The production and disposal of plastics have become significant concerns for the sustainability of the planet. During the past 75 years, around 80% of plastic waste has either ended up in landfills or been released into the environment. Plastic debris released into the environment breaks down into smaller particles through fragmentation, weathering, and other disintegration processes, generating microplastics (plastic particles ≤ 5 mm in size). Although marine and aquatic ecosystems have been the primary focus of microplastic pollution research, a growing body of evidence suggests that terrestrial ecosystems are equally at risk. Microplastic contamination has been reported in various terrestrial environments from several sources such as plastics mulch, pharmaceuticals and cosmetics, tire abrasions (tire wear particles), textiles industries (microfibers), sewage sludge, and plastic dumping. Recent studies suggest that the soil has become a significant sink for pollutants released into terrestrial ecosystems and is often contaminated with a mixture of organic and inorganic pollutants. This has gradually caused adverse impacts on soil health and fertility by affecting soil pH, porosity, water-holding capacity, and soil microbial enzymatic activities. Microplastics can interact with the co-existing pollutants of the environments by adsorbing the contaminants onto their surfaces through various intermolecular forces, including electrostatic, hydrophobic, non-covalent, partition effects, van der Waals forces, and microporous filling mechanisms. This subsequently delays the degradation process of existing contaminants, thereby affecting the soil and various ecological activities of the ecosystem. Thus, the present article aims to elucidate the deleterious impact of microplastics and their interactions with other pollutants in the terrestrial ecosystem. This review also addresses the impact of microplastics in disrupting the soil sustainability of the planet.

The present study explores the use of carbon dots coated with Iron (II, III) oxide (Fe₃O₄) for its application as an anode in microbial fuel cells (MFC). Fe₃O₄@PSA-C was synthesized using a hydrothermal-assisted probe sonication method. Nanoparticles were characterized with XRD, SEM, FTIR, and RAMAN Spectroscopy. Different concentrations of Fe₃O₄- carbon dots (0.25, 0.5, 0.75, and 1 mg/cm²) were coated onto the graphite sheets (Fe₃O₄@PSA-C), and their performance in MFC was evaluated. Cyclic voltammetry (CV) of Fe₃O₄@PSA-C (1 mg/cm²) modified anode indicated oxidation peaks at −0.26 mV and +0.16 mV, respectively, with peak currents of 7.7 mA and 8.1 mA. The fluxes of these anodes were much higher than those of other low-concentration Fe₃O₄@PSA-C modified anodes and the bare graphite sheet anode. The maximum power density (Pmax) was observed in MFC with a 1 mg/cm² concentration of Fe₃O₄@PSA-C was 440.01 mW/m², 1.54 times higher than MFCs using bare graphite sheet anode (285.01 mW/m²). The elevated interaction area of carbon dots permits pervasive Fe₃O₄ crystallization providing enhanced cell attachment capability of the anode, boosting the biocompatibility of Fe₃O₄@PSA-C. This significantly improved the performance of the MFC, making Fe₃O₄@PSA-C modified graphite sheets a good choice as an anode for its application in MFC.

Methyl red, a synthetic azo dye, was reported for not only being mutagenic but also its persistence has severe consequences on human health, such as cancer, alongside detrimental environmental effects. In the present study, the Pseudomonas putida OsEnB_HZB_G20 strain was isolated from the soil sample to study the catalytic activity for the degradation of methyl red dye. Another isolated strain, the Pseudomonas aeruginosa PA 1_NCHU strain was used as an electroactive anodophile and mixed with the Pseudomonas putida OsEnB_HZB_G20 strain to see the effect of co-culturing on the power generation in single-chambered microbial fuel cells (MFCs). The Pseudomonas putida OsEnB_HZB_G20 and Pseudomonas aeruginosa PA 1_NCHU strains were used as co-culture inoculum in a 1:1 ratio in MFCs. This work uses isolated bacterial strains in a co-culture to treat wastewater with varying methyl red dye concentrations and anolyte pH to investigate its effect on power output in MFCs. This co-culture produced up to 7.3 W/m³ of power density with a 250 mgL⁻¹ of dye concentration, along with 95% decolorization, indicating that the symbiotic relationship between these bacteria resulted in improved MFC performance simultaneous to dye degradation. Furthermore, the co-culture of Pseudomonas putida and Pseudomonas aeruginosa in a 1:1 ratio demonstrated improved power generation in MFCs at an optimized pH of 7.

Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug‐drug and compound‐protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC‐CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

In an agriculture sector, the quality of several raw crops depends upon the time factor. After harvesting, it is necessary to bring the crops either to cold storage or directly to customer though wholesale dealers. Keeping crops in cold storage decrease the nutrition value and also increase the overall cost of the crop leading to price hike. So, it will be best if the crops are put in the market as soon as possible. This can only be possible if the logistic system is updated and can handle the real-time requirement of agriculture product transport. This study investigates and highlights the possible IoT-based logistic support using artificial intelligence for the farmers such that a fast corridor is created between farmers’ lands to end-user customers. This will benefit the farmers in two-fold; first, it will increase the revenue of farmers by decreasing the time span avoiding cold storage fees, and second, it will maintain the quality of crops.

Biosurfactant-producing microorganisms improve the efficacy of hydrocarbon biodegradation as the biosurfactant is essential in making hydrocarbons available for breakdown. The present study reports the isolation of biosurfactant-producing bacteria that can be used for crude oil remediation and to characterize the biosurfactant generated during the breakdown of crude oil. This study also reports evaluating the synergism and potentiality of biosurfactant-producing bacteria for simultaneous hydrocarbon biodegradation and power generation. Two bacterial strains (Bacillus subtilis strain B1 and Pseudomonas aeruginosa strain B2) were isolated from petroleum-contaminated soils, which are found effective in producing biosurfactants and degrading crude oil as the sole carbon source. B. subtilis B1 exhibited a higher potential for biosurfactant production and crude oil degradation than P. aeruginosa B2. The FTIR and GC-MS analysis were conducted for further characterization of the biosurfactant, which revealed that the surfactant produced by strain B1 and B2 was surfactin and rhamnolipid, respectively. The application of the B1 and B2 co-culture in microbial fuel cells (MFCs) showed synergism among them and resulted in a maximum power density production of 6.3 W/m³ with an open circuit voltage of 970 mV while degrading 2.5% v/v crude oil containing anolyte. The findings indicate that the co-culture of isolated crude oil-degrading strains has great potential for enhanced power generation and the bioremediation of hydrocarbon-contaminated environments. Moreover, the synergism of isolated strains in MFCs suggested their potent applicability in environmental, energy, and industrial sectors as an economical and feasible alternative to the existing technologies.