Treatment of heavy metal-polluted sewage sludge using biochar amendments and vermistabilization

Removal of environmental pollutants using biochar: current status and emerging opportunities

In recent times, biochar has emerged as a novel approach for environmental remediation due to its exceptional adsorption capacity, attributed to its porous structure formed by the pyrolysis of biomass at elevated temperatures in oxygen-restricted conditions. This characteristic has driven its widespread use in environmental remediation to remove pollutants. When biochar is introduced into ecosystems, it usually changes the makeup of microbial communities by offering a favorable habitat. Its porous structure creates a protective environment that shields them from external pressures. Consequently, microorganisms adhering to biochar surfaces exhibit increased resilience to environmental conditions, thereby enhancing their capacity to degrade pollutants. During this process, pollutants are broken down into smaller molecules through the collaborative efforts of biochar surface groups and microorganisms. Biochar is also often used in conjunction with composting techniques to enhance compost quality by improving aeration and serving as a carrier for slow-release fertilizers. The utilization of biochar to support sustainable agricultural practices and combat environmental contamination is a prominent area of current research. This study aims to examine the beneficial impacts of biochar application on the absorption and breakdown of contaminants in environmental and agricultural settings, offering insights into its optimization for enhanced efficacy.

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.

Vermiremediation of heavy metals (HMs)-contaminated agricultural land: synergistic changes in soil enzyme activities and earthworm's growth parameters

In this investigation the heavy metals (Cu, Zn, Mn, Cr and Ni) remediation potential of Eisenia fetida was studied in the crude oil polluted soil. The potential of E. fetida was evaluated based on the decrease in concentrations of Cu, Zn, Mn, Cr and Ni, and improvement in the soil enzyme activities at the end of 90 days of experimental trials. Moreover, soil health quality, inter-relationship between the enzyme activities and the growth parameters of E. fetida and synergistic relation among the enzyme activities were also evaluated through G-Mean and T-QSI indices, chord plot analysis and principal component analysis (PCA) to confirm the performance of E. fetida during vermiremediation. The results revealed that the soil treated with E. fetida showed a reduction in the concentration of Cu, Zn, Mn, Cr and Ni by 17.4% 19.45%, 9.44%, 23.8% and 9.6% respectively by end of the experimental trials. The cellulase, amylase, polyphenol oxidase, peroxidase, urease, dehydrogenase and catalase activities in the E. fetida-treated soil were enhanced by 89.83%, 99.17%, 142%, 109.9%, 92.9%, 694.3% and 274.5% respectively. The results of SEM-EDS revealed enhancement in the O, K, Na, Mg and P content by 62.36%, 96.2%, 97.9%, 93.7% and 98.2% respectively by the end of the experimental trial. The G-Mean and T-QSI indices also confirmed the improvement in soil enzyme activities thereby indicating the positive influence of E. fetida on soil decontamination process. The chord plot indicated the interrelationship between the earthworm's growth parameters and enzyme activities of the soil as indicated by the high linkage between the nodes. Finally, the PCA confirmed the negative effect of the heavy metals on the soil enzyme activities and synergistic interrelationship between the enzyme activities during the vermiremediation process. Thus, this study demonstrated the changes in the soil enzyme activities and their interconnected influences during vermiremediation of crude oil sourced heavy metals from polluted soil.

State-of-art advances on removal, degradation and electrochemical monitoring of 4-aminophenol pollutants in real samples: A review

p_Aminophenol, namely 4-aminophenol (4-AP), is an aromatic compound including hydroxyl and amino groups contiguous together on the benzene ring, which are suitable chemically reactive, amphoteric, and alleviating agents in nature. Amino phenols are appropriate precursors for synthesizing oxazoles and oxazines. However, since the toxicity of aniline and phenol can harm human and herbal organs, it is essential to improve a reliable technique for the determination of even a trace amount of amino phenols, as well as elimination or (bio)degradation/photodegradation of it to protect both the environment and people's health. For this purpose, various analytical methods have been suggested up till now, including spectrophotometry, liquid chromatography, spectrofluorometric and capillary electrophoresis, etc. However, some drawbacks such as the requirement of complex instruments, high costs, not being portable, slow response time, low sensitivity, etc. prevent them to be employed in a wide range and swift in-situ applications. In this regard, besides the efforts such as (bio)degradation/photodegradation or removal of 4-AP pollutants from real samples, electroanalytical techniques have become a promising alternative for monitoring them with high sensitivity. In this review, it was aimed to emphasize and summarize the recent advances, challenges, and opportunities for removal, degradation, and electrochemical sensing 4-AP in real samples. Electroanalytical monitoring of amino phenols was reviewed in detail and explored the various types of electrochemical sensors applied for detecting and monitoring in real samples. Furthermore, the various technique of removal and degradation of 4-AP in industrial and urban wastes were also deliberated. Moreover, deep criticism of multifunctional nanomaterials to be utilized as a catalyst, adsorbent/biosorbent, and electroactive material for the fabrication of electrochemical sensors was covered along with their unique properties. Future perspectives and conclusions were also criticized to pave the way for further studies in the field of application of up-and-coming nanostructures in environmental applications.

Apoptotic, cytotoxic, antioxidant, and antibacterial activities of biosynthesized silver nanoparticles from nettle leaf

Here, we reported the biosynthesis of silver nanoparticles (AgNPs) using Urtica dioica (nettle) leaf extract as green reducing and capping agents and investigate their anticancer and antibacterial, activity. The Nettle-mediated biosynthesized AgNPs was characterized by UV-Vis a spectrophotometer. Their size, shape and elemental analysis were determined with the using of SEM and TEM. The crystal structure was determined by XRD and the biomolecules responsible for the reduction of Ag⁺ were determined using FTIR analysis. Nettle-mediated biosynthesis AgNPs indicated strong antibacterial activity against pathogenic microorganisms. Again, the antioxidant activity of AgNPs is quite high when compared to ascorbic acid. Anticancer effect of AgNPs, IC₅₀ dose was determined by XTT analysis using MCF-7 cell line and the IC₅₀ value was found to be 0.243 ± 0.014 μg/mL (% w/v).

A Review Unveiling Various Machine Learning Algorithms Adopted for Biohydrogen Productions from Microalgae

Biohydrogen production from microalgae is a potential alternative energy source that is now intensively being researched. The complex natures of the biological processes involved have afflicted the accuracy of traditional modelling and optimization, besides being costly. Accordingly, machine learning algorithms have been employed to overcome setbacks, as these approaches have the capability to predict nonlinear interactions and handle multivariate data from microalgal biohydrogen studies. Thus, the review focuses on revealing the recent applications of machine learning techniques in microalgal biohydrogen production. The working principles of random forests, artificial neural networks, support vector machines, and regression algorithms are covered. The applications of these techniques are analyzed and compared for their effectiveness, advantages and disadvantages in the relationship studies, classification of results, and prediction of microalgal hydrogen production. These techniques have shown great performance despite limited data sets that are complex and nonlinear. However, the current techniques are still susceptible to overfitting, which could potentially reduce prediction performance. These could be potentially resolved or mitigated by comparing the methods, should the input data be limited.