Transition Metal Catalysts for Atmospheric Heavy Metal Removal: A Review of Current Innovations and Advances

Atmospheric heavy metal pollution presents a severe threat to public health and environmental stability. Transition metal catalysts have emerged as a potent solution for the selective capture and removal of these pollutants. This review provides a comprehensive summary of current advancements in the field, emphasizing the efficiency and specificity of nanostructured transition metals, including manganese, iron, cobalt, nickel, copper, and zinc. Looking forward, we delve into the prospective trajectory of catalyst development, underscoring the need for materials with enhanced stability, regenerability, and environmental compatibility. We project that advancements in computational materials science, nanotechnology, and green chemistry will be pivotal in discovering innovative catalysts that are economically and environmentally sustainable. The integration of smart technologies for real-time monitoring and adaptive control is anticipated to revolutionize heavy metal remediation, ensuring efficient and responsive pollution abatement strategies in the face of evolving industrial scenarios and regulatory landscapes.

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.

A review of synthesis, fabrication, and emerging biomedical applications of metal-organic frameworks

The overwhelming potential of porous coordination polymers (PCP), also known as Metal-Organic Frameworks (MOFs), especially their nanostructures for various biomedical applications, have made these materials worth investigating for more applications and uses. MOFs unique structure has enabled them for most applications, particularly in biomedical and healthcare. A number of very informative review papers are available on the biomedical applications of MOFs for the reader's convenience. However, many of those reviews focus mainly on drug delivery applications, and no significant work has been reported on other MOFs for biomedical applications. This review aims to present a compact and highly informative global assessment of the recent developments in biomedical applications (excluding drug-delivery) of MOFs along with critical analysis. Researchers have recently adopted both synthetic and post-synthetic routes for the fabrication and modification of MOFs that have been discussed and analyzed. A critical review of the latest reports on the significant and exotic area of bio-sensing capabilities and applications of MOFs has been given in this study. In addition, other essential applications of MOFs, including photothermal therapy, photodynamic therapy, and antimicrobial activities, are also included. These recently grown emergent techniques and cancer treatment options have gained attention and require further investigations to achieve fruitful outcomes. MOF's role in these applications has been thoroughly discussed, along with future challenges and valuable suggestions for the research community that will help meet future demands.