Algal-derived biochar as an efficient adsorbent for removal of Cr (VI) in textile industry wastewater: Non-linear isotherm, kinetics and ANN studies

A Schiff base-functionalized chitosan magnetic bio-nanocomposite for efficient removal of Pb (II) and Cd (II) ions from aqueous solutions

The rapid industrialization and human activities in catchments have posed notable global challenges in removing of heavy metal contaminants from wastewater. Here, Schiff-bases (SB) of cyanoguanidine (CG) and salicylaldehyde (SA) were covalently grafted on a magnetic nanocomposite of chitosan to form a hybrid magnetic nanostructure (Fe3O4@CS-CGSB). The synthesized structure was characterized using various techniques such as Fourier transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (SEM), transmission electron microscopy (TEM), powder X-ray diffraction (XRD), thermogravimetric analysis (TGA), vibrating sample magnetometry (VSM), dynamic light scattering (DLS), zeta potential, and Brunauer-Emmett-Teller surface area analysis (BET). The prepared adsorbent demonstrated strong binding capabilities and high efficiency in adsorbing Pb(II) and Cd(II) metal ions from aqueous solutions with removal efficiencies of 98 % and 97 %, respectively. The study investigated various factors such as pH, adsorbate concentration, adsorbent dosage, isotherms, kinetics, and adsorption mechanism. The heavy metal ions were adsorbed through coordination with the nitrogen and hydroxyl groups of the nanostructure, as well as electrostatic interactions. The adsorption process followed the Freundlich isotherm with a high correlation coefficient (R2 = 0.97, 0.96) and a pseudo-second-order kinetic model. The Fe3O4@CS-CGSB is highly effective in removing heavy metal ions with maximum adsorption capacities of 394 mg/g for Pb(II) and 391 mg/g for Cd(II). The recycled hybrid nanostructure was dried and subjected to various adsorption-desorption tests, revealing a desorption efficiency of 98 %. In conclusion, the synthesized magnetic bio-sorbent shows great promise in effectively removing heavy metal ions from water and wastewater.

Facile fabrication of novel magnetic chitosan@Ag-MWCN nanocomposite for the adsorptive removal of ciprofloxacin from aqueous solutions

Pharmaceuticals are known as challenging class of water pollutants that threaten worldwide waterbodies. Even in negligible concentrations, antibiotics could lead to the development of antibiotic resistance genes. In environmental and health protection against antibiotics, adsorption is a promising technique, and designing effective, sustainable, and non-toxic adsorbents is crucial. Herein, a magnetic chitosan@Ag-multiwalled carbon nanotube nanocomposite (MC@Ag-MWCN) was synthesized and applied to eliminate a common antibiotic ciprofloxacin (CIP) from aqueous solutions. FESEM, TEM, XRD and FTIR, techniques characterized the as-synthesized MC@Ag-MWCN. The study evaluates the efficacy of the various key factors such as pH, varied initial CIP concentrations, nanocomposite doses and contact time in CIP uptake. Experimental equilibrium and kinetic data were analyzed utilizing four commonly used isotherm models: Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich. Notably, the Langmuir isotherm model fitted best to CIP removal data by MC@Ag-MWCN with a qe of 31.26 mg/g. Also, the adsorption data correlated well with the pseudo-second-order kinetics model. Adsorption mechanism the removal of CIP using the MC@Ag-MWCN nanocomposite occurs through a combination of physical and chemical interactions, facilitated by the composite's structural and chemical properties. In conclusion, MC@Ag-MWCN shows promising adsorptive characteristics against recalcitrant antibiotic CIP.

Cd adsorption prediction of Fe mono/composite modified biochar based on machine learning: Application for controllable preparation

In this study, artificial neural network (ANN) and random forest (RF) were constructed to predict the Cd adsorption capacity of Fe-modified biochar. The RF model outperformed ANN model in accuracy and predictive performance (R2 = 0.98). Through the contribution factors analysis of SHAP, structural characteristics (55.44%) were most important of Fe composite-modified biochar (CBC). And CBC have the best adsorption performance when C, Fe, O, H, N, and pH content were <50%, 10-20%, 10-20%, 0.5-1%, 0-2%, and >10, respectively. The Fe-Ca modified biochar (FeCa-BC) of different raw materials (wheat straw, corn straw and walnut shell) were successfully prepared according to the ML results, and the experimental data of FeCa-BC verified the accurate predictive ability of RF model (R2 = 0.89). The developed GUI toolbox results showed that the error between predicted and actual values was less than 5% based on the training set, testing set, and experimental validation set. The analysis of FTIR, XRD and XPS indicated that surface complexation, precipitation, and ion exchange were the main Cd adsorption mechanisms of FeCa-BC. This work presents new insights for the targeted preparation of functional biochar and its application in contaminated water through ML.

Applicability analysis of algae biochar for anaerobic membrane bioreactors in wastewater treatment: A review from a sustainability assessment perspective

The incorporation of biochar can significantly enhance the performance of anaerobic membrane bioreactors (AnMBRs), achieving up to a 95 % increase in pollutant removal efficiency and an 86 % improvement in methane production. Algae biochar, in particular, shows great promise as an effective additive in AnMBR systems because of its low cost (approximately $0.470/kg) and the abundance of raw material sources. This paper presents a comprehensive applicability analysis of algae biochar-AnMBRs from a sustainability assessment perspective, addressing technical, environmental, economic, and social dimensions. Key technical benefits include a reduction in membrane fouling by 92.1 % and an enhancement of energy recovery by 58.7 % compared to conventional AnMBRs. Following this, the paper evaluates algae biochar-AnMBRs from environmental, economic, and social viewpoints to emphasize the practical applicability and potential of this process. Finally, this review addresses the limitations related to the full-scale implementation of this technology and proposes strategic approaches to overcome these challenges. Overall, the review provides valuable insights into the practical application of algae biochar-AnMBR systems, with a strong focus on sustainability.

Comprehensive insights into the application of graphene-based aerogels for metals removal from aqueous media: Surface chemistry, mechanisms, and key features

Efficient removal of heavy metals and other toxic metal pollutants from wastewater is essential to protect human health and the surrounding vulnerable ecosystems. Therefore, significant efforts have been invested in developing practical and sustainable tools to address this issue, including high-performance adsorbents. In this respect, within the last few years, graphene-based aerogels/xerogels/cryogels (GBAs) have emerged and drawn significant attention as excellent materials for removing and recovering harmful and valuable metals from different aqueous media. Such an upward trend is mainly due to the features of the aerogel materials combined with the properties of the graphene derivatives within the aerogel's network, including the GBAs' unique three-dimensional (3D) porous structure, high porosity, low density, large specific surface area, exceptional electron mobility, adjustable and rich surface chemistry, remarkable mechanical features, and tremendous stability. This review offers a comprehensive analysis of the fundamental and practical aspects and phenomena related to the application of GBAs for metals removal. Herein, we cover all types of (bottom-up) synthesized GBAs, including true microporous graphene-based aerogels as well as other 3D graphene-based open-cell interconnected mesoporous and macroporous aerogels, foams, and sponges. Indeed, we provide insights into the fundamental understanding of the GBAs' suitability for such an important application by revealing the mechanisms involved in metals removal and the factors inducing and controlling the highly selective behavior of these distinctive adsorbents. Besides conventional adsorptive pathways, we critically analyzed the ability of GBAs to electrochemically capture metal pollutants (i.e., electrosorption) as well as their efficiency in metals detoxification through reductive mechanisms (i.e., adsorption-reduction-readsorption). We also covered the reusability aspect of graphene aerogels (GAs)-based adsorbents, which is strongly linked to the GBAs' outstanding stability and efficient desorption of captured metals. Furthermore, in view of their numerous practical and environmental benefits, the development and application of magnetically recoverable GAs for metals removal is also highlighted. Moreover, we shed light on the potential practical and scalable implementation of GBAs by evaluating their performance in continuous metals removal processes while highlighting the GBAs' versatility demonstrated by their ability to remove multiple contaminants along with metal pollutants from wastewater media. Finally, this review provides readers with an accessible overview and critical discussion of major recent achievements regarding the development and applications of GAs-based adsorbents for metal ions removal. Along with our recommendations and suggestions for potential future work and new research directions and opportunities, this review aims to serve as a valuable resource for researchers in the field of wastewater treatment and inspire further progress towards developing next-generation high-performance GBAs and expanding their application.

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Leveraging plant-based remediation technologies against chromite mining toxicity

The release of hazardous hexavalent chromium from chromite mining seriously threatens habitats and human health by contaminating water, air, and soil. Vulnerability to hexavalent chromium can result in significant health risks, viz, respiratory issues, gastrointestinal illnesses, skin problems in humans, and a plethora of toxic effects in animals. Moreover, Cr(VI) toxicity can adversely affect plant physiology by inhibiting seed germination, nutrient uptake, cell division, and root development, ultimately impairing growth and vitality. Fortunately, innovative techniques such as phytoremediation and nanotechnology have been developed to address heavy metal contamination, offering a promising solution, mainly through the use of hyperaccumulating plants. Biochar derived from plant waste is widely used and is emerging as a sustainable strategy for remediating Cr(VI) contamination. Biochar is rich in carbon and highly influential in removing Cr(VI) from contaminated soils. This approach addresses immediate challenges while providing a sustainable pathway for environmental rehabilitation in chromium mining. Integrating innovative technologies with nature-based solutions offers a holistic approach to reducing the harmful effects of chromium mining, thus protecting both human well-being and ecosystems. This review highlights the impact of Cr(VI) on different living biotas and further emphasizes the use of plants and plant-based materials for the sustainable remediation of chromite mining regions.

Adsorptive removal of toxic heavy metals from wastewater using water hyacinth and its biochar: A review

Heavy metal contamination in aquatic ecosystems worsens due to rapid industrial expansion. Biochar, an efficient and economical adsorbent, has attracted much interest in environmental science, particularly in removing heavy metals (HMs). The paper covers basic details on biochar, its preparation, and potential chemical and inorganic modifications. Possible adsorption mechanisms of HMs on biochar, which include electrostatic attraction, ion exchange, surface complexation, chemical precipitation, and hydrogen bonding, are also discussed. These mechanisms are affected by the type of biochar used and the species of HMs present. Research findings suggest that while biochar effectively removes HMs, modifications to the carbon-rich hybrid can enhance surface properties such as surface area, pore size, functional groups, etc., and magnetic properties in a few cases, making them more efficient in HM removal. The choice of feedstock materials is one of the key parameters influencing the sorption capacity of biochars. This review aims to investigate the use of various forms of water hyacinth (WH), including aquatic plants, biomass, biochar, and modified biochar, as effective adsorbents for removing HMs from aqueous solutions and industrial effluents through a comparative analysis of their adsorption processes. However, further studies on the diverse effects of functional groups of modified biochar on HMs adsorption are necessary for future research.

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g-1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin-Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R2) and root mean square error (RMSE), the optimal network configuration was identified as a 5-11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

Graphene Oxide Covalently Functionalized with 5-Methyl-1,3,4-thiadiazol-2-amine for pH-Sensitive Ga3+ Recovery in Aqueous Solutions

A novel graphene-based composite, 5-methyl-1,3,4-thiadiazol-2-amine (MTA) covalently functionalized graphene oxide (GO-MTA), was rationally developed and used for the selective sorption of Ga3+ from aqueous solutions, showing a higher adsorption capacity (48.20 mg g-1) toward Ga3+ than In3+ (15.41 mg g-1) and Sc3+ (~0 mg g-1). The adsorption experiment's parameters, such as the contact time, temperature, initial Ga3+ concentration, solution pH, and desorption solvent, were investigated. Under optimized conditions, the GO-MTA composite displayed the highest adsorption capacity of 55.6 mg g-1 toward Ga3+. Moreover, a possible adsorption mechanism was proposed using various characterization methods, including scanning electron microscopy (SEM) equipped with X-ray energy-dispersive spectroscopy (EDS), elemental mapping analysis, Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). Ga3+ adsorption with the GO-MTA composite could be better described by the linear pseudo-second-order kinetic model (R2 = 0.962), suggesting that the rate-limiting step may be chemical sorption or chemisorption through the sharing or exchange of electrons between the adsorbent and the adsorbate. Importantly, the calculated qe value (55.066 mg g-1) is closer to the experimental result (55.60 mg g-1). The well-fitted linear Langmuir isothermal model (R2 = 0.972~0.997) confirmed that an interfacial monolayer and cooperative adsorption occur on a heterogeneous surface. The results showed that the GO-MTA composite might be a potential adsorbent for the enrichment and/or separation of Ga3+ at low or ultra-low concentrations in aqueous solutions.

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Qm) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R2 of 0.865 and 0.874 for K and Qm, respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Qm. An interactive platform was deployed for relevant scientists to predict K and Qm of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal.

Research advances on production and application of algal biochar in environmental remediation

Algae, comprising microalgae and macroalgae, have emerged as a promising feedstock for the production of functional biochar. Recently, the application of algal biochar in environmental remediation gains increasing attention. This review summarizes research advancements in the synthesis and application of algal biochar, a versatile and sustainable material for environmental remediation ranging from wastewater treatment to soil improvement. Algal biochar can be prepared by pyrolysis, microwave-assisted pyrolysis, and hydrothermal carbonization. Physical and chemical modifications have proven to be effective for improving biochar properties. Algal biochar is promising for removing diverse pollutants including heavy metals, organic pollutants, and microplastics. The role in soil improvement signifies a sustainable approach to enhancing soil structure, nutrient retention, and microbial activity. Research gaps are identified based on current understanding, necessitating further exploration into variations in biochar characteristics, the performance improvement, large-scale applications, and the long-term evaluation for environmental application. This review provides a better understanding of algal biochar as a sustainable and effective tool in environmental remediation.

Removal and reduction mechanism of Cr (VI) in Leersia hexandra Swartz constructed wetland-microbial fuel cell coupling system

Cr (VI) is extremely harmful to both the environment and human health, and it can linger in the environment for a very long period. In this research, the Leersia hexandra Swartz constructed wetland-microbial fuel cell (CW-MFC) system was constructed to purify Cr (VI) wastewater. By comparing with the constructed wetland (CW) system, the system electricity generation, pollutants removal, Cr enrichment, and morphological transformation of the system were discussed. The results demonstrated that the L. hexandra CW-MFC system promoted removal of pollutants and production of electricity of the system. The maximum voltage of the system was 499 mV, the COD and Cr (VI) removal efficiency was 93.73% and 97.00%. At the same time, it enhanced the substrate and L. hexandra ability to absorb Cr and change it morphologically transformation. Additionally, the results of XPS and XANES showed that the majority of the Cr in the L. hexandra and substrate was present as Cr (III). In the L. hexandra CW-MFC system, Geobacter also functioned as the primary metal catabolic reducing and electrogenic bacteria. As a result, L. hexandra CW-MFC system possesses the added benefit of removing Cr (VI) while producing energy compared to the traditional CW system.

Design of 2D/2D β-Ni(OH)2/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, β- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of β-Ni(OH)2. The FESEM and TEM micrographs showed that the β-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that β-Ni(0.1)/ZNs and β-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R2, SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degradation percent of TC. The results revealed that using fabricated 2D/2D p-n heterojunctions (β-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

Utilization of biochar for remediation of heavy metals in aqueous environments: A review and bibliometric analysis

Biochar usage for removing heavy metals from aqueous environments has emerged as a promising research area with significant environmental and economic benefits. Using the PICO approach, the research question aimed to explore using biochar to remove heavy metals from aqueous media. We merged the data from Scopus and the Web of Science Core Collection databases to acquire a comprehensive perspective of the subject. The PRISMA guidelines were applied to establish the search parameters, identify the appropriate articles, and collect the bibliographic information from the publications between 2010 and 2022. The bibliometric analysis showed that biochar-based heavy metal remediation is a research field with increasing scholarly attention. The removal of Cr(VI), Pb(II), Cd(II), and Cu(II) was the most studied among the heavy metals. We identified five main clusters centered on adsorption, water treatment, adsorption models, analytical techniques, and hydrothermal carbonization by performing keyword co-occurrence analysis. Trending topics include biochar reusability, modification, acid mine drainage (AMD), wastewater treatment, and hydrochar. The reutilization of heavy metal-loaded spent biochar includes transforming it into electrodes for supercapacitors or stable catalyst materials. This study provides a comprehensive overview of biochar-based heavy metal remediation in aquatic environments and highlights knowledge gaps and future research directions.

Synergistic effects of adsorption and chemical reduction towards the effective Cr(VI) removal in the presence of the sulfur-doped biochar material

In this study, the anaerobic sludge withdrawn from thickener in a sewage treatment plant served as the precursor for sludge-based biochar fabrication, which was further modified via sulfur (S) heteroatom doping (i.e., S-BC). The S atom doping resulted in the adjustment of the physicochemical properties towards the carbon material, endowment of abundant functional groups on biochar surface, and increasing the binding sites between biochar and Cr(VI). Compared to the primary biochar (i.e., biochar without heteroatomic doping, named BC), S-BC exhibited a rough surface and possessed remarkable advantages in ash content, specific surface area, and pore volume. The existence of graphene carbon crystal structure for S-BC was confirmed through S-BC by XRD and FTIR analysis. The studies of adsorption kinetics and isotherms showed that pseudo-second-order kinetics and the Langmuir model more fitted the Cr(VI) removal behavior in the presence of S-BC. Therefore, the chemisorption and monolayer adsorption were the primary mechanisms involved in the Cr(VI) removal process. Additionally, XPS analysis results illustrated the aqueous Cr(VI) was efficiently eliminated through the synergistic effect of chemisorption and reduction to Cr(III) in the presence of S-BC. Moreover, S-BC could still achieve the Cr(VI) eliminating efficiency of 85.31% undergoing five cycles with unchanged functional group and crystal structure via FTIR and XRD analysis. Thus, the results of this study may shed light on a new approach for simultaneous economical sludge disposal and the sustainable remediation of the Cr(VI)-contaminated wastewater.

Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor

Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.

Tailoring the topology of ZIF-67 metal-organic frameworks (MOFs) adsorbents to capture humic acids

Chlorination is a versatile technique to combat water-borne pathogens. Over the last years, there has been continued research interest to abate the formation of chlorinated disinfection by-products (DBPs). To prevent hazardous DBPs in drinking water, it is decided to diminish organic precursors, among which humic acids (HA) resulting from the decomposition and transformation of biomass. Metal-organic frameworks (MOFs) such as zeolitic imidazolate frameworks (ZIFs) have recently received tremendous attention in water purification. Herein, customized ZIF-67 MOFs possessing various physicochemical properties were prepared by changing the cobalt source. The HA removal by ZIF-67-Cl, ZIF-67-OAc, ZIF-67-NO3, and ZIF-67-SO4 were 85.6%, 68.9%, 86.1%, and 87.4%, respectively, evidently affected by the specific surface area. HA uptake by ZIF-67-SO4 indicated a removal efficiency beyond 90% in 4  90% after 60 min mixing the solution with 0.3 g L-1 ZIF-67-SO4. Notably, an acceptable removal performance (∼72.3%) was obtained even at HA concentrations up to 100 mg L-1. The equilibrium data fitted well with the isotherm models in the order of Langmuir> Hill > BET> Khan > Redlich-Peterson> Jovanovic> Freundlich > and Temkin. The maximum adsorption capacity qm for HA uptake by ZIF-67-SO4 was 175.89 mg g-1, well above the majority of adsorbents. The pseudo-first-order model described the rate of HA adsorption by time. In conclusion, ZIF-67-SO4 presented promising adsorptive properties against HA. Further studies would be needed to minimize cobalt leaching from the ZIF-67-SO4 structure and improve its reusability safely, to ensure its effectiveness and the economy of adsorption system.

Microalgae-based biochar production and applications: A comprehensive review

Biochar, a solid carbonaceous substance synthesized from the thermochemical degradation of biomass, holds significant potential in addressing global challenges such as soil degradation, environmental pollution, and climate change. Its potential as a carbon sequestration agent, together with its versatile applications in soil amendments, pollutant adsorption, and biofuel production, has garnered attention. On the other hand, microalgae, with their outstanding photosynthetic efficiency, adaptability, and ability to accumulate carbohydrates and lipids, have demonstrated potential as emerging feedstock for biochar production. However, despite the significant potential of microalgal biochar, our current understanding of its various aspects, such as the influence of parameters, chemical modifications, and applications, remains limited. Therefore, this review aims to provide a comprehensive analysis of microalgae-based biochar, covering topics such as production techniques, pollutant removal, catalytic applications, soil amendments, and synthesis of carbon quantum dots to bridge the existing knowledge gap in this field.