MULTIDISCIPLINARY VOL 09 -HYANEA PUBLISH

MULTIDISCIPLINARY AREA OF RESEARCH VOLUME-9 ISBN- 978-81-969444-8-3
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MULTIDISCIPLINARY AREA OF RESEARCH
VOLUME – 09
Editor-in-Chief
Dr N Hariharan
Founder and Chief Editor Heduna Peer
International Research and Reviews
Associate Editors
Dr. S.V. REVATHI
Dr. E. MANIKANDAN
Mrs. S. KALAI SELVI
Legal Adviser
MR. P. RAJENDRA CHOLAN
ADVOCATE, BAR ASSOCIATION
THIRUTHIRAIPOONDI, THIRUVARUR

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Text ©AUTHOR, 2024
Cover page © HEDUNA PEER OF INTERNATIONAL RESEARCH
AND REVIEWS
Author ©: Dr N HARIHARAN
Editors: Dr. S.V. REVATHI, Dr. E. MANIKANDAN
Mrs. S. KALAI SELVI
Publisher: Heduna Peer International Research and Reviews
T. Vadipatty, M.P Nagar, Madurai, Tamilnadu, India Phone: + 91 9345020835
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Book: MULTIDISCIPLINARY AREA OF RESEARCH VOLUME-9
ISBN- 978-81-969444-8-3
Edition: MARCH-2024
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Mr.LAKSHYA CHAUDHARY
RESEARCH SCIENTIST
NATIONAL PRESIDENT & CHAIRMAN
KOSHAMBI FOUNDATION, INDIA.
Mr. P. RAJENDRA CHOLAN
ADVOCATE, BAR ASSOCIATION
THIRUTHIRAIPOONDI,THIRUVARUR
HPIRR JOURNAL & HYAENA PUBLISHERS INDIA
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PGP COLLEGE OF ENGINEERING AND TECHNOLOGY, NAMAKKAL
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LEGAL ADVISOR – HPIRR JOURNAL
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ASSOCIATE DIRECTOR – HPIRR JOURNAL

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Dr.S.V.REVATHI
ASSISTANT PROFESSOR &
IQAC CO-ORDINATOR
DEPARTMENT OF COMMERCE
PPG COLLEGE OF ARTS AND SCIENCE,
COIMBATORE
Dr E MANIKANDAN
ASSISTANT PROFESSOR
DEPARTMENT OF PHYSICS
Dr M.G.R GOVERNMENT ARTS AND SCIENCE
COLLEGE FOR WOMEN
VILLUPURAM
Mrs S.KALAISELVI
ASSISTANT PROFESSOR
DEPARTMENT OF COMPUTER SCIENCE
PPG COLLEGE OF ARTS AND SCIENCE
COIMBATORE
BOOK CHAPTER EDITORS

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SI
NO
CHAPTER TITLE AUTHOR PAG
NO
1 “ARTIFICIAL INTELLIGENCE EVOLUTION AND
INNOVATIONS IN AGRICULTURE SECTOR”
DR.J.NIMALA 10-15
2 “NATURAL TECHNIQUE OF SILVER (AG) NANOPARTICLES
USING SEED OF TAMARIND AND ITS ANTI-OXIDANT
POTENTIAL”
S.PUSHPA 1 ,
E.MANIKANDAN 2
E.GOPINATHAN 3
16-28
3 “CO-FE-ZNO NANOPARTICLES SYNTHESIS VIA SOL–GEL
TECHNIQUE”
S.PUSHPA 1 ,
E.MANIKANDAN 2
E.GOPINATHAN 3
29-36
4 “OPTICAL, PHOTOLUMINESCENCE PROPERTIES AND
SUPERCAPACITOR APPLICATION ON ZN DOPING SNO2
NANOPARTICLES”
S.PUSHPA 1 ,
E.MANIKANDAN 2
34-54
5 “A STUDY ON MAHINDRA THAR CARS TECHNOLOGY AND
BUSINESS STRATEGY IN CURRENT SCENARIO”
Dr. G. AYYANAR
55-65
6 “ A COMPARATIVE STUDY ON BETWEEN TELEGRAM AND
WHATSAPP SERVICES AND STUDY ON SECURITY ANALYSIS
Dr. G. AYYANAR 66-74
7 “ A STUDY ON ELECTRIC JCB ARE THE FUTURE
EARTHMOVING AND CONSTRUCTION EQUIPMENT IN INDIA”
Mr .B. VIGNESH 75-86
8 “A STUDY ON EMERGING TRENDS AND FEATURES OF
E-COMMERCE IN INDIA”
Dr.S.V.REVATHI 87-95

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9 “A STUDY ON COMPARISON ANALYSIS FOR AMAZON VS
FLIPKART CURRENT GENRATOIN”
Mrs S.KALAISELVI 96-106
10 “A STUHDY ON ON IMPACT OF JIO TELECOM INDUSTRY
THROUGH THEIR MARKETING STRATEGIES”
11
“A STUDY ON SEO - SEARCH ENGINE MARKETING THE
IMPACT AND CURRENT TRENDS”
Dr.M.KARUPPANASAMY 137-127
12 “A STUDY ON ONLINE SHOPPING APPLICATION OLX WITH
REFERENCE TO THENI DISTRICT”

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CHAPTER – 1
DR.J.NIMALA,
ASSISTANT PROFESSOR,
DEPARTMENT OF B.COM (BUSINESS ANALYTICS),
SREE SARASWATHI THYAGARAJA COLLEGE,
THIPPAMPATTI, POLLACHI
ABSTRACT
The application of artificial intelligence (AI) and machine learning in agriculture is growing as a
means of boosting productivity, raising crop yields, and cutting costs associated with food
production. Using in-ground sensors and drones to optimize crop yields, farmers can monitor real-
time video feeds of their crop fields, detect breaches caused by animals or humans, and identify these
threats with the aid of AI and machine learning. These technologies can also monitor livestock
health, anticipate pest infestations, and optimize irrigation systems, all of which contribute to the
productivity and well-being of livestock. The agricultural industry is undergoing a revolution thanks
to artificial intelligence (AI) and machine learning, which are giving farmers and agricultural
enterprises new tools and insights to enhance operations, cut expenses, and boost productivity.
Key Words
Artificial intelligence, Sustainable Agriculture, Automation, Cost reduction and Productivity.
INTRODUCION
Artificial intelligence (AI) is the theory and development of computer systems capable of performing
tasks that historically required human intelligence, such as recognizing speech, making decisions,
and identifying patterns. Artificial intelligence (AI) refers to computer systems capable of
performing complex tasks that historically only a human could do, such as reasoning, making
decisions, or solving problems. Today, the term “AI” describes a wide range of technologies that
power many of the services and goods we use every day.
Some of the most common examples of AI in use today include:
“ARTIFICIAL INTELLIGENCE EVOLUTION AND INNOVATIONS
IN AGRICULTURE SECTOR”

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 ChatGPT: Uses large language models (LLMs) to generate text in response to questions or
comments posed to it.
 Google Translate: Uses deep learning algorithms to translate text from one language to
another.
 Netflix: Uses machine learning algorithms to create personalized recommendation engines
for users based on their previous viewing history.
 Tesla: Uses computer vision to power self-driving features on their cars.
Artificial Intelligence (AI) in the agriculture sector
Artificial Intelligence (AI) has been increasingly implemented in the agriculture sector to enhance
productivity, efficiency, and sustainability. The following are the recent applications of AI in
agriculture
Precision Agriculture: AI helps in collecting, analysing, and interpreting data from various sources
such as satellite imagery, sensors, and weather forecasts to provide precise information about soil
health, crop growth, and yield prediction. This enables farmers to make data-driven decisions for
crop management, irrigation, and fertilization.
Automation: AI powers autonomous farming equipment like drones, robots, and self-driving
tractors. These machines can perform tasks such as planting, harvesting, and monitoring crops,
reducing the need for manual labour and increasing efficiency.
AI-powered Software: There are AI-powered software and tools that help farmers monitor crop
health, predict diseases, and suggest preventive measures. These tools can also help in managing
farm finances, market analysis, and supply chain optimization.
Indoor Farming: AI is used in indoor farming or vertical farming where crops are grown in stacked
layers in a controlled environment. AI helps in managing the climate, lighting, and nutrient supply
for optimal crop growth.
Waste Reduction: AI can help in reducing food waste by predicting the demand and supply of
agricultural products, thereby helping in better planning and management of the supply chain.
Sustainable Agriculture: AI can assist in making farming more sustainable by optimizing the use
of resources like water, energy, and fertilizers. It can also help in developing crop varieties that are
more resilient to climate change.

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AI and Farmers:
AI can help farmers increase their accuracy and productivity in several ways. For instance, AI-
powered crop and soil monitoring can provide farmers with real-time data on soil moisture, nutrient
levels, and crop health, which can help them make informed decisions about irrigation, fertilization,
and pesticide application. This can lead to more precise and efficient use of resources, reducing waste
and increasing yields.
AI-powered automation can also help farmers with tasks such as planting, weeding, and harvesting.
For example, autonomous tractors and drones can perform tasks with high precision, reducing human
error and increasing efficiency. Additionally, AI-powered robotic harvesters can pick crops with
greater accuracy and speed than human workers, reducing the risk of damage and waste.
Another way AI helps farmers is by providing them with predictive analytics for weather and disease
forecasting. By analyzing historical and real-time weather and disease data, AI algorithms can
predict the likelihood of crop diseases, pest outbreaks, and extreme weather events. This allows
farmers to take preventative measures to protect their crops, reducing the risk of crop failure and
increasing yields.
Furthermore, AI-powered market analysis can help farmers determine optimal times for sowing and
harvesting based on market demand and pricing trends. By analyzing historical and real-time market
data, AI algorithms can provide farmers with accurate demand forecasts, enabling them to make
informed decisions about when to plant and harvest their crops.
Overall, AI has the potential to revolutionize the agriculture industry by providing farmers with
accurate and real-time data, automating labor-intensive tasks, and enabling more informed decision-
making. However, it is important to note that AI relies on the quality and accuracy of data, wireless
connectivity, which can be limited in rural areas, and initial implementation costs may also pose a
challenge for small-scale farmers with limited resources. Additionally, AI cannot fully replace the
creative thinking and intuition that farmers bring to their work. By striking a balance between

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leveraging AI's capabilities and recognizing the value of human expertise, farmers can embrace AI
as a powerful tool, ensuring a more sustainable and prosperous future for agriculture.
The Indian government's AI schemes aim to promote the use of AI technologies in agriculture,
improve productivity and sustainability, and reduce waste and resource use. By striking a balance
between leveraging AI's capabilities and recognizing the value of human expertise, farmers can
embrace AI as a powerful tool, ensuring a more sustainable and prosperous future for
agriculture.the Indian government has also introduced various AI schemes in agriculture to
promote the use of AI technologies and increase productivity and sustainability. Here are some
examples:
National e-Governance Plan: The Indian government has launched the National e-Governance
Plan to digitize and automate various government services, including agriculture. The plan
includes the use of AI-powered decision support systems, crop monitoring, and yield estimation
tools to improve agricultural productivity and reduce crop losses.
SmartFarm: The Indian government has launched the SmartFarm initiative to promote the use of
AI and IoT technologies in agriculture. The initiative includes the development of precision
agriculture tools, smart irrigation systems, and automated crop monitoring systems to improve
crop yields and reduce resource use.
AI-based Crop Insurance: The Indian government has introduced an AI-based crop insurance
scheme to provide farmers with insurance coverage for crop losses due to natural disasters. The
scheme uses AI-powered satellite imagery and weather data to estimate crop yields and calculate
insurance payouts.
AI-powered Market Analysis: The Indian government has launched an AI-powered market
analysis tool to provide farmers with real-time market data and price forecasts. The tool uses AI-
powered algorithms to analyze market trends and provide farmers with accurate demand forecasts,
enabling them to make informed decisions about when to plant and harvest their crops.
AI-powered Disease Detection: The Indian government has introduced an AI-powered disease
detection tool to detect crop diseases and pests. The tool uses AI-powered image recognition
algorithms to analyze images of crops and identify signs of disease or pest infestations.

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The Indian government has been actively promoting AI awareness in agriculture among farmers.
They have introduced various AI-powered tools and technologies to help farmers increase their
productivity and sustainability. For instance, the government has launched the SmartFarm
initiative, which promotes the use of AI and IoT technologies in agriculture. The initiative includes
the development of precision agriculture tools, smart irrigation systems, and automated crop
monitoring systems to improve crop yields and reduce resource use.
AI AWARENESS AMONG FARMERS:
In addition, the Indian government has also introduced AI-powered market analysis tools to
provide farmers with real-time market data and price forecasts. The tool uses AI-powered
algorithms to analyze market trends and provide farmers with accurate demand forecasts, enabling
them to make informed decisions about when to plant and harvest their crops. Moreover, the
government has launched an AI-based crop insurance scheme to provide farmers with insurance
coverage for crop losses due to natural disasters. The scheme uses AI-powered satellite imagery
and weather data to estimate crop yields and calculate insurance payouts.
The Indian government has also introduced AI-powered disease detection tools to detect crop
diseases and pests. The tool uses AI-powered image recognition algorithms to analyze images of
crops and identify signs of disease or pest infestations.
Overall, the Indian government's AI awareness campaigns aim to promote the use of AI
technologies in agriculture, improve productivity and sustainability, and reduce waste and resource
use.
CONCLUSION
AI has a significant role to play in the future of agriculture, promising to transform the way we
grow and distribute food, making it more efficient, sustainable, and resilient. However, it is
important to note that AI relies on the quality and accuracy of data, wireless connectivity, which
can be limited in rural areas, and initial implementation costs may also pose a challenge for small-
scale farmers with limited resources. Additionally, AI cannot fully replace the creative thinking
and intuition that farmers bring to their work. The government is working closely with farmers,

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academic institutions, and local communities to ensure the successful adoption of AI technologies
in agriculture.
REFERENCES
Website:
1. https://www.pmindia.gov.in/en/government_tr_rec/empowering-farmers-for-a-
prosperous-india/
2. https://intellias.com/artificial-intelligence-in-agriculture/
3. https://www.reuters.com/sustainability/land-use-biodiversity/theres-an-app-that-how-
ai-is-ploughing-farming-revolution-2024-01-15/
4. https://www.agritechtomorrow.com/tag/ai
5. https://www.agritechtomorrow.com/tag/ai
Journals:
Implementation of artificial intelligence in agriculture for optimisation of irrigation and application
of pesticides and herbicides, Tanha Talaviya, Dhara Shahand etal, Volume 4, 2020, Pages 58-73,
Artificial Intelligence in Agriculture.
Emerging technologies from drones to digitalization have the potential to transform farming
productivity, reduce environmental impact and boost farmers’ incomes, World Economic
Forum,by Artificial Intelligence for Agriculture Innovation COMMUNITY PAPER MARCH
2021
AI for agriculture: How Indian farmers are harvesting innovation, by World Economic Forum,
Published Jan 11, 2024 · Updated Jan 12, 2024.

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CHAPTER – 2
S.PUSHPA 1 , E.MANIKANDAN 2 AND E.GOPINATHAN 3
1 DEPARTMENT OF PHYSICS, BHARATH INSTITUTE OF HIGHER EDUCATION AND
RESEARCH, CHENNAI, TAMILNADU. INDIA
2,3 DEPARTMENT OF PHYSICS, Dr.M.G.R. GOVERNMENT ARTS AND SCIENCE COLLEGE
FOR WOMEN, VILLUPURAM, TAMILNADU. INDIA.
ABSTRACT
The present work deals with the formation of silver nanoparticles by inexperienced
synthesis method the usage of an aqueous Tamarind seed extract. The Tamarind seed extract is
manipulated in phytochemical screening the inexperienced-synthesized TS-AgNPs display an
absorption top at 454nm corresponds to surface plasma resonance of AgNPs. The existence of
biomolecules in the Tamarind seed extract has identified the usage of Fourier remodel infrared
spectroscopy (FTIR). Those bio compounds found in seed extract act as stabilizing and capping
agents. The Field emission scanning electron microscopy (FESEM) found out that the synthesized
TS-AgNPs had been round in form and the particle size range from 58 nm. The electricity-
dispersive X-ray spectroscopy (EDAX) indicates predominant peaks for silver. The X-ray powder
diffraction (XRD) patterns of the TS-AgNPs are face-focused cubic in shape and crystalline in
nature. This silver nanoparticle shows an appropriate impact on antioxidant (DPPH method)
assays. The biosynthesis of TS-AgNPs is easy, eco-friendly, non-toxic in nature and it is projected
in prescribed drugs.
KEYWORDS: Green synthesis, Silver nanoparticles, Characterization, antioxidant assay.
INTRODUCTION
Medicinal vegetation is used as the foremost natural supply for drug formulation and the
“NATURAL TECHNIQUE OF SILVER (AG) NANOPARTICLES USING SEED
OF TAMARIND AND ITS ANTI-OXIDANT POTENTIAL”

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remedy of human illnesses. Nature has blessed the plant with a paranormal phenomenon to supply
bioactive secondary metabolites. Numerous natural merchandise is made and used for each day.
Synthesis of nanoparticles is one of the new biomedicine manufacturing manners from the
medicinal plant life. Nanoparticles showcase specific residences because of their length,
distribution, and morphology. Synthesis of nanoparticles from leaf extract is financial, green,
easily scaled up for massive scale synthesis, and do now not need the usage of poisonous chemical
compounds, high stress, temperature, and energy [1]. inexperienced synthesized AgNPs particles
have extraordinary software such as drug shipping [2], gene remedy [3], spectrally selective
coatings for solar energy absorption and intercalation cloth for sun power batteries, as optical
receptors, catalysts in chemical reactions [4,5], bio labeling, and as antimicrobial [6,7], antifungal
[8]and antioxidant sellers [9]. The developing desires to develop smooth, non-toxic, and eco-
friendly techniques for the synthesis of nanoparticles have resulted in researchers to paintings
enthusiastically in organic systems[2].
Antioxidants are very crucial to protect cells and organic macromolecules from
degenerative reactions produced by using unfastened radicals and reactive oxygen species. The
antioxidant property of various plant merchandise, inclusive of polyphenolic substances (e.g.,
flavonoids and tannins) derived from various flora and herbal extracts had been suggested [10–
12]. In the current research, the oxygen- primarily based free radicals were proved to be scavenged
successfully by using inorganic nanoparticles [13]. moreover, silver has long been documented as
a valuable antimicrobial agent that well-known shows low toxicity in people and incorporates
various in vitro and in vivo applications a few of the different metals [14]. The noticeably reactive
metal oxide nanoparticles are widely recognized to demonstrate remarkable bactericidal hobby in
opposition to Gram-positive and Gram-negative bacteria [15]. recently, AgNPs exhibit a lot of
scopes in the discipline of high sensitivity biomolecular detection, catalysis, biosensors, and
medication along with the anti-fungal, anti-inflammatory, and anti-angiogenesis activities [16] in
addition to antioxidant and antimicrobial pastime, the cytotoxicity studies additionally contain
prominent importance and several studies are underway to elucidate these aspects. In Indian
traditional medicinal drugs, natural extracts have lengthy been used for treating numerous
pathological approaches along with respiratory, neurodegenerative, and cardiovascular
illnesses[17,18].
The organic materials of the plant's starting place play a pioneering role in lowering salt in

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the plant extract to synthesize nanoparticles and contributing to their stability. We record an easy,
low-value, handy, green synthesis approach to reap big portions of silver nanoparticles by
reduction of silver ions through the usage of Tamarind seed extract. The importance of the existing
piece of labor is considered mainly with admire to the unconventional aspect of surface capping
of AgNPs with plant secondary metabolites of medicinal hobby which may also generate
suggestive scope for studies within the area of nanoparticles assisted plant metabolomics. This
examination is likewise vital to recognize the phytosynthesized AgNPs (synthesis of nanoscale
silver debris the usage of plant material extract) in scavenging the free radicals. The silver
nanoparticle era is awarded more modern programs in Business the front especially within the field
of drug enterprise due to the useful potentials of nanoscale size.
MATERIALS AND METHODS
The medicinal plant Tamarind seed extract was amassed from Cuddalore, Tamil Nadu in
India. 1000 mL of double-distilled water became taken in a beaker blended with (0.169g) silver
nitrate weighted the use of an analytical balance and stirred using a magnetic stirrer till it dissolved
and turned into then preserved in a brown bottle in the room as depicted.
PHYTOSYNTHESIS OF SILVER NANOPARTICLES
Six milliliters of seed extract were delivered to 120 mL of 10-3
M of AgNO3 the reaction
took place the alternate in color from colorless to an obvious yellow brown and reached dark
brown[19,20]. This showed the formation of TS-AgNPs. The attention and particle size of TS-
AgNPs eventually extended. After of completion of the discount reaction, there was no giant color
exchange. Those TS-AgNPs were centrifuged (Remi RM-12C) at 12,000 rpm for 20 min. This
separated substance changed into purified via washing with alcohol in a few instances. After, the
purification pellets were kept in a warm air oven at 200°C for 2 hours. As the end result, black-
colored materials are collected and powdered by the usage of pestle mortar.
MECHANISM OF AGNPS FORMATION
The biochemical reaction of AgNO3 reacts with seed extract format to form AgNPs [21,22].
The equation below explains the mechanism of biosynthesized nanoparticles.

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Ag+
NO3-+Seed extract → Ag°NPs+by-products
The Phytocompounds consisting of seed extract act as a decreasing agent among those,
flavonoids act as a sturdy decreasing agent in the reduction system. These Phytocompounds act as
lowering and capping marketers. They may be also a prime supply of lengthy stability of
compounds.
FREE RADICAL SCAVENGING ACTIVITY (DPPH METHOD INVITRO)
The Free radical scavenging activity is examined with the aid of using 2, 2-diphenyl-1-
picrylhydrazyl (DPPH). The DPPH substance is prepared using a methanol answer for 0.2mM.
Take TS-AgNPs (20-100µg/ml) mix with water and upload 1 mL of prepared DPPH solution and
shake vigorously. Then region the mixed solution in a dark room for 30 min after measuring the
absorbance. Similarly, ascorbic acid is used as widespread and is compared with silver
nanoparticles. After measuring the IC50value is calculated[23].
The scavenging ability is calculated using a formula.
% of inhibition = 100 ×
(A − B)
A
Where I (%) is inhibition percentage
A- Absorbance of control reaction
B- Sample absorbance of test compound.
RESULT AND DISCUSSION
UV-VIS ABSORPTION SPECTROSCOPY ANALYSIS
The UV-visible spectral studies act as the number one characterization and as a cornerstone
in the deduction of metallic nanoparticles particularly silver [24]. The dimensions and form of
controlled nanoparticles in aqueous suspensions can be examined by means of UV-vis
spectroscopy [25]. The stages of absorption band 425-450 nm indicate the presence of round
formed silver nanoparticles [26]. In our present report, we analyze UV-vis spectroscopy for TS-
AgNPs occurs at 454 nm as shown in Fig.2. These characterization bands at this range are nicely-
recognized for many metal nanoparticles degrees from 2-100nm[27].
FT-IR SPECTROSCOPY

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FT-IR spectroscopy identifies the silver nanoparticle and bio-molecules found in
synthesized nanoparticles [28]. The FT-IR spectrum of Tamarindseed extract silver nanoparticles
powder suggests numerous absorption peaks as in Fig.3. The bands at 3361,
2362,1612,1521,1446,1055, and 603 cm-1
correspond to N-H stretching of a number one amine, O-
H stretching of alcohol, C-H stretching of aldehyde doublet, NH bending amine, N-O stretching
nitro compound, C-H bending alkene, C-N stretching amine, and C-C bending alkene,
respectively. The bands at 3361, 2362, 1612, 1521, 1446, and 1055 cm-1
correspond to O-H
stretching, C꞊O stretching, C꞊C stretching conjugated alkene, N-O stretching nitro compound, C-
H bending alkane, and CN stretching amine, respectively. The band intensities before and after the
reaction with silver nitrate have been measured. The untreated Tamarind aqueous seed crude
suggests some peaks, which reflex its complex nature. After treating with silver nitrate, the shift
in height 3361 cm-1
to 3822 cm-1
was determined. Except, the height shift from 1446 cm-1
to 1280
cm-1
. The peak of 1446 cm-1
is due to the absorption of NO3- at the surface of silver nanoparticles
[29]. Quinones, organic acids, flavones are water-soluble phytocompounds, these energetic
compounds are chargeable for a fast discount of the ions [30,31]. The bioactive compounds
evaluated inside the screening are liable for capping and stabilization of silver nanoparticles.
FIELD EMISSION SCANNING ELECTRON MICROSCOPY (FESEM) AND ENERGY-
DISPERSIVE X-RAY SPECTROSCOPY (EDAX)
FE-SEM is a surface imaging technique, capable of determining sizes, shapes, floor
morphology, and length distribution in micro (10-6
) and nano (10-9
) scales [32]. It's far being
analyzed that the TS-AgNPs is polydisperse and round formed as in Fig 4a. EDAX is a chemical
analysis technique blended with the FE-SEM to know the detail composition [33]. The detail
present in the inexperienced synthesis of silver nanoparticles became showed through the EDAX
spectrum. The principal emission peaks at 2.5 to 3.five KeV strongly depict the fundamental height
of metallic silver shaped by means of silver nanoparticles. The result is constant with the literature
values [34,35]. A vulnerable absorption top for oxygen, carbon, Cl, and Na is discovered. The bio-
molecule concerned produce the signal in X-ray emission which represents the steadiness of silver
nanoparticles [36]. The Si is found because of the glass wafer used for coating the nanoparticles.
This suggests the pure silver nanoparticles are discovered as in Fig.4b.

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X-RAY DIFFRACTION (XRD) ANALYSIS
XRD technique is used to the checkout crystal shape or polycrystalline-based material [37].
It measures particle size and crystallinity. The synthesized TS-AgNPs have analyzed the use of
XRD, and the crystalline nature is studied proven in Fig. 5. The XRD sample display, 4 awesome
peaks at 38.2, 44.5, 64.7, and 77.3 for marked indices of (111), (200), (220), and (311),
respectively, this confirms that the sample is metallic silver nanoparticles with face-targeted cubic
(FCC) crystal structure they matched with silver trendy information record JCPDS No.04-0783.
The roJCBt and broadening peaks mission that the silver nanoparticles is crystalline in nature and
small in length. The diffraction height (111) is greater excessive at 38.2˚ and is the preferred
orientation. Silver acts as the main peak. while different small peaks are because of bio-natural
compounds found in the leaf extract. with the aid of the use of the Debye-Scherrer technique the
implied crystalline size of silver nanoparticles became calculated D=okλ/β cosθ. The average
length of the particle is 58 nm.
ANTIOXIDANT ACTIVITY (DPPH METHOD IN-VITRO)
The antioxidant hobby is tested for numerous concentrations of TS-AgNPs from 20 µg/ml
to 100µg/ml. they're instead compared with ascorbic acid, which is a standard for antioxidant assay
here TS-AgNPs show great antioxidant assets but low compared with the ascorbic acid standard.
However, TS-AgNPs are non-toxic and have no aspect effect while as compared with ascorbic
acid. The IC50value is calculated as in Fig.6. and Table.1
CONCLUSION
The present work the synthesis of silver nanoparticles using the seed of Tamarind. The
silver nanoparticles are then characterized by means of numerous techniques such as UV-vis, FT-
IR, FE-SEM, EDAX, XRD. These bio compounds play a crucial role to synthesize the
nanoparticles after it is tested in the antioxidant assay in vitro and nanoparticles show the desirable
document for antioxidant assay. The particle size is 58 nm. Here we in the end say that this
technique is simple, eco-friendly, and is a herbal approach. This can be implied inside the drug
production.
CONFLICT OF INTEREST

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All of the authors declare that they do not have any competing financial interests or
personal relationships that could have appeared to influence the work reported in this paper.
Table 1: Free radical scavenging activities (DPPH method in vitro) of TS-silver nanoparticles
and standard were triplicated.
Concentration of plant extract
(µg/ml)
TS-AgNPs Ascorbic acid
20 56.32 86.35
40 59.23 87.56
60 60.40 90.34
80 64.72 93.57
100 79.18 96.23
IC50 Value 57.35 88.90
Figure.1. Color of the Reactions

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Figure.2. UV-visible spectral studies of Tamarind seed Silver Nanoparticles.
Figure.3.FT-IR of Tamarind seed Silver Nanoparticles.

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Fi
Figure.4a. FE-SEM of Tamarind seed Silver Nanoparticles and 3b. EDAX of Tamarind seed
Silverparticles.
Figure.5.XRD of Tamarind seed Silver Nanoparticles.
Position [°2Theta] (Copper (Cu))
20 30 40 50 60 70 80
Counts
0
50
100
MHP

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Figure. 6: DPPH free radical scavenging assay of TS-AgNPs were compared with the standard
measurements were done in triplicates (n=3, mean±SD) in a bar chart.
REFERENCES:
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CHAPTER – 3
S.PUSHPA 1 , E.MANIKANDAN 2 AND E.GOPINATHAN 3
2,3 DEPARTMENT OF PHYSICS, Dr.M.G.R. GOVERNMENT ARTS AND SCIENCE COLLEGE
FOR WOMEN, VILLUPURAM, TAMILNADU. INDIA.
ABSTRACT
In this work, Co-Codoped Fe-ZnO and mixed ZnO nanoparticles were synthesized via a
modified sol–gel method using water as unique solvent. The structural properties were analysed
by X-ray diffraction and the results showed the existence of wurtzite ZnO structure. The XRD
results also indicate phase segregation in the samples with more than 2mol% of Co. UV–Vis
diffuse reflectance spectra showed that Co-Codoped Fe-ZnO exhibited are d-shift of the band-edge
and a decrease in band gap energy, as compared to pure ZnO. The surface area increased
significantly with their on addition, and TEM analysis revealed hexagonal nanoparticles.
KEYWORDS: Nanoparticles, Sol-gel method, Co-Codoped, Fe-ZnO,ZnO.
INTRODUCTION
Currently, semiconductor nano structures have attracted great attention due to their unique
physical and chemical properties [1]. ZnO is an-type semiconductor, with a wide band gap energy
(Eg.3.37eV), and a large excit on binding energy, 60meV. It has important properties such as chemical
and thermal stability, low cost and environmentally safe [2,3]. ZnO also have excellent optical and
electronic properties, which have many applications in photo- catalysis, solar cells, gas sensors, and
even in sunscreens [4–7]. Transition metalions such as Cu2þ, Co2þ, and Fe3þ have been used as
dopants for ZnO, with the objective of modifying some of their properties [8]. In particular, iron has
been investigated in order to improve electrical, optical and magnetic properties. Studies have reported
the influence of iron doping in the structure of ZnO, revealing significant improvements in results
with the metal addition, in applications such as photo catalysis and gas sensors [9,10]. It is well known
that the properties of nanoparticles are sensitive to the condition so their preparation. ZnO has been
prepared by avariety of techniques such as hydro thermal method [11], comJCBtion [12] , co-
precipitation [13], and sol–gel method [14]. Among these, a modified sol–gel method [15] shows
some advantages over the other methods such as being simple, costeffective and uses only water as
solvent. This method was used in this work for ZnO and Co-Codoped Fe-ZnO synthesis. Their
structural, morphological and optical properties were characterized by XRD, TEM, diffused
reflectance spectroscopy and surface area determination. In recent years, semiconductor nanoparticles
“CO-FE-ZNO NANOPARTICLES SYNTHESIS VIA SOL–GEL TECHNIQUE”

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have attracted great interest [16–18]. This is stimulated mainly by physical probe into low-
dimensional systems and potential applications for this class of materials. They exhibit novel
electrical, mechanical, and optical properties due to quantum confinement effects compared with their
bulk counterparts and thus can be applied in many areas, including solar cells, luminescent devices,
chemical sensors, biological labeling and diagnostics. Due to its super electrical, mechanical, optical
and chemical properties. Zinc Oxide semiconductor has attracted a great deal of attention in the
material research field. Due to its significant optical and electronic properties, ZnO has been widely
used for the fabrication of various nano optoelectronic devices [19–22]. Now a day, the doping by
well-chosen impurities has been comprehensively discovered as a current technique to change the
ZnO nanostructures properties [23–27].
MATERIAL AND METHODS
About 1g of Zinc nitrate in 10 ml of water stirred then 1g of Poly ethylene glycol in 10 ml
of water is added this solution under maintain with continues stirring. About the solution then
added Cobalt nitrate, ferric nitrate solutions stirred well. The solution was maintained at room
temperature under stirring for 2h. Then the solution added 1:1 ammonia solution. A gel like a
solution is formed, this solution keep overnight and settled filtrate filtrated using water [28]. In
this way, Co-Codoped Fe-Zno nanoparticles containing different concentrations(1 and
10mol%)were prepared. The powder XRD (X-ray diffractogram) were obtained in a 2θ range of
10-80° employing a Eq inox 1000 diffractometer using Cu Kα rays at 1.5406 Å with a tube current
of 30 mA at 40 kV. An ESCA-3 Mark II spectrometer (VG scientific Ltd., England) using Al Kα
(1486.6 eV) radiation as the source was used for XPS (X-ray photoelectron spectra). Absorption
spectra of the samples in all solvents were found using Perkin Elmer Lambda-35
spectrophotometer (UV) and photoluminescence spectra (PL) were found with a Perkin Elmer LS-
55 spectrofluorimeter. Perkin Elmer Lambda 35 spectrometer with RSA-PE-20 integrating sphere
attachment was used to record the UV –vis diffuse reflectance spectra (DRS). Transmission
electron microscope (TEM) images at high resolution were recorded with a TEM using 200 kV
electron beam.
RESULTS AND DISCUSSION
The X-ray diffractogram pattern of the ZnO and Co-Codoped Fe–ZnO nanoparticles are
displayed in Fig 1. All the diffraction peaks are indexed to hexagonal ZnO of wurtzite structure
(JCPDS36-1451) [29]. The average grain size was calculated from Scherer’s formula using the 1
& 10% of Co-Codoped Fe–ZnOpeaks are 100, 002, 101, 102, 110, 103, 200, 112, 201. A base line
shift is observed in the 34–37° intervals for the samples containing 1mol% of Co, indicating
overlapping of peaks. On the other hand, for the Co 10Fe-Zn90 sample also emerges a peak at 32°.
Peaks at 2θ¼32° and 36° can explain the observed behavior. The average crystallite size (d) was
calculated for all samples by Debye–Scherrer’s equation. The average crystallite size deduced
from Debye–Scherrer equation [D=kλ/βCosθ, D - average crystal size; k - Scherer coefficient; λ
- X-ray wave length; θ - Braggs angle; β - full width at half maximum intensity] is 3.6 nm.The
average crystal size of pristine ZnO is 3.6 nm[30–32].
d =
0.9𝜆
𝐵𝐶𝑜𝑠𝜃𝐵
Fig. 2 shows representative TEM images taken from Co codoped Fe-ZnO. In wide-ranging,
Co codoped Fe-ZnO of 1 and 10% samples show similar morphologies. The images of the Co
codoped Fe-ZnO sample revealed that it presents tendency to hexagonal morphology, but sample
does not have a definite shape.The average size Co codoped Fe-ZnOof 3.6 nm good agreements

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with XRD and TEM results.
It is clear that iron addition changes the morphology of the particles. The oxidation state of the
constituent elements in the synthesized material was deduced using XPS (Fig. 3). XPS
measurements were made different concentrations of Co codoped Fe-ZnO (1 and 10mol%)were
prepared.The XPS spectra show the presence of Co codoped Fe-ZnO. Elemental analysis showed
thatthere was no evidence of the starting materials. The Co codoped Fe-ZnOcontains C, Fe, Co
and O species. The observed two symmetrical peaks α (527.2) and β (532.3) shows that O1s is
asymmetric and revealing two types of oxygen present in ZnO. Zn2p located at 1022.0 and 1045.1
eV shows the +2 oxidation of zinc in pristine ZnO, Fe2p1/2 and Fe2p3/2 located 725 and 708,
Co2p1/2 and Co2p3/2 located at 797 and 782.
The diffuse reflectance spectra (DRS) are presented in Fig. 4. The reflectance data are reported
as F(R) values obtained by the application of the Kubelka–Munk algorithm [F(R) = (1 – R)2/2R].
A red shift of the band gap with the incorporation of Co codoped Fe-ZnO has been observed and
interpreted as mainly due to the sp–d exchange interactions between the band electrons and the
localized d electronsoftheFe3þ ions substitutingZn2þ ions [33,34].The direct band gap (Eg)
energies of the Co codoped Fe-ZnOpowders were calculated from their diffuse-reflectance spectra
by plotting [F(R).hυ]n versus hυ.
Fig. 1.XRD patterns of the synthesized ZnO and Co codoped Fe-ZnO.

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Fig. 2.TEM images of Co codoped Fe-ZnO nanoparticles.
Fig 3.XPS of pristine Co codoped Fe-ZnO nanoparticles.

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Fig. 4.(a) DRS presenting the absorption edge; (b) with direct and (c) indirect bandgaps of
pristine ZnO and Ag-doped ZnO.
CONCLUSION
It was conceivable to synthesize nanostructured zinc oxide and iron doped zinc oxide by
sol–gel method. The ZnO could be doped with 1mol% of Co Codoped Fe-ZnO. Results indicate
that, most probably, with more than 1 mol% of Co, phase segregation occurs. The average
crystallite size of the samples decreased with an increase in the Co Codoping amount. The nano
particles showed an ear hexagonal shape. A decrease in the band gap energy was observed from
3.3 eV (ZnO) to 2.7 and 2.9eV (1% and 10% Co Codoped Fe-ZnO) and the surface are a increased
from 19(ZnO) to 52m2
g-1
.
CONFLICT OF INTEREST
All of the authors declare that they do not have any competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.

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CHAPTER – 4
E MANIKANDAN 1, S. PUSHPA 2
2, DEPARTMENT OF PHYSICS, Dr.M.G.R. GOVERNMENT ARTS AND SCIENCE COLLEGE FOR
WOMEN, VILLUPURAM, TAMILNADU. INDIA.
ABSTRACT
In this research on (0.00, 0.025, 0.045, 0.065 M %) Zn/SnO2 nanoparticles have been prepared
effectively chemical precipitation route on different doping concentration of zinc from 0.00 to
0.065%. XRD results showed the crystalline nature for different doping concentrations that are
existed as a tetragonal structure. HR-TEM investigation of pictures confirms the presence of very
little, homogeneously dispersed, and spherical shapes are observed. The crystallite size is
calculated using the Scherrer formula and was formed in the size of nanoparticles range from 11.6
nm to 42.0 nm. The presence of dopant (i.e. Zn) and arrangement of Sn to O phase and hydrous
nature of Zn/SnO2 nanoparticles are confirmed by EDX and FTIR (O-Sn-O stretching)
investigations. The band gap value is observed from 3.20 eV to 3.50 eV in undoped and Zn/SnO2
nanoparticles, Due to the large grain size. The grains size develops so deformity density decreases
and increases crystalllinity. These defects act as luminescent focuses and cause a decrease in
emission intensity and increase in the band gap. The cyclic voltammetry investigation is a
specific capacitance value, calculate as 496 F/g and 572 F/g was obtained at a scan rate 5
mV/s for undoped and (0.025 M %) Zn/SnO2 nanoparticles it’s suitable for supercapacitor
applications.
Keywords
Zn/SnO2, XRD, HR-TEM, UV-DRS, PL and CV
“OPTICAL, PHOTOLUMINESCENCE PROPERTIES AND SUPERCAPACITOR
APPLICATION ON ZN DOPING SNO2 NANOPARTICLES”

38 | P a g e
I INTRODUCTION
It has been generally acknowledged in current years that electrochemical supercapacitors (ECs)
are the most excellent possibility to give brilliant reversibility and high power density with a long
cycle life for novel energy applications for example, burst power generation, memory back up
devices with hybrid vehicles [1]. Therefore, the progress of proper electrode materials for ECs to
gather the condition for high power and long durability is attracting in a lot thought. In view of the
charge storage mechanism, electrochemical specific capacitance is separated into two types: (i)
Electrical Double Layer Capacitors (EDLC) that use the capacitance emerging from charge
separation an electrode/electrolyte interface and (ii) Pseudocapacitors that use the charge exchange
emerging from redox responses happening the surface of an electrode. Pseudocapacitors are large
generally examined on account of their high specific capacitance with high energy properties.
Since, pseudocapacitance emerges from the redox reaction of electroactive materials, transition
metal oxides [2, 3], and conductive polymers [4,5] with several oxidation states are considered
promising electrode materials for pseudocapacitors. Transition metal oxides electroactive
materials. The anhydrous ruthenium oxide, metal oxides has higher capacitance with superb
electrochemical reversibility [6]. SnO2 nanoparticles used in potential gas sensing [7], dye sensing
solar cells [8] and lithium-ion batteries [9]. It is well known that the regular ways to change the
characteristics of a material is by introducing dopants into the structure. Doping with metal added
substances (Al, Co, Fe and Cu) can prompt an increase in the surface region of SnO2 based powders
[10, 11], the balanced SnO2 surface, and advance a decreased grain size. In case, the Zn dopant
can reduce growth of crystallite and a main role in the electrochemical properties. The Sb doped
SnO2 nanocrystallites were prepared by sol-gel route and SC of 16 F/g got from a CV scan rate of
4 mV/s [12]. Similar authors have also studied the composite electrode of SnO2 and RuO, and a
specific capacitance value reported on 33 F/g of a scan rate 50 mV/s.
In this work, Zn/SnO2 nanoparticles have been prepared chemical precipitation route for
synthesizing Zn/SnO2 nanoparticles that are nearing quantum confinement effect and its structural,
optical and electrochemical properties are examined. For the best of our information, there are no
reports in the literature about to the utilization of Zn/SnO2 as electrode material for supercapacitor

39 | P a g e
application.
II. MATERIALS AND METHOD
Materials
Sigma Aldrich AR grade purchased in all chemicals and used without any further
purification. Aqueous solutions containing stoichiometries rates of the precursors for tin (IV)
chloride pentahydrate (SnCl4.5H2O), Zinc (II) chloride hexahydrate (ZnCl2.6H2O) and distilled
water were used in the followed synthesis process.
Synthesis of Zn/SnO2 nanoparticles
All Sn1-xZnxO2 (x=0.00, 0.025, 0.045 and 0.065) samples were prepared by chemical
precipitation route. For the synthesis of undoped and Zn/SnO2, we have used two precursors as:
(i) SnCl4. 5H2O and ZnCl2.6H2O (Precursor 1) and (ii) NH4OH (Precursor 2). The precursor 1 is
heated under constant stirring at 80o
C and Precursor 2 is added slowly, drop by drop in boiling
Precursor 1 the solution is pH maintained at 9. Then the resulting solution was heated constantly
for 2 hours until a White precipitate was formed which washed repeatedly with Distilled water and
ethanol and dried at 100o
C. The as-prepared samples were annealed at 700o
C in a furnace under a
continuous flow of O2 and N2 for 2hrs to obtain undoped and (0.025, 0.045, 0.065 M %) Zn/SnO2
Samples, respectively.
III. RESULT AND DISCUSSION
Phase Identification Analysis
X- Ray Diffraction patterns are shown in Fig.1. It is clear that the position of the peak is agreed
well with the reflection of rutile tetragonal structure of the SnO2 phase (JCPDS#72-1147) in the
all concentrations. Moreover, there is no additional peaks of the undoped and Zn/SnO2 at 0.025
molar ratios wherein the diffraction peaks can accredit as (110), (101), (200), (111), (211), (220)
plus (002) with no some additional phases detected indicating that the Zn ions be introduced by
the crystal lattice of SnO2. Moreover, the results indicate that Zinc ions obtain substituted at Sn
site no changing the cassiterite structure. Notwithstanding, the secondary phase of the hexagonal
is ZnO was distinguished at 0.045 and 0.065 Zn/SnO2 nanoparticles molar ratio [13] (JCPDS#89-
7102). It is watched peak position movements to higher 2θ as the ZnO molar ratio increments.

40 | P a g e
Table 1 depicts the outlines difference in crystallite size and change in the lattice parameters were
calculated from the peaks (110), (101) and (002) of Zn/SnO2. The results were shown by the lattice
parameters and the crystallite sizes were reduce with increasing Zn exchange ratio in table.1 [14].
As the concentration of doping increases, the intensity of the XRD peak decreases and the full
width half maximum increases, because of the degradation of crystallinity. This suggests that even
though Zn2+
ions occupy a normal Sn4+
lattice, it produces crystal defects about the dopants with
charge imbalance emerging from this defect changes the stoichiometry of the materials. The lattice
parameters for undoped and Zn/SnO2 nanoparticles were predicted from the formula
1
𝑑ℎ𝑘𝑙
2 =
ℎ2+𝑘2
𝑎2 +
𝑙2
𝑐2 (1)
Where a and c are the lattice parameters, h, k, and l are the Miller indices, and dhkl is interplanar
spacing for the plane (hkl). This interplanar spacing is calculated by this formula
2dhklsinθ = nλ (2)
Where, x-ray wavelength is λ, the angle of Bragg diffraction is θ, and n is order of diffraction
(n=1). The radius of ionic Sn4+
is 0.71 A˚, which is small, correlated to 0.74 A˚ for Zn2+
[15].
Because of this reality, the lattice distortion could be credited to Zn2+
replacing Sn4+
in the lattice.
Besides, the average crystallite size of the samples was calculated with Scherrer's formula
D = 𝑘𝜆
𝛽𝑐𝑜𝑠𝜃
(3)
Where D is the average crystallite size, 𝛽 is the FWHM, λ is the x-ray wavelength
(CuKα = 0.1546 nm), θ is the Bragg Diffraction angle, and k is a shape factor that is in use at 0.9.
As Table 1 is depicted by the calculated value of the crystallite size is reduced from 42 nm to 11.6
nm among the increase in the (0.025, 0.045, 0.065 M %) concentration of Zn doping [17].

41 | P a g e
Figure 1. XRD patterns of Undoped and Zn/SnO2 nanoparticles XRD
Table 1. Various structural and optical parameters of undoped and Zn/SnO2 nanoparticles.
Functional Group Analysis
Samples
Lattice parameter
a=b≠c (Tetragonal
structure)
Average
crystallite
size
(D) nm
Band gap Energy (eV)
a=b c
Pure
SnO2
4.7308 3.1836 22.0
3.50
Zn 0.025% 4.7350 3.1942 11.6
3.38
Zn 0.045% 4.7444 3.1936 40.0
3.32
Zn 0.065% 4.7549 3.1907 42.0
3.20

42 | P a g e
Figure 4. FTIR spectrum of SnO2 nanoparticles with different Zinc concentrations.
FTIR is a more sensitive technique as compared to the XRD characterization of phases and lattice
distortions. Fig.4 depicts the FTIR spectra recorded in the range 400 cm-1
to 4000 cm-1
in order to
confirm the phase purity of Zn/SnO2 nanoparticles annealed at 700◦
C. The observed broad peak at
531 cm-1
to 682 cm-1
range is due to possible vibrations of Sn-O and O-Sn-O modes [18, 20]. The
peak in 1229, 1366, 1630 and 1709 cm-1
are due to possible vibrations of Sn-OH and H2O modes
[19, 20] the broad peak at 3449 cm-1
and 3000 cm-1
is due to the possible vibrations of Sn-OH
mode. [21] The absorption peaks of CO2 mode assigned between 2300 -3000 cm-1
[22]. The
enhancement in band intensity and bandwidth indicates a reduction in particle size. [23] So the
size of the particles annealed at 700◦
C. The FTIR analysis strongly supports XRD and TEM
analyses.
UV-DRS studies
Optical property of a metal oxide semiconductor material is the key of one parameter
deciding its photoluminescence performance, which makes crucial the assurance of the optical
band gap Fig.5 shows the UV-Vis diffuse reflectance spectra of the undoped and (0.025, 0.045,
0.065 M %) Zn/SnO2 nanoparticles. All spectrums depict the intense absorption in the recorded
wavelength range 200-700 nm.
Figure 5. UV-vis DRS of undoped and Zn/SnO2 nanoparticles absorbance spectra.

43 | P a g e
Figure 6. Band gap evaluation from the plots of (Ahυ)2
versus Photon energy (hυ) of undoped
doped and Zn/SnO2 nanoparticles.
The inception of the absorption bands, displays a red-shift when the doping concentration
increments from 0.000 to 0.065 at %. To obtain more quantitative insight in the optical properties
as the function of Zn content, the band gap energy value (Eg), for every Zn/SnO2 nanopowders
was predictable for the intersection point of the line tangent of the Tauc’s curve inflection point
with the horizontal energy axis (Fig.6) utilizing the following Tauc’s plot formula [24] behind
assuming that cassiterite SnO2 was a direct bandgap semiconductor [25].
2
– Eg)
Where is the absorption coefficient (or optical density), Eg stands for band gap energy, v is the
light frequency and A is constant. The band gap energy values then changed from 3.50 eV for
undoped SnO2 to 3.20 eV for 0.065 M% Zn/SnO2, the electromagnetic spectrum is corresponding
to the near ultraviolet-violet region. Thusly, the increase in the Zinc doping concentration brings
on liberal decreases of the band gap value. This was ascribed to the part of a secondary phase ZnO
[26]. Still, no confirmation of the arrangement of ZnO was found in the case of Zn/SnO2
nanopowders prepared by the chemical precipitation route. Therefore, the less energy band gap of
the values can more probable ascribe to the melting of an impurity band into the conduction band,
hence the decreasing bandwidth and the occurrence of Zn2+
in the Sn4+
in the cation sites. Hence
Zn2+
was really integrated in the system and determined the semiconducting properties of the

44 | P a g e
material [27] which is completely steady with the literature data described for doped SnO2
nanopowders [28,29] Regardless, the magnitude of the red shift observed with Zinc as dopant is
much less pronounced than that observed with before transition metal ions as cobalt (Co) or
vanadium (V) where the energy gap decrease was rationalized based on s-d and p-d exchange
interactions among the band electrons of SnO2 and the localized d electrons of the transition metal
ions substituting Sn4+
ions [30,31]. At the last moment, these results are find out the new pathways
in photoluminescence, which is the modifications of Zinc content led to a fine tuning of the optical
absorption of these materials.
D. Photoluminescence Studies
At the Room temperature PL emission spectra are excited at the wavelength of
251 nm of Zn/SnO2 nanoparticles with the different concentrations of Zn is recorded. As show in
Fig.7, each nanoparticle has a similar peak at around 326 nm. Prepared SnO2 nanoparticles
additionally distinguish a PL peak at around 326 nm [32]. At the point when the Zn concentration
increased to 0.025at %, the two new broad peaks at around 353 nm and 368 nm are observed.
Additionally, increasing the Zinc concentration to 0.065 M%, the intensity of these new peaks is
increased.
Figure 7. PL spectrum of undoped and Zn/SnO2 nanoparticles.
Generally, oxygen vacancies are the normal defects in the metallic oxides which further go

45 | P a g e
in luminescent procedures at the center of the radioactive [33]. They are three different charge
state were occurring in the oxygen vacancies that are V0
0
, V0
+
and V0
2+
[34]. Among these charged
states, V0
0
is a shallow benefactor, which lies almost to the conduction band. It is believed that
most of the oxygen vacancies are likely in the V0
2+
state [35]. Since ionic radius of Zn2+
(0.074
nm) with Sn4+
(0.069 nm) are equivalent, Zn can simply substitute for Sn4+
in host lattice. The lack
V0
2+
charge can be seen as an oxygen vacancy. As it were, the incorporation of Zn2+
into SnO2
lattice can make oxygen vacancy V0
2+
. As a result, two novel new peaks in PL medium around
351 - 369 nm are showed because of Zn doped. In general, the PL medium observed red shift could
be attributed. It has been accounted that the PL spectra at the red shift can be attributed to the effect
of compressive stress [36, 37]. Even though Zn/SnO2 nanoparticles are in a tensile stress state
(c<co), Zn2+
ions compared with Sn4+
ions, which subsides the impact of tensile stress state in
films [38]. So the progressions of the PL spectra with the different concentration of Zn propose
that the defects and stress influenced by Zn doping assume a vital part in the photoluminescence
behavior.
E. Electrochemical Properties Analysis
The CV measurements were made in investigating electrochemical performances.
Although the compound electrochemical efficiency of cyclic voltammetry measured the potential
window between -1.6 and 1.5 mV/s shows in Fig.8. The undoped and Zn/SnO2 nanoparticles were
tested as the electrode to evaluate the improved electrochemical performance in a three electrode
system. It exhibits Quasi Rectangular Shape of the cyclic voltammetry curve with scan rate 5-100
mV/s. The specific capacitances were synthesized by undoped and Zn/SnO2 nanoparticles can be
calculated by using this formula [39, 40].
Cs = 𝑄
∆v.m
Here, Cs stands for the specific capacitance, Q for anode charge and m is the mass of the prepared
electrode material and ∆v scan rate. The estimate of electrochemical is completed at 0.2 M
C16H36ClNO4 with a standard three electrode designs comprising of a sample working electrode,
an Ag/AgCl is reference electrode and a platinum wire is counter electrode [41]. Table.2 shows
the specific capacitance value of undoped and Zn/SnO2 nanoparticles. A specific capacitance value
of 497 F/g and 572 F/g was obtained for the undoped and (0.025%) Zn/SnO2 nanoparticles at a

46 | P a g e
lower scan rate of 5 mV/s. The capacitance value is high which is due to the high crystallinity of
the product and the zinc doping, as well as the porous morphology [42], confirmed by XRD and
TEM. The mobility of the charge carriers and the crystallinity of the products increased due to zinc
doping as well as increase the capacitance.
The obtained capacitance values of SnO2 synthesized by other wet chemical techniques were
higher than reported values [43]. The changes in specific capacitance while raising the scan rate
owing to the pseudocapacitance nature of the undoped and Zn/SnO2 nanoparticles. A faradic
reaction, it indicates that the lower scan rate in the ionic diffusion happens only in the inner (core)
and an outer surface of the material. Whereas, increase the scan rate in the ionic diffusion it
happens just only the outer surface of the nanoparticles [44]. From the cyclic voltammetry study,
the reduction peaks are appearing at 0.4 and 0.8 V and oxidation peak at 0.7 V was obtained.
When scan rate increases the curve also changed which is represented good capacitance
performance and good reversibility of Zn/SnO2 nanoparticles. The Zn/SnO2 nanoparticles is
annealed at 700o
C with high specific capacitance value of 572 F/g this gives a better report than
previous work Co2SnO4/activated carbon was specific capacitance value 285 F/g by sol–gel route
[45]. The scan rate increases with a capacitance values decrease, which is the behavior of
electrochemical systems.
The most important factors influencing the entire specific capacitances difference with scan rate
are:
(i) the increase scan rate with decreasing in specific capacitances was assigned to the reduced
diffusion rate of the ions in the pores at higher scan rates. The increase in scan rate directly reduced
the ion diffusion, since at high scan rates the ions approach only the outer surface of the electrode
material.
(ii) The surface adsorption process at high scan rates. This is based on the diffusion effects of the
proton within the electrode material. Hence, it is held that part of the surface of the electrode
materials contributes to a high charging/discharging rate, which decreased the specific capacitance
at higher scan rates [46].

47 | P a g e
Figure 8. CV curve of specific capacitance on Undoped and Zn/SnO2 nanoparticles.
Figure 8 (b). Dependence of specific capacitance as a function of scan rate of undoped and

48 | P a g e
Zn/SnO2 nanoparticles.
Table.2. The different scan rate and their specific capacitance value of pure and Zn/SnO2
(0.025%, 0.045%, 0.065%) annealed at 700◦
C.
IV. CONCLUSIONS
Undoped and (0.025, 0.045, 0.065 M %) Zn/SnO2 nanoparticles are synthesized via chemical
precipitation route it annealed at 700o
C. The XRD analysis were confirmed the undoped and 0.025
M % Zn/SnO2 nanoparticles is tetragonal structure and secondary phase of the hexagonal is ZnO
was distinguished at 0.045 and 0.065 M % Zn/SnO2 nanoparticles, the average crystallite size
ranges from 11.6 to 42.0 nm. SEM studied the synthesized product with an agglomerated spherical
shape. EDX confirmed that the prepared samples are only containing Zn, Sn, and O with no
different impurities. The average grain size of a TEM picture ranges from 12.5 to 22.5 nm which
concurs with the average crystallite size ~11.6 nm calculated by Scherrer’s equation from XRD
pattern. The observed photoluminescence of the nanocomposites is attributed to electron transfer
by lattice defects and oxygen vacancies. The superior specific capacitance value of 596 F/g was
gotten at the scanning rate of 5 mV/s. We believe that too easy process and specific capacitance
Scan Rate
(mVs-1
)
Specific Capacitance (Fg-1
)
Pure SnO2 Zn/SnO2
(0.025%)
Zn/SnO2
(0.045%)
Zn/SnO2
(0.065%)
5 496 572 542 507
10 247 254 236 228
30 180 204 196 182
50 119 147 135 120
100 94 106 98 96

49 | P a g e
performance also clears a way to prepare stable 0.025% Zn/SnO2 nanoparticles, which is
predictable survive a potential possibility for supercapacitor applications.
Disclosure of interest
The authors declare that they have no conflicts of interest concerning this article.
ACKNOWLEDGEMENTS
The authors wish to thank Centralized Instrumentation and Services Laboratory (CISL),
Annamalai University, Annamalai Nagar, Tamilnadu, India and Sophisticated Analytical
Instrumentation Facility (SAIF), Cochin, Kerala, India for providing their analytical instrument
facilities.
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CHAPTER – 5
Dr. G. AYYANAR
ASSISTANT PROFESSOR
DEPARTMENT OF COMMERCE WITH CA
KAMMAVAR SANGAM COLLEGE OF ARTS AND SCIENCE THENI.
ABSTRACT
There is a perceived pattern of assembling administrations and, surprisingly, complete answers
for Business issues. Research has featured financial, market interest and intensity factors as
answerable for the re-molding of Business systems that this has involved. This study investigations
the degree to which another element, innovation, has been a huge calculate the switch from item
situated to support arranged procedures. The impact of technology is examined through a case
study of Mahindra Thar, a manufacturer of aircraft engines, and it is discovered that manufacturers
have altered their Business strategies. That's what the investigation discovers improvements in a
single innovation specifically, in particular computerized gadgets, have been a strong empowering
factor working with the execution of administration methodologies. By allowing them to acquire
new knowledge management capabilities, this gave original equipment manufacturers (OEMs)
like Mahindra Thar a competitive advantage over conventional service providers.
KEY WORDS: Advancement, Administrations, Innovation, Technique, Business Aviation
INTRODUCTION
The administration writing encouraging producers to make the progress from providing items to
providing items and administrations on a coordinated premise is very broad (Oliva and Kallenberg,
2003). Concerning industry areas, the writing that has featured and dissected this pattern stretches
out for the most part to capital products, including processing, cranes, trains, and aviation
(Howells, 2004). The rationale behind producers pushing ahead along the worth chain to
incorporate the arrangement of administrations as well as assembling is typically credited to three
principal determinants: financial variables, request conditions and upper hand (Oliva also,
Kallenberg, 2003). The financial contention is that through the arrangement of administrations
extra income can be created from an introduced base of items particularly on the off chance that
“A STUDY ON MAHINDRA THAR CARS TECHNOLOGY AND BUSINESS STRATEGY IN
CURRENT SCENARIO”

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the item life cycle is a long one with items staying in help for numerous years. To build up this it
is additionally called attention to that administrations normally yield essentially higher edges than
items. The case for request conditions is that as clients become more modern so their assumptions
rise and they request arrangements as opposed to simple items. Frequently this is set as far as the
pattern towards more prominent specialization and out of here the piece of many organizations to
re-appropriate administrations as they center around center capabilities (Pralahad and Hamel,
1990). At long last the case for intensity lays on the idea that
Administrations are in many cases more hard to mimic and thusly are a wellspring of likely upper
hand. Anyway not very many investigations have checked elective viewpoints out. The authors
(Johnstone et al., 2009) pinpoint two of these three factors, namely economic factors in the form
of cost pressures, and the need to provide a stronger customer focus given the trend towards greater
use of outsourcing. They also note that implementation creates challenges in terms of gaining
employee involvement and the integration of different parts of the organisation. A study by Ward
and Graves (2007) surveyed eleven companies operating in different parts of the aerospace supply
chain. It identified four factors behind servitization.
Ward and Graves (2007) also highlighted the importance of information systems in implementing
this kind of service. Significantly Ward and Graves (2007) like Johnstone, Dainty and Wilkinson
(2009) said little about the part played by technology, in providing information in the first place.
In contrast, an earlier study by Lorrell et al. (2000), while noting the importance of the factors
identified by Oliva and Kallenberg (2003), also cited industry specific institutional changes, in the
form of market de-regulation, as an important driver particularly in the commercial sector.
Ivey (2001) comes close because it did note how increased knowledge of product performance is
becoming available through the development of engine health monitoring systems. However the
study said nothing about the technologies that are helping to make this possible. In the light of this,
and the way in which studies of servitization in aerospace like studies of other sectors have ignored
advances intechnology, as a possible factor behind servitization, this study seeks to rectify this
omission.
REVIEW LITERATURE
Manish Kumar Srivastava, A.K. Tiwari investigates consumer behaviour in Jaipur for A3 segment Jcbs
such as the Honda City and SX4. Data was taken from 100 people, 50 from Honda City and 50 from Maruti
SX4. Gender, occupation, and income class were all taken into account when selecting respondents. Price,
Safety, Comfort, Power & Pickup, Mileage, Max Speed, Styling, After Sales Service, Brand Name, and
Spare Parts Cost are also studied as customer purchasing parameters. Based on the above parameters and
the analysis conducted, it was discovered that when buying an A3 segment vehicle, customers place a high
value on safety, brand recognition, and seating and driving comfort.

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