Nanotechnology mainly involves the fabrication, manipulation, and utilization of materials in nano size (materials having size less than a micron to that of individual atoms). However, nanoparticles can be synthesized by several chemical and physical approaches; now, it is also possible to integrate the use of biological entities. In recent years, mycogenesis of nanoparticles is considered as a prominent way where fungi can be used for the production of nanostructures with desirable shape and size intracellularly or extracellularly. Several researchers have reported that NADH-dependent nitrate reductase enzyme plays an important role in transformation of metal ions into metal nanoparticles. The size and shape of the nanoparticles depend on the microorganism utilized and experimental condition employed during synthesis process. Nanoparticles synthesized from microbes are safe, environmental benign and have several applications in agriculture, textile, medicine, drug delivery, biochemical sensors and allied areas. Future challenges may include large-scale production, enhancement of stability, reduced time to obtain desirable shape and size and their possible applications in several fields. In this review paper, we provide a brief overview on the emerging role of fungi in the synthesis of metal nanoparticles, possible mechanisms and their potential bio-prospective applications.

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Mycogenic nanoparticles and their bio-prospective applications: current status and future challenges

The objective of the present study was one step extracellular biosynthesis of silver nanoparticles (AgNPs) using supernatant of Candida glabrata isolated from oropharyngeal mucosa of human immunodeficiency virus (HIV) patients and evaluation of their antibacterial and antifungal potential against human pathogenic bacteria and fungi. The mycosynthesized AgNPs were characterized by color visualization, ultraviolet-visible (UV) spectroscopy, fourier transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM). The FTIR spectra revealed the binding and stabilization of nanoparticles with protein. The TEM analysis showed that nanoparticles were well dispersed and predominantly spherical in shape within the size range of 2⁻15 nm. The antibacterial and antifungal potential of AgNPs were characterized by determining minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC)/ minimum fungicidal concentration (MFC), and well diffusion methods. The MBC and MFC were found in the range of 62.5⁻250 μg/mL and 125⁻500 μg/mL, which revealed that bacterial strains were more susceptible to AgNPs than fungal strains. These differences in bactericidal and fungicidal concentrations of the AgNPs were due to the differences in the cell structure and organization of bacteria and yeast cells. The interaction of AgNPs with C. albicans analyzed by TEM showed the penetration of nanoparticles inside the Candida cells, which led the formation of "pits" and "pores" that result from the rupturing of the cell wall and membrane. Further, TEM analysis showed that Candida cells treated with AgNPs were highly deformed and the cells had shrunken to a greater extent because of their interaction with the fungal cell wall and membrane, which disrupted the structure of the cell membrane and inhibited the normal budding process due to the destruction and loss of membrane integrity and formation of pores that may led to the cell death.

Biosynthesis of Silver Nanoparticles from Oropharyngeal Candida glabrata Isolates and Their Antimicrobial Activity against Clinical Strains of Bacteria and Fungi

As the core driving force of the new round of informatization development and industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infi...

https://www.tandfonline.com/doi/pdf/10.1080/23270012.2020.1852895

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Emerging Enabling Technologies for Industry 4.0 and Beyond

Biogenic nanomaterials: Synthesis, characterization, growth mechanism, and biomedical applications.

Among the different methods employed for the synthesis of nanoparticles, the biological method is most favorable and quite well established. In microorganisms, use of fungi in the biosynthesis of silver nanoparticles has a greater advantage over other microbial mediators. In this study, intracellular synthesis of silver nanoparticles from Aspergillus terrerus (Thom) MTCC632 was carried out. We observed that synthesis of silver nanoparticles depended on factors such as temperature, amount of biomass and concentration of silver ions in the reaction mixture. Hence, optimization of biosynthesis using these parameters was carried out using statistical tool ‘robust experimental design’. Size and morphology of synthesized nanoparticles were determined using X-ray diffraction technique, field emission scanning electron microscopy, energy dispersion spectroscopy, and transmission electron microscopy. Nano-embedded cotton fabric was further prepared and studied for its antibacterial properties.

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Fungal mediated silver nanoparticle synthesis using robust experimental design and its application in cotton fabric

The import and export of crude oil are vastly affected by the economy of a developing country. It can be useful for the production of petroleum products. Likewise, the developing country, India is completely relying on the import of crude oil from Gulf countries. Thus, there is a need for optimization of routes and modes of transportation. This article presents a two-stage cost and time minimizing fuzzy business restricted multi−objective multi−index transportation problem, in which supplies, demands, and requirements are triangular fuzzy numbers. A business restricted constraints using binary variables are added in the developed supply chain of crude oil in India. The proposed model helps the decision-maker to choose the source country for the import of crude oil. The model is solved by using our proposed fuzzy non−dominated sorting genetic algorithm (NSGA)−II. The performance of the proposed algorithm is analyzed by using a simulation technique for uncertainty level $\\alpha \\in [{0, 1}]$ . Also, the Pareto decision space for formulated business restricted transportation problem is discussed using $\\alpha -$ cut technique. For the superiority of the proposed methodology, we have implemented this on real-world case study viz., Daya case study. Based on the results we claim that the methodology is superior.

Multi-Objective Multi-Index Transportation Model for Crude Oil Using Fuzzy NSGA-II

Inventory management is a crucial task for any industry. In this paper, we have determined the optimum profit and economical order quantity under variety of assumptions such as the demand per unit ...

Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm

Today’s global environment is highly competitive. The competition between two firms means the competition in their supply chain. The prime objective of decision makers are to optimize the supply chain. In every stage of supply chain, the cost and delivery time are involved. These criteria are uncertain in nature. We express these criteria in either the triangular or trapezoidal fuzzy numbers. While optimizing the time and cost simultaneously, the problem becomes bi-objective. Such problems can be solved by using Multiple Objective Genetic Algorithm (MOGA). We get the different Pareto front at different α-cuts which is called as the Pareto decision space. According to the uncertainty in the market, decision maker has a choice to take decision among the set of alternatives. Here, the proposed solution methodology is fusion of fuzzy set theory and MOGA. Fuzzy set theory is used for uncertainty in parameters and MOGA is used for obtaining non-dominated solutions. The numerical example is given to illustrate the proposed methodology and industrial case study is solved by using proposed methodology.

Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts

The newsvendor model is very simple, but it is very important to study problems of supply chain coordination. This model is also called a single-cycle inventory model. In a short cycle, it is very difficult to forecast the demand. The effect of good prediction reduces inventory shortage, increases the profit and enhances competitiveness. This type of product is characterized by strong uncertainty, that means demand always changes. This creates challenges for decision makers. For newly developed products, it is very difficult to obtain the statistical demand distribution of a product. Here, we consider the demand of the milk products, which are fluctuating in nature. Hence, demand is a random variable. It is assumed that demand follows Weibull distribution. Also, there is some uncertainty in demand. It leads to the over estimation or under estimation of the parameters of the distribution. Fuzzy triangular numbers are used to obtain the uncertain demand. The lifetime of the items are very short. Thus, unsold items are sent back to the manufacturers. The retailers and manufacturers profit is obtained under decentralized and centralized supply chain. Also,the proposed single period (newsboy) inventory model is used to obtain an optimal order quantity. The proposed methodology is illustrated for milk product from Aundh market Pune, India.

Raosaheb Latpate

Papers

Fungal mediated silver nanoparticle synthesis using robust experimental design and its application in cotton fabric

Multi-Objective Multi-Index Transportation Model for Crude Oil Using Fuzzy NSGA-II

Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm

Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts

Single stage fuzzy supply chain model with Weibull distributed demand for milk commodities