhibited structural damage due to its instability in water. Fe-BTC maintained consistent adsorption capacity over a wide range of pH values. Furthermore, a 23 full factorial design analysis was implemented to evaluate the conditions for maximum MO-removal efficiency. The main effects, interaction effects, analysis of variance (ANOVA), and the Pareto chart were reported. The statistical analysis demonstrated that the MOF type was the most significant factor, followed by dosage and initial concentration. The analysis indicated that the type of MOF and dosage had a positive effect on the removal efficiency, while the initial concentration had a negative effect. The two-way and three-way interactions were also found to be significant.

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.

Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning

The science-informed design of 'green' carbonaceous materials (e.g., biochar and activated carbon) with high removal capacity of recalcitrant organic contaminants (e.g., pharmaceuticals and personal care products (PPCPs)) is indispensable for promoting sustainable wastewater treatment. In this study, machine learning (ML) incorporating PPCPs and biochar properties as well as adsorption conditions were applied to build adsorption prediction models and explore the contributions of various biochar's inherent properties to their PPCPs adsorption capacity. The results demonstrated that the models developed by detailed biochar properties (e.g., surface functionality and hierarchical porous structure) from advanced microscopic and spectroscopic techniques were more accurate (i.e., the root-mean-square error decreased by 18-24%) than those by general information such as bulk elemental composition and total pore volume. The relative importance of surface carbon functionalities ranked in the order of C-O bond > CO bond > non-polar carbon for predicting the adsorption capacity. According to the partial dependence analysis, the average pore diameters of adsorbents that were larger than the maximum diameter of PPCPs molecules by 1.5-fold to 2.5-fold favored the PPCPs adsorption. This study reveals new insights into the adsorption of PPCPs and provides a comprehensive reference for the sustainable engineering of biochar adsorbents for PPCPs wastewater treatment.

Application of random forest for modeling batch and continuous fixed-bed removal of crystal violet from aqueous solutions using Gypsophila aretioides stem-based biosorbent

In this work, the Gypsophila aretioides (GYP-A) stem is used as a biosorbent to remove crystal violet (CV) by the static and dynamic systems from aqueous solutions; the biosorbent is interesting in green chemistry and, on the other hand, cheaper than activated carbon and does not have the limitation of industrialization. The effects of different operating parameters such as pH(3-9), biosorbent dosage(0.4-1.8 mg/L), and initial concentration of CV(100-250 mg/L) and time for the batch method and the bed height, inlet CV concentration(75-250 mg/L), and flow rate(3-8) on the breakthrough curves for the continuous method is investigated. The result of CV adsorption onto GYP-A using the batch method indicates that the model fits Freundlich > Temkin > Langmuir > R-D, and R² equal 0.9953, 0.9847, 0.9161, 0.7909 were obtained for isotherm model, respectively. A pseudo-second-order model (R² = 0.9995-0.9997) is recommended to describe the adsorption kinetics. The Thomas and Yoon-Nelson models were analyzed to study the adsorption kinetics. The random forest model shows an excellent ability to predict the parameters involved in the CV adsorption process with appropriate accuracy and useable for large data, robust against noise; it can be very effective in selecting important variables.

Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants

A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters.

Habitat potential modelling and mapping of Teucrium polium using machine learning techniques

Determining suitable habitats is important for the successful management and conservation of plant and wildlife species. Teucrium polium L. is a wild plant species found in Iran. It is widely used to treat numerous health problems. The range of this plant is shrinking due to habitat destruction and overexploitation. Therefore, habitat suitability (HS) modeling is critical for conservation. HS modeling can also identify the key characteristics of habitats that support this species. This study models the habitats of T. polium using five data mining models: random forest (RF), flexible discriminant analysis (FDA), multivariate adaptive regression splines (MARS), support vector machine (SVM), and generalized linear model (GLM). A total of 119 T. poliumlocations were identified and mapped. According to the RF model, the most important factors describing T. polium habitat were elevation, soil texture, and mean annual rainfall. HS maps (HSMs) were prepared, and habitat suitability was classified as low, medium, high, or very high. The percentages of the study area assigned high or very high suitability ratings by each of the models were 44.62% for FDA, 43.75% for GLM, 43.12% for SVM, 38.91% for RF, 28.72% for MARS, and 39.16% for their ensemble. Although the six models were reasonably accurate, the ensemble model had the highest AUC value, demonstrating a strong predictive performance. The rank order of the other models in this regard is RF, MARS, SVM, FDA, and GLM. HSMs can provide useful output to support the sustainable management of rangelands, reclamation, and land protection.

A Study on Machine Learning Methods' Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon

The adsorption of dyes using 39 adsorbents (16 kinds of agro-wastes) were modeled using random forest (RF), decision tree (DT), and gradient boosting (GB) models based on 350 sets of adsorption experimental data. In addition, the correlation between variables and their importance was applied. After comprehensive feature selection analysis, five important variables were selected from nine variables. The RF with the highest accuracy (R² = 0.9) was selected as the best model for prediction of adsorption capacity of agro-waste using the five selected variables. The results suggested that agro-waste characteristics (pore volume, surface area, agro-waste pH, and particle size) accounted for 50.7% contribution for adsorption efficiency. The pore volume and surface area are the most important influencing variables among the agro-waste characteristics, while the role of particle size was inconspicuous. The accurate ability of the developed models’ prediction could significantly reduce experimental screening efforts, such as predicting the dye removal efficiency of agro-waste activated carbon according to agro-waste characteristics. The relative importance of variables could provide a right direction for better treatments of dyes in the real wastewater.

Engineered Fe3 triangle for the rapid and selective removal of aromatic cationic pollutants: complexity is not a necessity

In this study, a low-cost oxo-bridged {Fe3} triangular cluster was constructed based on a benzoate ligand via slow evaporation. The cluster was thoroughly characterized by FTIR and UV-visible spectroscopy, TGA, and PXRD, and the exact structure was elucidated by single-crystal XRD. The formation of C-H⋯π and π-π interactions is responsible for the extra stability of {Fe3} clusters, which further enhances the dye adsorption property. The dye adsorption experiments performed on cationic [methylene blue (MB) and rhodamine-B (Rh-B)] as well as anionic [methyl orange (MO) and congo red (CR)] dyes revealed the ultimate selectivity of the present cluster towards the cationic ones. The {Fe3} cluster exclusively adsorbs the cationic dyes, i.e., MB and Rh-B even in the presence of anionic dyes, i.e., CR and MO. The extra stability, reusability and high efficiency of the {Fe3} molecular ensemble make it an attractive and fascinating material of importance. The kinetics analysis was evaluated employing different kinetics models. Furthermore, the plausible adsorption mechanism was also proposed, which suggests the interplay of cation-π and π-π interactions consolidating the efficient adsorption. Thus, the present work opens new doors for coordination chemists to further tune the structural features to modulate the adsorption/separation capacities of simple low-cost clusters for environmental protection for future efforts.

Biogenic and chemically synthesized Solanum tuberosum peel-silver nanoparticle hybrid for the ultrasonic aided adsorption of bromophenol blue dye

This work was aimed at the synthesis of a hybrid (STpe-AgNP), obtained by impregnation of silver nanoparticles (AgNP) onto Solanum tuberosum peel (STpe), for the ultrasonic assisted adsorption of bromophenol blue (BB) dye. SEM, FTIR, XRD, EDX, TGA and BET techniques were used to characterize the adsorbents. The XRD, SEM and EDX confirmed successful impregnation of AgNPs onto STpe to form the hybrid. The AgNPs impregnated onto the hybrid were found to be water stable at various pH values of 2.0-9.0. Chi-square (χ2 < 0.024) and linear regression (R2 > 0.996) showed that the Freundlich model was best fitted among the isotherm models, corroborated by the oriented site model. Kinetic analysis conformed to the intraparticle diffusion and pseudo-first-order rate equations, while thermodynamics displayed a physical, spontaneous and endothermic adsorption process. The presence of competing Pb(II), Ni(II), Cd(II) and Zn(II) metal ions in solution interfered with the adsorption of BB onto the biosorbents. In terms of reusability, STpe and STpe-AgNP showed BB desorption of 91.3% and 88.5% respectively, using NaOH as eluent. Ultra-sonication significantly enhanced the adsorption of BB by both adsorbents, but the impregnation of AgNPs only slightly improved adsorption of the dye from the simulated wastewater. This study also illustrated that pristine STpe biomass waste is a cheap viable option for the decontamination of BB from water.

A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent

The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R² of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.

Sustainable and low-cost Ocimum gratissimum for biosorption of indigo carmine dye: kinetics, isotherm, and thermodynamic studies

In the quest for a sustainable environment and clean water resources, the efficacy of Ocimum gratissimum leave (OGL) for indigo carmine (IC) dye biosorption was studied in a batch technique. The physicochemical properties of OGL supported its suitability for biosorption studies. Of 92.6% removal efficiency was achieved at optimum conditions of pH 2, contact time 120 min, initial IC concentration 500 ppm, temperature 298 K, and 100 mg OGL dose. Kinetic data were best fitted to pseudo second-order (PSO) and the mechanism was pore diffusion governed as validated by sum of square error (SSE) and non-linear chi-square (χ ²). Freundlich isotherm model gave the best description at 298 K as supported by Halsey, Redlich-Peterson, and Fowler-Guggenheim confirming the heterogeneous nature of OGL and multilayer biosorption process. Langmuir Q _max (77.52 mg g^-1) surpassed those previously reported. SEM and EDX confirmed the reality of the biosorption process. Thermodynamic parameters (ΔH°, ΔS°, ΔG°, and Ea) affirm a feasible, spontaneous, exothermic, and randomness of the process. Results revealed that OGL is a potential and efficient environmentally benign, low cost, and sustainable biosorbents. It is therefore recommended as a bi-functional biosorbent for wastewater treatment.

Carbazole-tagged pyridinic microporous network polymer for CO2 storage and organic dye removal from aqueous solution

A microporous organic polymer (Cz-pyr-P) was prepared from a monomer of pyridine-imides, flanked by four carbazoles, and its application as an adsorbent for both CO₂ and methylene blue dye in wastewater was investigated. The polymer was synthesised by oxidative polymerisation facilitated by FeCl₃ and comprehensively characterised using routine spectroscopic, thermal, textural, and morphological analyses. With a high surface area of 1065 m²/g and a median pore width of 8.06 Å, the nitrogen-enriched Cz-pyr-P reversibly adsorbed 19.41 wt% (273 K) and 12.78 wt% (295 K) CO₂ at 1 bar, with a good isosteric heat value of CO₂ adsorption (28.5 kJ/mol). For the removal of methylene blue dye from wastewater, Cz-pyr-P exhibited excellent partition coefficient of 380.10 mg/g μM with an equilibrium time of 6 min which is shorter than previously reported values for other materials. The results indicate that Cz-pyr-P with desirable functionality could be utilised for reaching CO₂ emission reduction targets as well as for wastewater treatment.

High adsorption of fulvic acid by amino modified styrene-type macroporous resin and evaluation of its mechanism

Amino-modified HPD 100 styrene-type macroporous resin (M-HPD 100) was successfully synthesized by the atom transfer radical polymerization process. The modified resin showed excellent performance in the degradation of fulvic acid (FA). FA removal was pH, temperature and flow velocity dependent. The adsorption data could be well interpreted by the Freundlich model. The maximum adsorption efficiency for M-HPD 100 obtained from the Freundlich model was 92.5% at 298 K, which was 37% higher than that of unmodified styrene-type macroporous resin (HPD 100). The adsorption process could be described by the pseudo-second-order kinetic model. The intra-particle diffusion and film diffusion were believed to be the rate-limiting process for both adsorbents. Thermodynamic parameters suggested it was a multi-layer physicochemical process. More importantly, although limited improvements were seen, the results of this study suggested that the surface of resin can be modified with functional groups to enhance the adsorption of FA from aqueous solution and may give other advantages; for example, despite the interference of the pore diffusion coefficient and other substances, M-HPD 100 has excellent regeneration capacity, and the adsorption and desorption efficiency was 74% and 64.28% respectively after six regenerations, which proved it has engineering application value.

The application of machine learning methods for prediction of metal sorption onto biochars

The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regression models were trained and optimized to predict the adsorption capacity according to biochar characteristics, metal sources, environmental conditions (e.g. temperature and pH), and the initial concentration ratio of metals to biochars. The RF model showed better accuracy and predictive performance for adsorption efficiency (R² = 0.973) than ANN model (R² = 0.948). The biochar characteristics were most significant for adsorption efficiency, in which the contribution of cation exchange capacity (CEC) and pH_H2O of biochars accounted for 66% in the biochar characteristics. However, surface area of the biochars provided only 2% of adsorption efficiency. Meanwhile, the models developed by RF had better generalization ability than ANN model. The accurate predicted ability of developed models could significantly reduce experiment workload such as predicting the removal efficiency of biochars for target metal according to biochar characteristics, so as to select more efficient biochar without increasing experimental times. The relative importance of variables could provide a right direction for better treatments of heavy metals in the real water and wastewater.

Assessing Habitat Suitability of Parasitic Plant Cistanche deserticola in Northwest China under Future Climate Scenarios

: Cistanche deserticola Ma, a perennial parasitic herb of family Orobanchaceae, is mainly parasitic on the roots of the Haloxylon ammodendron Bunge. In view of this special parasitic relationship, we applied random forest (RF) model to forecast potential geographic distribution, and developed a comprehensive habitat suitability model by integrating bioclimatic and soil factors to assess the suitable distribution of C. deserticola and H. ammodendron across China in 2050s and 2070s under RCP2.6, RCP4.5, and RCP8.5, respectively. We modeled the core potential geographic distribution of C. deserticola by overlaying the distribution of these two species, and analyzed the spatial distribution pattern and migration trend of C. deserticola by using the standard deviational ellipse. In addition, we evaluated the accuracy of RF model through three evaluation indexes, and analyzed the dominant climate factors. The results showed that the core potential distribution areas of C. deserticola are distributed in the Xinjiang Uygur Autonomous Region, the junction of Shaanxi–Gansu–Ningxia provinces, and the Inner Mongolia Autonomous Region. The spatial dispersion would intensify with the increasing of emission scenarios, and the geographical habitat is moving towards higher latitude. Among the three evaluation indexes, the area under the ROC curve (AUC) and True Skill Statistic (TSS) have better assessment results. The main bioclimatic factors affecting the distribution are min temperature of coldest month (Bio6), annual precipitation (Bio12), precipitation of wettest month (Bio13), precipitation of wettest quarter (Bio16), and precipitation of warmest quarter (Bio18), among which the importance of precipitation factors is greater than temperature factors. More importantly, the results of this study could provide some guidance for the improvement of desert forest system, the protection of endangered species and the further improvement of the ecological environment.

Brief bibliometric analysis of "ionic liquid" applications and its review as a substitute for common adsorbent modifier for the adsorption of organic pollutants

The importance of improving adsorbent's adsorption efficiency in organic pollutants has been reported by many researchers. Surfactant-based modified adsorbents were a tasteful choice. As a result, the use of surfactants as a modifier for removing organic pollutants has shown to play a very big role in enhancing the adsorption efficiency of different materials. Ionic liquids are receiving extensive interest as green multipurpose compounds, primarily as a replacement for traditional chemicals that are used in many chemical processes. This work gives a brief bibliometric analysis of application of ionic liquid from 1930 to 2017, documents were collected from Scopus database and keywords from the abstracts and titles were analyzed using VOSviewer software. Furthermore, the work presents a review of conventionally known surfactants and the recent likelihood of ionic liquids for modifying adsorbents for adsorption of organic pollutants. Over the period of years between 1930 and 2017, 13,144 documents were published on the application of ionic liquids. VOSviewer software further confirms that adsorption is one of the leading areas in applications of ionic liquids. Review also showed that ionic liquid is a good modifier of adsorbents.

Prunus cerasifera Ehrh. fabricated ZnO nano falcates and its photocatalytic and dose dependent in vitro bio-activity

Zinc oxide nano falcates of sickle shape have been synthesized fromPrunus cerasiferapomological extract as a reducing cum stabilizing agent via novel, biomimetic and non-toxic route. Zinc oxide nano falcates were analyzed via ultraviolet spectroscopy, Fourier transform infrared analysis, X-ray powder diffraction, scanning electron microscopy and atomic force microscopy. Highly stable zinc oxide nano falcates synthesized at 200°C and 400°C calcination temperatures expressed intense UV-vis peak at 398 nm. Phenolic and amino groups were revealed by FTIR in pomological extract. Wurtzite crystalline structure of zinc oxide nano falcates was confirmed by XRD with average crystal size of 4.93 nm. SEM sizes ranged between 72.11-120 nm and 56.57-107.70 nm, respectively and shown higher polydispersity levels for two calcination temperatures. Augmented photocatalytic degradation of methyl red and bromophenol blue under direct solar irradiance shown pseudo first order kinetics (R2= 0.99 and 0.96). Furthermore, biomedical and agriculturally important pathogenic strains i.e.,Xanthomanas axonopodispv.citriandPseudomonas syringae,Aspergillus niger,Aspergillus flavus,Aspergillus fumigatus,Aspergillus terreus,Penicillium chrysogenum,Fusarium solaniandLasiodiplodia theobromaewere remarkably inhibited. Enhanced photocatalytic and antimicrobial activity reveals zinc oxide nano falcates promising prospects in nano bioremediation of polluted water and conversion into green nano pesticides.

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200W) as an efficient method and characterized by FE-SEM, FTIR and N₂ adsorption-desorption isotherm. The effect of different parameters such as contact time (0-120min), initial metal ion concentration (25-200mg/L), temperature (298, 318 and 333K), amount of adsorbent (0.002-0.007g) and the solution's initial pH (1-7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R²>0.99). The maximum monolayer adsorption capacity (q_m) of NiO/ACNC was 1428.57mg/L. Thermodynamic parameters (ΔG°, ΔH° and ΔS°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R²>0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.

Synthesis of amine-modified zeolitic imidazolate framework-8, ultrasound-assisted dye removal and modeling

The present research is focused on the ultrasound assisted adsorption of Acid blue 92 (AB92) and Direct red 80 (DR80) as anionic dyes in single and binary systems onto zeolitic imidazolate framework (ZIF-8) functionalized with 3-Aminopropyltrimethoxysilane (APTES). Different techniques such as Fourier transform infrared (FTIR), scanning electron microscope (SEM), field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) and thermogravimetric analyses (TGA) were used to characterize the prepared adsorbent. The individual effects and possible interactions between the various parameters including adsorbent dosage, sonication time, initial dye concentrations and pH on dyes removal efficiency were investigated by response surface methodology (RSM). The optimized experimental conditions were fixed at adsorbent dosage 0.005g for AB92 and 0.01g for DR80, pH 2.1, sonication time 15min, and initial dyes concentration 15mgL^-1 to get maximum removal percentage (>95.0%). A reliable and intelligent model based on least-squares support vector machine (LS-SVM) was developed to predict dye removal efficiency. The root mean square error (RMSE) of 0.604, 0.734 and 1.549 with high determination coefficient (R²) of 0.999, 0.996 and 0.997 for AB92, DR80 and binary system, respectively, were able to predict and model the adsorption process. The presented model illustrates better performance in predicting dye removal efficiency compared to the kinetic models. The results showed that the adsorption process had better conformation with pseudo-second order model. The adsorption equilibrium data was investigated by Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich isotherm models and the data were well fitted by Langmuir model with maximum adsorption capacity of 633.4 and 500.2mgg^-1 for AB92 and DR80 dyes, respectively. APTES@ZIF-8 was regenerated and found to be reusable after four successive cycles without considerable loss in adsorption capacity.

Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review

Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO). Most of these papers are discussed. The further research needs in this field are suggested. These ANNs models are obtaining popularity as approaches, which can be successfully employed for the adsorption of dyes with acceptable accuracy.

Application of linear and non-linear methods for modeling removal efficiency of textile dyes from aqueous solutions using magnetic Fe3O4 impregnated onto walnut shell

The performance of the Nano-magnetite Fe₃O₄ impregnated onto walnut shell (Fe₃O₄-WNS), which possessed the adsorption features of walnut shell and the magnetic property of Fe₃O₄, was investigated for the elimination of the methyl violet and Rhodamine 6G from contaminated aqueous solutions. The effects of different experimental variables on the removal efficiency of the cited dyes were examined. Then these variables were used as the inputs to generate linear and non-linear models such as the multiple linear regression, random forest, and artificial neural network to predict the removal efficiency of these dye species at different experimental conditions. The validation studies of these models were performed using the test set, which was not present in the modeling procedure. It was found that ANN had a higher ability to predict the adsorption process under different experimental conditions, and could be applied for the development of an automated dye wastewater removal plant. Also the maximum adsorption capacity (q_max) indicated that the q_max value for Fe₃O₄-WNS for removal of cationic dyes was comparable or better than that for some reported adsorbents. Also it should be cited that exhausted Fe₃O₄-WNS was regenerated using dishwashing liquid, and reused for removal of the cited dye species from aqueous solutions.

Preparation of nanomaterials for the ultrasound-enhanced removal of Pb2+ ions and malachite green dye: Chemometric optimization and modeling

Copper oxide nanoparticle-loaded activated carbon (CuO-NP-AC) was synthesized and characterized using different techniques such as FE-SEM, XRD and FT-IR. It was successfully applied for the ultrasound-assisted simultaneous removal of Pb²⁺ ions and malachite green (MG) dye in binary system from aqueous solution. The effect of important parameters was modeled and optimized by artificial neural network (ANN) and response surface methodology (RSM). Maximum simultaneous removal percentages (>99.0%) were found at 25mgL^-1, 20mgL^-1, 0.02g, 5min and 6.0 corresponding to initial Pb²⁺ concentration, initial MG concentration, CuO-NP-AC amount, ultrasonication time and pH, respectively. The precision of the equation obtained by RSM was confirmed by the analysis of variance and calculation of correlation coefficient relating the predicted and the experimental values of ultrasound-assisted simultaneous removal of the analytes. A good agreement between experimental and predicted values was observed. A feed-forward neural network with a topology optimized by response surface methodology was successfully applied for the prediction of ultrasound-assisted simultaneous removal of Pb²⁺ ions and MG dye in binary system by CuO-NPs-AC. The number of hidden neurons, MSE, R², number of epochs and error histogram were chosen for ANN modeling. Then, Langmuir, Freundlich, Temkin and D-R isothermal models were applied for fitting the experimental data. It was found that the Langmuir model well describes the isotherm data with a maximum adsorption capacity of 98.328 and 87.719mgg^-1 for Pb²⁺ and MG, respectively. Kinetic studies at optimum condition showed that maximum Pb²⁺ and MG adsorption is achieved within 5min of the start of most experiments. The combination of pseudo-second-order rate equation and intraparticle diffusion model was applicable to explain the experimental data of ultrasound-assisted simultaneous removal of Pb²⁺ and MG at optimum condition obtained from RSM.