Tourmaline enhanced methane yield via regulating microbial metabolic balance during anaerobic co-digestion of corn stover and cow manure

The concentration of radioisotopes (Potassium-40, Polonium-210, Radium-226, and Thorium-230) in fillet tissue carp fishes: A systematic review and probabilistic exposure assessment

Chemical contamination of seafood has become a global health concern. Carp fish is one of the most widely consumed globally, and several studies have been conducted on the contamination of carp fish with radioisotopes. In the current study, a meta-analysis and probabilistic exposure assessment regarding the Potassium-40 (40K), Polonium-210 (210Po), Radium-226 (226Ra), and Thorium-230 (230Th) in the fillet tissue of carp fish were performed. In this regard, Scopus and PubMed were screened to retrieve the associated citations with on the concentration of radioisotopes in the fillet tissue of carp fish until October 2021. The rank order of radioisotopes in fillet tissue carp fish was 40K (103.49 Bq kg-1) > 210Po (9.39 Bq kg-1) > 226Ra (0.62 Bq kg-1) > 230Th (0.39 Bq kg-1). The highest effective dose due to 210Po ingestion was observed in Spain (male; 4.44E-05 Sv y-1, female; 2.67E-06 Sv y-1); 40K (female, 5.07E-07 Sv y-1); 226Ra (male, 9.93E-09 Sv y-1). The mean of effective dose (ED) in the male and females in India due to ingestion of 230Th as result of carp fish consumption was (1.70E-06 Sv y-1) and (7.01E-08 Sv y-1), respectively. The probabilistic exposure assessment by the Monte Carlo simulation method revealed that consumers of fillet tissue carp fish content of radioisotopes are at a safe range (0.001 Sv y-1).

Thermally modified tourmaline enhances hydrogen production by influencing hydrolysis acidification in two stages during dark fermentation of corn stover

This study investigated the influence of thermally modified tourmaline (Tur) on hydrogen production during the dark fermentation of corn stover. Single-factor experimental results revealed influencing factors of particle size, mass, and temperature. Optimization of the experimental process was achieved using the Box-Behnken design, reaching optimum at conditions of 407 °C, 910-mesh, and 6.2 g. The principle analysis experiment showed that the Tur-enhanced group (Tur_En) amplified cumulative hydrogen production by elevating hydrogen production during the sugar-production stage. The Tur_En group's cumulative hydrogen production was measured at 396.2 ± 40.3 (mL/g VS), marking a 34.2% increase compared to the control group. Analysis of microbial diversity indicated that Firmicutes and Bacteroidota emerged as dominant colonies in both stages. Tur facilitated hydrogen production by stimulating the activity of Firmicutes. This study suggests a highly effective Tur-enhanced technology for hydrogen production from corn stover and elucidates the principles underpinning this method from two stages.

Spatial green growth in China: exploring the positive role of investment in the treatment of industrial pollution

The industrial sector of China is critical to the country's economic growth. On the other side, industrialisation has resulted in a high rate of emissions, pushing China to spend extensively on industrial pollution remediation. As a result, this study looks at the relationship between investment completed in the treatment of industrial pollution and economic development. Initially, the study used the global Moran's I test (Queen's contiguity matrix) to find spatial autocorrelation for the 'investment completed in the treatment of industrial pollution' factor, where the study found a positive association across Chinese provinces, and suggest the existence of spatial autocorrelation. Thereafter, a time-fixed effect spatial error model was used due to the lowest Akaike information criterion and Bayesian information criterion to analyse regional data of China from 1999 to 2018. The data reveal a positive association between investment completed in the treatment of industrial pollution and regional economic growth, both in the short and long term. Furthermore, the negative consequences of urban wages and foreign investment on investment completed in the treatment of industrial pollution are having the reverse effect on regional green development, necessitating ecologically friendly actions to mitigate the negative environmental effects of both. The results highlight the need for policymakers in other countries to review their plans for economic expansion and create environmentally friendly legislation. By implementing the Chinese green economic growth model, policymakers in industrially polluting nations can reduce industrial pollution and foster green growth in their nation.

Biogenic synthesis of reduced graphene oxide decorated with silver nanoparticles (rGO/Ag NPs) using table olive (olea europaea) for efficient and rapid catalytic reduction of organic pollutants

In this work, graphene oxide (GO) sheets were prepared via a facile electrochemical exfoliation of graphite in acidic medium and subsequent oxidation with potassium permanganate. The GO sheets were employed for preparation of reduced GO adorned with nanosized silver (rGO/Ag NPs) using green reduction of GO and Ag(I) via olive fruit extract as a reducing and immobilizing agent. The crystal phase, morphology, and nanostructure of the prepared catalyst were characterized by XRD, SEM, EDX, UV-Vis and Raman spectroscopy techniques. The as-prepared rGO/Ag NPs showed superior catalytic performance towards the complete reduction (up to 99%) of 4-nitrophenol (4-NPH) to 4-aminophenol (4-APH) and rhodamine B (RhB) to Leuco RhB within 180 s using NaBH₄ at ambient condition. The rate constant (k) values were found to be 0.021 and 0.022 s^-1 for 4-NPH and RhB reduction, respectively. In addition, the regenerated catalyst could be reused after seven cycles without losing any apparent catalytic efficiency. Accounting for the excellent catalytic capability, chemical stability and environment-friendly synthesis protocol, the rGO/Ag NPs has great potential working as a heterogeneous catalyst in the transforming harmful organic contaminants into less harmful or harmless compounds.

Efficient Catalytic Combustion of Cyclohexane over PdAg/Fe2O3 Catalysts under Low-Temperature Conditions: Establishing the Degradation Mechanism Using PTR-TOF-MS and in Situ DRIFTS

Cyclohexane, a typical volatile organic compound (VOC), poses high risks to the environment and humans. Herein, synthesized PdAg/Fe₂O₃ catalysts exhibited exceptional catalytic performance for cyclohexane combustion at lower temperatures (50% mineralization temperature (T₅₀) of 199 °C, 90% mineralization temperature (T₉₀) of 315 °C) than Pd/Fe₂O₃ (T₅₀ of 262 °C, T₉₀ of 335 °C) and Fe₂O₃ (T₅₀ of 305 °C, T₉₀ of 360 °C). In addition, PdAg/Fe₂O₃ displayed enhanced stability by alloying Ag with Pd. The redox and acidity of the PdAg/Fe₂O₃ were studied by XPS, H₂-TPR, and NH₃-TPD. In situ diffuse reflectance infrared Fourier transform spectroscopy and proton-transfer-reaction time-of-flight mass spectrometry were applied to identify the intermediates formed on the catalyst surface and in the tail gas during oxidation, respectively. Results suggested that loading PdAg onto Fe₂O₃ significantly enhanced the adsorption and activation of oxygen and cyclohexane, oxidative dehydrogenation of cyclohexane to benzene, and catalytic cracking of cyclohexane to olefins at low temperatures. This in-depth study will benefit the design and application of efficient catalysts for the effective combustion of VOCs at low temperatures.

Carbon-based photocatalysts for hydrogen production: A review

Future energy crises and environmental deterioration may only be avoided by converting solar energy into sustainable, safe, cost-effective, and environmentally friendly technologies such as water splitting. Many researchers and governments throughout the globe have stressed the imperative need for affordable, environmental benign, resistive to corrosion, and earth-abundant nanostructured photocatalysts. This has led scientists to look for a green and cost-effective way to generate energy. As a result, the significance of photo catalyst engineering and reactor design difficulties connected to the performance of the photocatalytic reactions, as well as the examination and analysis of photocatalyst behaviors for adaptable and cost effective H2 production, is emphasized and summarized. The carbon-based materials have an appealing band structure, strong chemical stability, is plentiful on Earth, and is relatively easy to produce, making them suitable for hydrogen production. As example, graphene oxide (GO) with the oxygenated functional groups and graphene and its counterparts, including Graphene quantum dots (GQDs), GO, reduce graphene oxide (rGO), have been demonstrated to be ideal nanocomposite materials due to their superior properties and distribution in matrix and CNTs with excellent electronic transmission efficiency, low cost, stability, and environmental friendly are a great alternative of electron mediators for photocatalytic devices to boost light absorptivity for efficient hydrogen generation but some of them have limited photocatalytic activity due to their low sunlight usage efficiency, therefore the numerous methods, such as doping ions, constructing heterostructure, and functionalizing carbon-based materials, have recently been proven to promote the photocatalytic activity of them. The pore structure of carbon material functions as an acceptor of photogenerated electrons, improved the photocatalyst's specific surface area. Generally low-dimensional carbon materials demonstrated immense promise as highly efficient, low-cost, and environmentally friendly catalysts for hydrogen generation as an energy source. This article reviews the recent research progress on carbon-based materials for hydrogen evolution for the first time. It commences with a quick overview of the present state of affairs and fundamental concepts of hydrogen production in carbon-based nanomaterials for use in this field. We anticipate that this study will inspire readers to expand the use of carbon-based materials in H₂ generation in a more environmentally friendly way.

Application of sulfur-coated magnetic carbon nanotubes for extraction of some polycyclic aromatic hydrocarbons from water resources

In the present work, novel sulfur-coated magnetic carbon nanotubes (MCNTs-S) material was fabricated by S coating on the MCNTs using a simple heating procedure. TGA, EDX, XRD, TEM, and VSM were employed to characterize the as-prepared composite. Using HPLC-UV system, the produced superparamagnetic sorbent was employed for the extraction and measurement of trace levels of five polycyclic aromatic hydrocarbons (PAHs) in environmental waters. The synergistic effect of the sulfur layer and CNTs substrate is primarily responsible for the remarkable extraction efficiency of the MCNTs-S sorbent towards PAHs. The experimental factors including MCNTs-S dosage, sorption time, elution solvent, ionic strength and solution pH were explored and optimized. Considering that the ionic strength and pH do not have any impact on the PAHs extraction, as a result, there is no need the unnecessary adjustment of the water samples. The linear dynamic ranges and detection limits under optimal conditions were in the range of 0.05-0.11 ng mL^-1 and 0.2-150 ng mL^-1, respectively. The analysis of PAHs in the real samples (sea water and river water) using this approach was successfully assessed with appropriate recovery values (94.6%-99.0%).

Improved feed forward with bald eagle search for conjunctive water management in deficit region

Due to increasing requirements on water resources and a lower recharge rate, the farming seasons are a vital season for the management of groundwater and surface water resource management. This condition necessitates the use of combined water distribution to meet the full water requirements. Analysis of existing surface water resources and related restrictions, this research suggested an algorithm for aquifer stabilization and fulfilling optimum water requirements. To manage the optimum withdrawals and the subsequent drop, this technique first employed the MODFLOW model for simulating the water levels. Next, an improved feed-forward neural network (IFFNN) was combined with an optimization method to create a machine learning (ML) framework. During the last phase, the findings of the optimized connectives approach as well as the relevant fields technologies to determine using improved bald eagle search with least square SVM(IBES-LSSVM) method that predicted the level of water deficit for every period, especially during farming seasons. This approach is based on an improved bald eagle search (IBES) optimization technique for finding the best settings for a least-squares support vector machine (LSSVM). The findings revealed that between 2005 and 2020, the year with the biggest water deficit was 2018 when only roughly 64 percent of water need was satisfied by groundwater (69 percent) and surface water (64 percent) (33 percent). The water depth may have risen by around 0.7 m during the study period if the optimum model had been used. The outcome of this research will help the management forecast future water shortages and make smarter water strategic choices.

Improved neural network with least square support vector machine for wastewater treatment process

This research offers a unique interval by using the predicting approach for discharge indicators of water quality data such as biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N). This is considered one of the significant quality metrics in wastewater treatment plants for water quality management as well as surveillance. To begin, the effluent information for BOD/NH3-N and their supplementary parameters are gathered. Hence BOD and NH3 are considered major feature sources for estimating water pollutants. BOD is high then oxygen level is very low in the water due to pollutants or algae. Ammonia nitrogen is an organic waste component in water from sewage. The significant characteristics with good correlation levels of BOD and NH3-N are examined and identified using a grey correlation analysis method after certain basic data pre-processing procedures. The BOD/NH3-N effluent information of a water treatment plant is predicted using an upgraded feed-forward neural network with the least square support vector machine (FFNN-LSSVM) method. An optimization approach for an enhanced feed-forward neural network (IFFNN) is built by Machine Learning Algorithms. The IFFNN used regular influent water quality, influent rate of flow, and Wastewater performance monitoring and operational conditions as input parameters. For future prediction, input variables were previous different wastewater quality measurements. Lastly, the analysis shows that, when compared to other current algorithms, the proposed methodology can forecast wastewater quality of water with high accuracy in predicting BOD and NH3 levels, limited computation duration, mean error less than 10% and R² is 90% proves better than existing techniques.

Synthesis characterization of Zn-based MOF and their application in degradation of water contaminants

Metal-organic frameworks (MOFs) are currently popular porous materials with research and application value in various fields such as medicine and engineering. Aiming at the application of MOFs in photocatalysis, this paper mainly reviews the main synthesis methods of ZnMOFs and the latest research progress of Zn MOF-based photocatalysts to degrade organic pollutants in water, such as organic dyes. This nanomaterial is being used to treat wastewater and has proven to be very efficient because of its exceptionally large surface area and porous nature. The results show that Zn-MOFs are capable of high degradation of the above pollutants and over 90% of degradation was observed in publications. In addition, the reusability percentage was examined and studies showed that the Zn-MOF nanostructure has very good stability and can continue to degrade a high percentage of pollutants after several cycles. This review focuses on Zn-MOFs and their composites. First, the methods of synthesis and characterization of these compounds are given. Finally, the application of these composites in the process of photocatalytic degradation of dye pollutants such as methylene blue, methyl orange, crystal violet, rhodamine B, etc. is explained.

Antiviral COVID-19 protein and molecular docking: In silico characterization of various antiviral compounds extracted from Arisaema jacquemontii Blume

Arisaema jacquemontii Blume is a highly medicinal and poisonous plant belong to the family Araceae. It is used to treat several deadly diseases, including viral infections. It has antioxidant, anti-cancerous, antimalarial, anti-vermicidal, and antiviral activities. Therefore, five parts of the Arisaema jacquemontii Blume plant, such as leaf, seed, stem, pulp, and rhizome extract, were evaluated for metabolic and in silico characterization of probable compounds using gas chromatography-mass spectrometry (GC-MS) analysis. A total of 22 compounds were isolated from the methanolic extracts of A. jacquemontii Blume. A selected antiviral COVID-19 protein i.e., protease (6LU7) was docked against the obtained compounds. Different affinities were obtained through various compounds. The best results were shown by three different compounds identified in the rhizome. The maximum binding affinity of these compounds is 8.1 kJ/mol. Molecular docking (MD) indicate that these molecules have the highest binding energies and hydrogen bonding interactions. The binding mode of interaction was discovered to be reasonably effective for counteracting the SARS virus COVID-19. The findings of this study could be extremely useful in the development of more phytochemical-based COVID-19 therapeutics.