Sweta Yadav

Dual substrate strategy to enhance butanol production using high cell inoculum and its efficient recovery by pervaporation

The present study deals with the development of an efficient ABE fermentation process using mixed substrate strategy for butanol production wherein no acetone was produced. For this, glucose was supplemented in the medium containing glycerol as main substrate which leads to a higher butanol production of 17.75 g/L in 72 h by Clostridium acetobutylicum KF158795. Moreover, the high cell inoculum also resulted in an increased ABE productivity of 0.46 g/L/h. Further, industrial scalability of the process was also successfully validated in a 300 L fermenter. Furthermore, potential of the Polymeric (PolyRMem) and Zeolite (ZeoMem) membranes for separation of butanol from fermentation broth was also studied by testing the pervaporation performance through which the butanol was successfully recovered.

An interactive study of influential parameters for shikimic acid production using statistical approach, scale up and its inhibitory action on different lipases

Shikimic acid is the promising candidate as a building block for the industrial synthesis of drug Tamiflu used for the treatment of Swine flu. The fermentative production process using microbes present an excellent and even more sustainable alternative to the traditional plants based extraction methods. In the present study, the fermentative production of shikimic acid by Citrobacter freundii GR-21 (KC466031) was optimized by process engineering using a statistical modeling approach and a maximum amount of 16.78 g L(-1) was achieved. The process was also scaled up to 14L bioreactor to validate the production of shikimic acid. Further, the potential of anti-enzymatic nature of purified shikimic acid was evaluated for different lipases wherein, shikimic acid inhibited the hydrolysis of triglycerides by 55-60%. Shikimic acid also profoundly inhibited pancreatic lipase activity by 66%, thus providing another valuable therapeutic aspect for treating diet induced obesity in humans.

ORGANIC SYNTHESIS OF MAIZE STARCH-BASED POLYMER USING Rhizopus oryzae LIPASE, SCALE UP, AND ITS CHARACTERIZATION

The industrial utilization of native starches is limited because of their inherit nature, with characteristics such as water insolubility and their tendency to form unstable pastes and gels. In this investigation, a lipase produced from Rhizopus oryzae was used for modification of maize starch with palmitic acid at a reaction temperature of 45°C for 18 hr in the presence of dimethyl sulfoxide (DMSO). The synthesis of maize starch palmitate was confirmed by Fourier-transform infrared (FT-IR) and 1H-nuclear magnetic resonance (NMR) spectra with a higher degree of substitution (DS) of 1.68. Thermal gravimetric analysis (TGA) showed that the maize starch palmitate is more stable even up to 496°C as compared to unmodified maize starch (231.4°C). Maize starch palmitate possesses high degree of substitution and thermal properties and thus can be widely used in food and pharmaceutical industry.

Leveraging Machine Learning in Mist Computing Telemonitoring System for Diabetes Prediction

Big data analytics with the help of cloud computing is one of the emerging areas for processing and analytics in healthcare system. Mist computing is one of the paradigms where edge devices assist the fog node to help reduce latency and increase throughput for assisting at the edge of the client. This paper discusses the emergence of mist computing for mining analytics in big data from medical health applications. The present paper proposed and developed mist computing-based framework, i.e., MistLearn for application of K-means clustering on real-world feature data for detecting diabetes in-home monitoring of patients suffering from diabetes mellitus. We built a prototype using Intel Edison and Raspberry Pi; the embedded microprocessor for MistLearn. The proposed architecture has employed machine learning on a deep learning framework for analysis of pathological feature data that can be obtained from smartwatches worn by the patients with diabetes. The results showed that mist computing holds an immense promise for analysis of medical big data especially in telehealth monitoring of patients.

Plasma diagnosis as a tool for the determination of the parameters of electron beam evaporation and sources of ionization

The atomic vapor generated by electron beam heating is partially ionized due to atom–atom collisions (Saha ionization) and electron impact ionization, which depend upon the source temperature and area of evaporation as compared to the area of electron beam bombardment on the target. When electron beam evaporation is carried out by inserting the target inside an insulating liner to reduce conductive heat loss, it is expected that the area of evaporation becomes significantly more than the area of electron beam bombardment on the target, resulting in reduced electron impact ionization. To assess this effect and to quantify the parameters of evaporation, such as temperature and area of evaporation, we have carried out experiments using zirconium, tin and aluminum as a target. By measuring the ion content using a Langmuir probe, in addition to measuring the atomic vapor flux at a specific height, and by combining the experimental data with theoretical expressions, we have established a method for simultaneously inferring the source temperature, evaporation area and ion fraction. This assumes significance because the temperature cannot be reliably measured by an optical pyrometer due to the wavelength dependent source emissivity and reflectivity of thin film mirrors. In addition, it also cannot be inferred from only the atomic flux data at a certain height as the area of evaporation is unknown (it can be much more than the area of electron bombardment, especially when the target is placed in a liner). Finally, the reason for the lower observed electron temperatures of the plasma for all the three cases is found to be the energy loss due to electron impact excitation of the atomic vapor during its expansion from the source.