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Jyoti Rani

Researcher at Indian Institute of Technology (BHU) Varanasi

Publications -  19
Citations -  290

Jyoti Rani is an academic researcher from Indian Institute of Technology (BHU) Varanasi. The author has contributed to research in topics: Biology & Chemistry. The author has an hindex of 3, co-authored 3 publications receiving 87 citations.

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Journal ArticleDOI

Green synthesis of silver nanoparticles from Tectona grandis seeds extract: characterization and mechanism of antimicrobial action on different microorganisms

TL;DR: In this paper, the authors used seed extract of Tectona grandis (teak) for reduction of 1 1/mM silver nitrate solution to silver nanoparticles, which proved to be very simple, cost-efficient, and convenient.
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Microalgal Co-cultivation for Biofuel Production and Bioremediation: Current Status and Benefits

TL;DR: The current advances towards biofuel and its value-added production from various microalgae co-culture is summarized and compared it with monoculture fermentation and some critical challenges of co-culturing for the economically viable bioprocess development for biofuel production are included.
Book ChapterDOI

Biovalorization of winery industry waste to produce value-added products

TL;DR: In this paper, the authors focus on the minimization of environmental hazards of winery waste by its efficient utilization for the production of phytochemicals and potential biofuels through biovalorization.
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Antibiotics in anaerobic digestion: Investigative studies on digester performance and microbial diversity.

TL;DR: In this article , the adverse effects of antibiotics on biogas yield and COD removal in anaerobic reactor technologies for antibiotic wastewater treatment have been discussed and perspectives have been given for understanding the fate and elimination of antibiotics from AD.
Journal ArticleDOI

Smart solution for leaf stress detection and classification a research pattern

TL;DR: In this article , the authors explored different types of IoT frameworks in different sectors and laid down their pros and cons for plant leaf stress detection and classification, and discussed how both of them can be linked to getting the proper information to identify stress in crops using leaf images.