Metal-organic frameworks as advanced sorbents for oil/water separation

An Appraisal for Providing Charge Transfer (CT) Through Synthetic Porous Frameworks for their Semiconductor Applications

In recent years, there has been considerable focus on the development of charge transfer (CT) complex formation as a means to modify the band gaps of organic materials. In particular, CT complexes alternate layers of aromatic molecules with donor (D) and acceptor (A) properties to provide inherent electrical conductivity. In particular, the synthetic porous frameworks as attractive D-A components have been extensively studied in recent years in comparison to existing D-A materials. Therefore, in this work, the synthetic porous frameworks are classified into conjugated microporous polymers (CMPs), covalent organic frameworks (COFs), and metal-organic frameworks (MOFs) and compare high-quality materials for CT in semiconductors. This work updates the overview of the above porous frameworks for CT, starting with their early history regarding their semiconductor applications, and lists CT concepts and selected key developments in their CT complexes and CT composites. In addition, the network formation methods and their functionalization are discussed to provide access to a variety of potential applications. Furthermore, several theoretical investigations, efficiency improvement techniques, and a discussion of the electrical conductivity of the porous frameworks are also highlighted. Finally, a perspective of synthetic porous framework studies on CT performance is provided along with some comparisons.

A review of the prospects, efficacy and sustainability of nanotechnology-based approaches for oil spill remediation

Numerous marine oil spill incidents and their environmental catastrophe have raised the concern of the research community and environmental agencies on the topic of the offshore crude oil spill. The oil transport through oil tankers and pipelines has further aggravated the risk of the oil spill. This has led to the necessity to develop an effective, environment-friendly, versatile oil spill clean-up strategy. The current review article analyses various nanotechnology-based methods for marine oil spill clean-up, focusing on their recovery rate, reusability and cost. The authors weighed the three primary factors recovery, reusability and cost distinctively for the analysis based on their significance in various contexts. The findings and analysis suggest that magnetic nanomaterials and nano-sorbent have been the most effective nanotechnology-based marine oil spill remediation techniques, with the magnetic paper based on ultralong hydroxyapatite nanowires standing out with a recovery rate of over 99%. The chitosan-silica hybrid nano-sorbent and multi-wall carbon nanotubes are also promising options with high recovery rates of up to 95-98% and the ability to be reused multiple times. Although the photocatalytic biodegradation approach and the nano-dispersion method do not offer benefits for recovery or reusability, they can nevertheless help lessen the negative ecological effects of marine oil spills. Therefore, careful evaluation and selection of the most appropriate method for each marine oil spill situation is crucial. The current review article provides valuable insights into the current state of nanotechnology-based marine oil spill clean-up methods and their potential applications.

A review on the effectiveness of nanocomposites for the treatment and recovery of oil spill

The introduction of unintended oil spills into the marine ecosystem has a significant impact on aquatic life and raises important environmental concerns. The present review summarizes the recent studies where nanocomposites are applied to treat oil spills. The review deals with the techniques used to fabricate nanocomposites and identify the characteristics of nanocomposites beneficial for efficient recovery and treatment of oil spills. It classifies the nanocomposites into four categories, namely bio-based materials, polymeric materials, inorganic-inorganic nanocomposites, and carbon-based nanocomposites, and provides an insight into understanding the interactions of these nanocomposites with different types of oils. Among nanocomposites, bio-based nanocomposites are the most cost-effective and environmentally friendly. The grafting or modification of magnetic nanoparticles with polymers or other organic materials is preferred to avoid oxidation in wet conditions. The method of synthesizing magnetic nanocomposites and functionalization polymer is essential as it influences saturation magnetization. Notably, the inorganic polymer-based nanocomposite is very less developed and studied for oil spill treatment. Also, the review covers some practical considerations for treating oil spills with nanocomposites. Finally, some aspects of future developments are discussed. The terms "Environmentally friendly," "cost-effective," and "low cost" are often used, but most of the studies lack a critical analysis of the cost and environmental damage caused by chemical alteration techniques. However, the oil and gas industry will considerably benefit from the stimulation of ideas and scientific discoveries in this field.

Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks

Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R², RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.