Biofuel production for circular bioeconomy: Present scenario and future scope.

In recent years, biofuel production has attracted considerable attention, especially given the increasing worldwide demand for energy and emissions of greenhouse gases that threaten this planet. In this case, one possible solution is to convert biomass into green and sustainable biofuel, which can enhance the bioeconomy and contribute to sustainable economic development goals. Due to being in large quantities and containing high organic content, various biomass sources such as food waste, textile waste, microalgal waste, agricultural waste and sewage sludge have gained significant attention for biofuel production. Also, biofuel production technologies, including thermochemical processing, anaerobic digestion, fermentation and bioelectrochemical systems, have been extensively reported, which can achieve waste valorization through producing biofuels and re-utilizing wastes. Nevertheless, the commercial feasibility of biofuel production is still being determined, and it is unclear whether biofuel can compete equally with other existing fuels in the market. The concept of a circular economy in biofuel production can promote the environmentally friendly and sustainable valorization of biomass waste. This review comprehensively discusses the state-of-the-art production of biofuel from various biomass sources and the bioeconomy perspectives associated with it. Biofuel production is evaluated within the framework of the bioeconomy. Further perspectives on possible integration approaches to maximizing waste utilization for biofuel production are discussed, and what this could mean for the circular economy. More research related to pretreatment and machine learning of biofuel production should be conducted to optimize the biofuel production process, increase the biofuel yield and make the biofuel prices competitive.

Microfibers in laundry wastewater: Problem and solution.

Data corroborated in this study highlights laundry wastewater as a primary source of microfibers (MFs) in the aquatic environment. MFs can negatively impact the aquatic ecosystem via five possible pathways, namely, acting as carriers of other contaminats, physical damage to digestive systems of aquatic organisms, blocking the digestive tract, releasing toxic chemicals, and harbouring invasive and noxious plankton and bacteria. This review shows that small devices to capture MFs during household laundry activities are simple to use and affordable at household level in developed countries. However, these low cost and small devices are unrealiable and can only achieve up to 40 % MF removal efficiency. In line filtration devices can achieve higher removal efficiency under well maintained condition but their performance is still limited compared to over 98 % MF removal by large scale centralized wastewater treatment. These results infer that effort to increase sanitation coverage to ensure adequate wastewater treatment prior to environmental discharge is likely to be more cost effective than those small devices for capturing MFs. This review also shows that natural fabrics would entail significantly less environmental consequences than synthetic materials. Contribution from the fashion industry to increase the share of natural frabics in the current textile market can also reduce the loading of plastic MFs in the environment.

Performance of a dual-chamber microbial fuel cell as a biosensor for in situ monitoring Bisphenol A in wastewater.

This research explores the possibilities of a dual-chamber microbial fuel cell as a biosensor to measure Bisphenol A (BPA) in wastewater. BPA is an organic compound and is considered to be an endocrine disruptor, affecting exposed organisms, the environment, and human health. The performance of the microbial fuel cells (MFCs) was first controlled with specific operational conditions (pH, temperature, fuel feeding rate, and organic loading rate) to obtain the best accuracy of the sensor signal. After that, BPA concentrations varying from 50 to 1000 µg L-1 were examined under the biosensor's cell voltage generation. The outcome illustrates that MFC generates the most power under the best possible conditions of neutral pH, 300 mg L-1 of COD, R 1000 Ω, and ambient temperature. In general, adding BPA improved the biosensor's cell voltage generation. A slight linear trend between voltage output generation and BPA concentration was observed with R2 0.96, which indicated that BPA in this particular concentration range did not real harm to the MFC's electrogenic bacteria. Scanning electron microscope (SEM) images revealed a better cover biofilm after BPA injection on the surface electrode compared to it without BPA. These results confirmed that electroactive biofilm-based MFCs can serve to detect BPA found in wastewaters.

Enhanced photo-fermentative biohydrogen production from biowastes: An overview.

Clean energy like hydrogen can be a promising strategy to solve problems of global warming. Photo-fermentation (PF) is an attractive technology for producing biohydrogen from various biowastes cost-effectively and environmentally friendly. However, challenges of low light conversion efficiency and small yields of biohydrogen production still limit its application. Thus, advanced strategies have been investigated to enhance photo-fermentative biohydrogen production. This review discusses advanced technologies that show positive outcomes in improving biohydrogen production by PF, including the following. Firstly, genetic engineering enhances light transfer efficiency, change the activity of enzymes, and improves the content of ATP, ammonium and antibiotic tolerance of photosynthetic bacteria. Secondly, immobilization technology is refined. Thirdly, nanotechnology makes great strides as a scientific technique and fourthly, integration of dark and photo-fermentation technology is possible. Some suggestions for further studies to achieve high levels of efficiency of photo-fermentative biohydrogen production are mentioned in this paper.

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

Advanced strategies for enhancing dark fermentative biohydrogen production from biowaste towards sustainable environment.

As a clean energy carrier, hydrogen is a promising alternative to fossil fuel so as the global growing energy demand can be met. Currently, producing hydrogen from biowastes through fermentation has attracted much attention due to its multiple advantages of biowastes management and valuable energy generation. Nevertheless, conventional dark fermentation (DF) processes are still hindered by the low biohydrogen yields and challenges of biohydrogen purification, which limit their commercialization. In recent years, researchers have focused on various advanced strategies for enhancing biohydrogen yields, such as screening of super hydrogen-producing bacteria, genetic engineering, cell immobilization, nanomaterials utilization, bioreactors modification, and combination of different processes. This paper critically reviews by discussing the above stated technologies employed in DF, respectively, to improve biohydrogen generation and stating challenges and future perspectives on biowaste-based biohydrogen production.

A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion.

Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean - square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to various agricultural SOC retrieval studies globally.

Bio-membrane integrated systems for nitrogen recovery from wastewater in circular bioeconomy.

Wastewater contains a significant amount of recoverable nitrogen. Hence, the recovery of nitrogen from wastewater can provide an option for generating some revenue by applying the captured nitrogen to producing bio-products, in order to minimize dangerous or environmental pollution consequences. The circular bio-economy can achieve greater environmental and economic sustainability through game-changing technological developments that will improve municipal wastewater management, where simultaneous nitrogen and energy recovery are required. Over the last decade, substantial efforts were undertaken concerning the recovery of nitrogen from wastewater. For example, bio-membrane integrated system (BMIS) which integrates biological process and membrane technology, has attracted considerable attention for recovering nitrogen from wastewater. In this review, current research on nitrogen recovery using the BMIS are compiled whilst the technologies are compared regarding their energy requirement, efficiencies, advantages and disadvantages. Moreover, the bio-products achieved in the nitrogen recovery system processes are summarized in this paper, and the directions for future research are suggested. Future research should consider the quality of recovered nitrogenous products, long-term performance of BMIS and economic feasibility of large-scale reactors. Nitrogen recovery should be addressed under the framework of a circular bio-economy.

A dual chamber microbial fuel cell based biosensor for monitoring copper and arsenic in municipal wastewater.

This study investigated a dual-chamber microbial fuel cell-based biosensor (DC-MFC-B) for monitoring copper and arsenic in municipal wastewater. Operational conditions, including pH, flow rate, a load of organic substrate and external resistance load, were optimized to improve the biosensor's sensitivity. The DC-MFC-B's toxicity response was established under the electroactive bacteria inhibition rate function to a specific heavy metal level as well as the recovery of the DC-MFC-B. Results show that the DC-MFC-B was optimized at the operating conditions of 1000 Ω external resistance, COD 300 mg L-1 and 50 mM K3Fe(CN)6 as a catholyte solution. The voltage output of the DC-MFC-B decreased with increasing in the copper and arsenic concentrations. A significant linear relationship between the maximum voltage of the biosensor and the heavy metal concentration was obtained with a coefficient of R2 = 0.989 and 0.982 for copper and arsenic, respectively. The study could detect copper (1-10 mg L-1) and arsenic (0.5-5 mg L-1) over wider range compared to other studies. The inhibition ratio for both copper and arsenic was proportional to the concentrations, indicating the electricity changes are mainly dependent on the activity of the electrogenic bacteria on the anode surface. Moreover, the DC-MFC-B was also recovered in few hours after being cleaned with a fresh medium. It was found that the concentration of the toxicant effected on the recovery time and the recovery time was varied between 4 and 12 h. In short, this work provided new avenues for the practical application of microbial fuel cells as a heavy metal biosensor.

Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells.

Microbial electrolysis cell (MEC) system is an environmentally friendly method for clean biohydrogen production from a wide range of biowastes owing to low greenhouse gas emissions. This approach has relatively higher yields and lower energy costs for biohydrogen production compared to conventional biological technologies and direct water electrolysis, respectively. However, biohydrogen production efficiency and operating costs of MEC still need further optimization to realize its large-scale application.This paper provides a unique review of impact factors influencing biohydrogen production in MECs, such as microorganisms and electrodes. Novel strategies, including inhibition of methanogens, development of novel cathode catalyst, advanced reactor design and integrated systems, to enhance low-cost biohydrogen production, are discussed based on recent publications in terms of their opportunities, bottlenecks and future directions. In addition, the current challenges, and effective future perspectives towards the practical application of MECs are described in this review.

Enhanced Degradation of Rhodamine B by Metallic Organic Frameworks Based on NH2-MIL-125(Ti) under Visible Light.

Samples of the bimetallic-based NH2-MIL-125(Ti) at a ratio of Mn+/Ti4+ is 0.15 (Mn+: Ni2+, Co2+ and Fe3+) were first synthesized using the solvothermal method. Their fundamental properties were analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectra, scanning electron microscopy (SEM), N2 adsorption-desorption measurements, and UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS). The as-acquired materials were used as high-efficiency heterogeneous photocatalysts to remove Rhodamine B (RhB) dye under visible light. The results verified that 82.4% of the RhB (3 × 10-5 M) was degraded within 120 min by 15% Fe/Ti-MOFs. Furthermore, in the purpose of degrading Rhodamine B (RhB), the rate constant for the 15% Fe/Ti-MOFs was found to be 2.6 times as fast as that of NH2-MIL-125(Ti). Moreover, the 15% Fe/Ti-MOFs photocatalysts remained stable after three consecutive cycles. The trapping test demonstrated that the major active species in the degradation of the RhB process were hydroxyl radicals (HO∙) and holes (h+).