EFFECT OF SILVER NANOPARTICLES ON TOMATO PLANTS AND 
 
DEVELOPMENT OF A PLANT MONITORING SYSTEM (PMS) 
 
 
 
 
LeVar Marquel Odum 
 
 
 
 
 
 
A Thesis 
 
Submitted to 
 
the Graduate Faculty of 
 
Auburn University 
 
in Partial Fulfillment of the 
 
Requirements for the 
 
Degree of 
 
Master of Science 
 
 
 
 
 
 
 
 
 
Auburn, Alabama 
December 17, 2007 
 
EFFECT OF SILVER NANOPARTICLES ON TOMATO PLANTS AND 
 
DEVELOPMENT OF A PLANT MONITORING SYSTEM (PMS) 
 
 
 
 
 
Except where reference is made to the work of others, the work described in this thesis is 
my own or was done in collaboration with my advisory committee. This thesis does not 
include proprietary or classified information. 
 
 
________________________________ 
LeVar Marquel Odum 
 
 
 
Certificate of Approval: 
 
 
 
______________________     _______________________ 
Aleksandr Simonian       ZhongYang Cheng, Chair 
Professor                   Associate Professor 
Materials Engineering       Materials Engineering 
 
 
 
______________________     _______________________ 
Bryan A. Chin        Bart Prorok 
Department Chair/ Professor                            Associate Professor 
Materials Engineering       Materials Engineering 
 
 
 
 
________________________ 
George T. Flowers 
Interim Dean 
Graduate School 
 iii
EFFECT OF SILVER NANOPARTICLES ON TOMATO PLANTS AND 
 
DEVELOPMENT OF A PLANT MONITORING SYSTEM (PMS) 
 
 
 
 
 
 
LeVar Marquel Odum 
 
 
 
Permission is granted to Auburn University to make copies of this thesis at its discretion, 
upon request of individuals or institutions and at their expense. The author reserves all 
publication rights. 
 
 
 
 
______________________________ 
Signature of Author 
 
 
 
 
 
______________________________ 
Date of Graduation 
 
 
 
 
 
 
 
 
 
 
 
 iv
VITA 
 
 
 LeVar Marquel Odum, son of Tommy and Magdalene Odum, was born on July 
  
25
th
, 1977, in Tuscaloosa, Alabama.  He attended the University of Alabama at  
 
Birmingham in September 1997 and graduated with a Bachelor of Science in Electrical  
 
Engineering in June 2001. In 2005, he entered Graduate School at Auburn University to 
pursue a Masters degree in Materials Engineering. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 v
THESIS ABSTRACT 
 
EFFECT OF SILVER NANOPARTICLES ON TOMATO PLANTS AND 
 
DEVELOPMENT OF A PLANT MONITORING SYSTEM (PMS) 
 
 
LeVar Marquel Odum 
 
Master of Science, December 17, 2007 
(B.S.E.E, University of Alabama at Birmingham, Birmingham, 2001) 
 
76 Typed Pages 
 
Directed by Zhong Yang Cheng 
 
 
 
 Smart Agriculture will determine whether farmers in the US can compete 
internationally. Engineering plays a significant role in Smart Agriculture through the 
development of sensor networks, data acquisition, and control systems that will help 
advance the fundamental understanding of food quality and plant growth. In this thesis, 
two issues related to engineering in Smart Agriculture are studied: 1) the influence of 
nano-materials on plants; and 2) the development of a plant monitoring system.  
The influence of silver (Ag) nanoparticles on tomato plant growth is studied. 
Silver nanoparticles have become a significant tool in the development of novel 
biological and chemical processes.  Silver nanoparticles have unique physical and 
chemical properties, such as the ability to kill or impede growth of bacteria and 
 vi
microorganisms such as E. coli.  With the growth of silver nanoparticles usage, it is only 
a matter of time before silver nanoparticles are heavily distributed into the ecosystem. For 
that reason, it is critical to study the long-term effects of silver nanoparticles on our food 
supply, such as plants and crops.  
In this study, tomato plants are grown hydroponically, in solution with known 
concentrations (1.97x10
13
 and 1.57x 10
11
) of silver nanoparticles. Silver nanoparticles 
ranging in sizes from 10 nm to 50 nm were investigated. The uptake or absorption of the 
silver nanoparticles by the tomato plants was monitored using a Ultra-Violet/vis 
Spectrophotometer. Material characterization technologies, such as X-ray Diffraction, 
Raman Spectroscopy, and Energy Dispersive Spectrometer were then used to determine 
the location of the silver nanoparticles in the plants. The growth of plants was visually 
observed and recorded. Based on experimental results, it was concluded that tomato 
plants uptake the silver nanoparticles from the hydroponic solution and these silver 
nanoparticles led to the death of the plants. It was also found that the smaller the silver 
nanoparticles, the quicker the tomato plants die. 
This thesis also investigated the use of sensors, data acquisition systems, and 
wireless networks to monitor the growth and health of plants.  A plant monitoring system 
was designed and fabricated to monitor plants.  This system was designed to operate over 
a wide temperature range, have low-power consumption, and be robust in nature. A 
multitude of sensors was wired to the data acquisition system. Bluetooth, the wireless 
protocol of choice, was then used to communicate the information from the data 
acquisition system to a remote computer monitoring station. The system monitored and 
recorded temperature, humidity, soil moisture, and ripeness of fruit and/or vegetables.
 vii
ACKNOWLEDGEMENTS 
 
 
 The author would like to express his sincere thanks and gratitude to his major 
professor, Dr. ZhongYang Cheng, for his guidance and support during his graduate 
studies.  He would also like to thank Dr. Bryan Chin for his advice and guidance through 
his graduate degree. Thanks are also due to Dr. Aleksandr Simonian for his helpful 
suggestions. Thanks are due to Joseph Wu for his help in nanoparticles synthesis. 
 The author would also like to thank Dr. Bart Prorok for all his advice and 
suggestions during his research.  Appreciation is due to Dr. Clyde Wikle, L.C. Mathison, 
Antoine Harvis, and Shirley Lyles for all their help. 
 The author would also like to thank his wife (Nicole), his parents and his siblings 
for their love and support. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 viii
Style manual or journal used:  Auburn University Guide To Preparation 
 
     And Submission Of Thesis And Dissertations 
 
Computer software used: Microsoft Word XP, Microsoft Excel, Orcad 
Capture, Orcad Layout, Origin 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 ix
TABLE OF CONTENTS 
LIST OF FIGURES ........................................................................................................... xi 
LIST OF TABLES........................................................................................................... xiv 
1. EFFECT OF SILVER NANOPARTICLES ....................................................................1 
            1 Properties of Silver Nanoparticles ........................................................................1  
                        1.1.1 Physical Characteristics ......................................................................1  
                        1.1.2 Toxic Effects of Silver Nanoparticles.................................................3 
            1.2 Phytoremediation versus Nanoparticle Uptake..................................................4 
2. OBJECTIVES OF RESEARCH......................................................................................7 
3. SYNTHESIS OF SILVER NANOPARTICLES.............................................................8 
4. EXPERIMENTS............................................................................................................11 
             4.1 Detection of silver nanoparticles ....................................................................11 
                        4.1.1 X-ray Diffraction (XRD) ..................................................................11 
                        4.1.2 Energy Dispersive Spectrometer (EDS) ...........................................15 
                        4.1.3 Raman Spectroscopy.........................................................................16 
                        4.1.4 Ultra-Violet Spectrophotometer (UVS)............................................19 
            4.2 UVS Sample Preparation .................................................................................20 
                        4.2.1 Citrate/Borohydride Control Test .....................................................23 
 
 x
5. RESULTS AND DISCUSSIONS..................................................................................25 
5.1 Experimental Results .......................................................................................25 
5.2 Conclusions......................................................................................................32 
6. FUTURE WORK...........................................................................................................34 
7. PLANT MONTORING SYSTEM ................................................................................35 
8. OBJECTIVES OF RESEARCH....................................................................................37 
9. CIRCUIT DESIGN........................................................................................................38 
            9.1 Microcontroller Selection ................................................................................39 
            9.2 Wireless Communication Selection.................................................................40 
            9.3 Board Layout ...................................................................................................45 
            9.4 Software ...........................................................................................................49 
10. SENSING NETWORK................................................................................................51 
11. EXPERIMENTS..........................................................................................................52 
            11.1 Set-up .............................................................................................................52 
12. RESULTS AND DISCUSSIONS................................................................................56  
             12.1 Conclusion ....................................................................................................59 
13. FUTURE WORK.........................................................................................................60 
BIBLOGRAPHY ...............................................................................................................61 
 
 
 
 
 
 xi
LIST OF FIGURES 
 
 
 
Fig.1.1.1 Logarithmic plot of spherical particle radius versus surface area-to-  
   volume ratio??????????????????...???..??2  
 
Fig. 1.1.1.2    Diagram illustrating Ag particles sizes versus active sites for 
microorganism attachment...........................................................................3 
 
Fig.1.1.2.1       TEM images of P. aeruginos samples.........................................................4 
 
Fig.1.1.2.2       Diagram of phytoremediation versus nanoparticle uptake illustration........6 
 
Fig.3.1             SEM image of 50 nm Ag nanoparticles.......................................................9 
  
Fig.3.2 a) TEM image of 50 nm Ag nanoparticles and b) TEM image of 10 nm Ag 
nanoparticles .............................................................................................10 
 
Fig. 4.1.1.1      Schematic of a typical XRD powder diffraction (Siemens manual) .........12 
 
Fig. 4.1.1.2      XRD plots of the first 2? peaks for a) 10 nm and b) 50 nm sized silver 
                        nanoparticles ..............................................................................................12     
   
Fig. 4.1.1.3      Tomato plants after two weeks for XRD...................................................14 
 
Fig. 4.1.1.4      The XRD plots for the tomato plants roots (10 nm and 50 nm)................14 
 
Fig. 4.1.3.1      Schematic of Raman Spectroscopy ...........................................................16 
 
Fig. 4.1.3.2      Raman spectra for citrate synthesized silver nanoparticles.......................17 
 
Fig. 4.1.3.3      Raman spectra for leaves (top) and stems (bottom) of tomato plants .......18 
 
Fig. 4.1.4.1      UVS/vis operation schematic diagram ......................................................19 
 
 
 
 xii
Fig. 4.2.1    a) Tomato plant in a cuvette filled with 1 mL of citrate solution of 50 nm 
silver nanoparticles and b) Tomato plant in a cuvette filled with 1 mL of 
borohydride solution of 10 nm silver nanoparticles ..................................21 
 
Fig.4.2.1.1       Schematic of citrate/borohydride control test............................................23 
 
Fig. 4.2.1.2 a) UVS Spectra of pure citrate solution b) UVS Spectra of pure 
borohydride solution ..................................................................................24 
 
Fig. 4.2.1.3 a) UVS spectra of 50 nm silver particles in plant immersed citrate solution 
using the sequence shown in Figure 4.2.1.1. b) UVS spectra of 10 nm 
silver particles in plant immersed borohydride solution using the sequence 
shown in Figure 4.2.1.1..............................................................................24  
 
Fig. 5.1.1 Cycle 1, UVS spectra of tomato plant samples in 10 nm and 50 nm Ag  
                        nanoparticles solutions and reference solutions.........................................26 
 
Fig. 5.1.2 Cycle 2, UVS spectra of tomato plant samples in 10 nm and 50 nm Ag  
                        nanoparticles solutions and reference solutions.........................................27 
 
Fig. 5.1.3 Cycle 3, UVS spectra of tomato plant samples in 10 nm and 50 nm Ag  
                        nanoparticles solutions and reference solutions.........................................28 
 
Fig. 5.1.4 Cycle 4, UVS spectra of tomato plant samples in 10 nm and 50 nm Ag  
                        nanoparticles solutions and reference solutions.........................................29 
 
Fig. 5.1.5 Cycle 5, UVS spectra of tomato plant samples in 10 nm and 50 nm Ag  
                        nanoparticles solutions and reference solutions.........................................30 
 
Fig. 5.1.6         Image of samples and reference ................................................................31 
 
Fig. 5.1.7     Raman spectra of leaves after UVS cycle 5 with 0.1% power of 514 nm  
                        light source.................................................................................................32 
 
Fig. 5.2.1.        Slopes of 10nm Ag nanoparticles uptake in tomato..................................33 
Fig. 5.2.2.        Slopes of 50nm Ag nanoparticles uptake in tomato..................................33 
 
 
 
 
 
 
 
 xiii
Fig. 7.1            Illustration of the concept of smart agriculture with wireless  
                        communication...........................................................................................36 
 
Fig. 9.1            Block diagram of PMS ..............................................................................38 
 
Fig. 9.2.1         Comparison of the different wireless protocols.........................................43 
 
Fig. 9.2.2         Bluetooth Module......................................................................................43 
 
Fig. 9.3.1         PMS prototype board.................................................................................46 
 
Fig. 9.3.2         PMS circuit board schematic drawing.......................................................47 
 
Fig. 9.4.1         PMS C-programming flow-diagram..........................................................50 
 
Fig. 11.1.1       Experiment Setup for PMS testing ............................................................53 
 
Fig. 11.1.2       Screen print of HyperTerminal capturing PMS output data......................54 
 
Fig. 11.1.3      Environmental chamber used for temperature test.....................................55 
 
Fig. 12.1         Graph of PMS soil readings........................................................................58 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 xiv
LIST OF TABLES 
 
 
 
Table 3.1         Size and concentration of silver nanoparticles solutions...........................10 
 
Table 4.1.2.1   EDS scans on the leave, stem, and root of tomato plant in 10 nm  
                        nano-silver..................................................................................................15 
 
Table 4.2.1      Refills performed in experiment................................................................22 
 
Table 9.1.1      List of selected low power microcontrollers..............................................40 
 
Table 9.2.1      List of wireless technology modules and their parameters........................44 
 
Table 9.3.1      PMS power consumption...........................................................................46 
 
Table 9.3.2      PMS Bill of materials ................................................................................48 
 
Table 12.1      Temperature and humidity data recorded (PMS and reference).................57 
 
Table 12.2      Results of temperature extreme operation test............................................58 
 
 
 
 
 
 
 
 
 
 
1 1
1. EFFECT OF SILVER NANOPARTICLES 
 
1.1 Properties of Silver Nanoparticles 
 Nanoparticles are utilized in cosmetic, medical and military applications due to 
their unique chemical and physical properties. The unique properties of these 
nanomaterials are highly dependent upon their size and shape.  Nanometer sized particles 
tend to exhibit very different properties than their bulk scaled counterparts. The small 
size of the silver nanoparticles is reported to improve the antibacterial properties of silver 
in addition to improving chemical durability (Toshikazu, 1999). The antibacterial 
properties of silver nanoparticles have led to several new products such as nanoparticles 
embedded clothes dryers sheets to reduce clothing (socks) odors and antibacterial soap 
and toothpaste. 
 
1.1.1 Physical Characteristics 
 For silver nanoparticles, the enhanced properties are due to the large surface area-
to-volume ratio. This larger ratio significantly increases the antibacterial effects by 
allowing easier interaction with other particles (Lee et al., 2004). 
 The volume, V, of spherical particles is defined as:  
V= (4/3) ?r
3
                               (1.1) 
 
2 2
and the surface area, SA, is given by: 
SA = 4 ?r
2
                                 (1.2) 
where r is the particle?s radius. 
Therefore, the surface area-to-volume ratio is given by: 
(SA/V) = 3/r                          (1.3) 
From this equation it is evident that the surface area-to-volume ratio is inversely 
proportional to the particle radius. Figure 1.1.1.1 plots this relationship.  As the particle 
size decreases, the number of active surface sites increases, relative to inactive, internal 
sites, enhancing the material properties. The larger particle internal active sites are not 
exposed to forces or reactions, reducing the particles material properties. Figure 1.1.1.2 
displays a diagram illustrating particles? size versus active sites for microorganism 
interaction. 
 
Figure 1.1.1.1. Logarithmic plot of spherical particle radius versus surface area-to-
volume ratio 
 
 
0.01
0.1
1
10
11010
log particle radius (nm)
lo
g S
A
/
V
 
r
a
t
i
o (
n
m
-1
)
3 3
 
Figure 1.1.1.2. Diagram illustrating Ag particles size versus active sites for 
microorganism attachment 
 
1.1.2 Toxic Effects of Silver Nanoparticles 
 It has been well known for thousands of years that silver ions and silver-based 
compounds are highly toxic toward many microorganisms and have shown fatal effects to 
many of bacteria (Sondi and Sondi, 2004). Recent studies have also demonstrated silver 
nanoparticles? ability to attach to human immunodeficiency virus (HIV-1), hindering the 
virus? ability to bind to the host cells (Elechiguerra et al., 2005).   
 The exact mechanism by which silver nanoparticles destroy and prevent pathogen 
growth is not well understood. There are many possible mechanisms discussed by 
researchers but the exact mechanism has not been confirmed. One researcher states that 
in order to understand the antibacterial mechanism of silver nanoparticles, one has to first 
understand how bacteria and viruses live and grow. Bacteria are believed to use an 
enzyme to metabolize oxygen to sustain life. Silver ions cripple the enzyme and stop the 
metabolization of oxygen. This suffocates the bacteria, resulting in its death. Viruses 
grow by invading living cells and then reproducing replicas of itself. As part of the 
process, the cell relies upon oxygen metabolizing enzymes. Again, silver ions kill the 
virus-producing cell by suffocation (Puebla et al., 2004). Another study suggests that 
monovalent silver ions (Ag
+
) replace hydrogen cations (H
+
) of sulfhydryl or thiol (S-H) 
groups on the surface of cellular membranes. This disables the production of proteins 
4 4
require to sustain the cell, resulting in eventual death of the cell (Feng and Wu et al., 
2000). However, one study shows that not only the surface of the cell is affected but the 
inside of the cell is also exposed to the effects of the silver nanoparticles (Morones et al., 
2005). Figure 1.1.2.1 shows (a) Control cells of P. aeruginosa and (b) Cells of P. 
aeruginosa disrupted by silver nanoparticles. 
 
Figure 1.1.2.1. TEM images of P. aeruginos samples. (Morones et al., 2005) 
Figure 1.1.2.1 (b) shows silver nanoparticles on the surface as well as inside the P. 
aeruginosa cells.   
 Despite the uncertainty of the exact mechanism of interaction, and that very little 
is known regarding the effects of silver nanoparticles on the environment and human 
health, silver nanoparticles are still being widely used because of their toxicity toward 
microrganisms. 
 
 
5 5
1.2 Phytoremediation versus Nanoparticle Uptake 
 Contaminated soil and water poses a great threat to vegetation and a dangerous  
health risk for people around the world. Heavy metals ions at high concentrations are one 
of the most harmful contaminates, however, they are ubiquitous on earth. At normal 
concentrations (less than 1000 parts per million (ppm)) most heavy metal ions are 
harmless to living organisms (Gardea-Torresdey et al., 2005).   
Although there are several current technologies that can be used for cleaning sites 
contaminated with heavy metals, they tend to be expensive and labor intensive. Natural 
remediation by plants has recently become a possible alternative. One of the promising 
alternative methods, phytoremediation uses plants to purify the water and soil 
contaminated with heavy metal ions, such as silver.  During phytoremediation, metallic 
ions are absorbed by plants.  Accumulation of these ions eventually leads to the 
synthesis/formation of metal nanoparticles inside the plant?s tissues (Gleba et al., 1999). 
However, to the best of our knowledge, the mechanism by which the plants absorb 
nanoparticles of silver is not well known. The possible effects of silver nanoparticle 
contamination of food, such as vegetables, must be studied since vegetation may uptake 
nanoparticles from contaminated soil and water.   
In phytoremediation, the synthesis of metal nanoparticles in plant tissue results in 
very high concentrations of the nanoparticles in the plant (Gardea-Torresdey et al., 2005).  
However, if previously formed nanoparticles are absorbed by a plant, it is assumed that 
the tissue will have a lower concentration of nanoparticles.  Figure 1.1.2.2 illustrates the 
differences between phytoremediation and nanoparticle absorption. In Figure 1.1.2.2, Ag
+
 
symbolizes the silver ions and Ag
0
 symbolizes the synthesized silver nanoparticles. 
6 6
 
 
A few studies describing the effects of nanoparticles on living plants were found 
in the literatures. One study found that alumina nanoparticles impeded the growth of seed 
roots of five plant species (corn, cucumber, soybean, cabbage, and carrot) (Yang and 
Watts, 2005). Another study using nanoparticles of carbon tubes, zinc, zinc oxide, 
alumina and aluminum was conducted on the root growth of seeds of radish, rape, 
ryegrass, lettuce, corn, and cucumber (Lin and Xing, 2007). This study concluded that the 
nanoparticles impeded the growth of the seed resulting in shorter root elongation 
compared to a control group. The study also found that if the seeds were fed smaller size 
nanoparticles, the root elongations measured were even shorter than seeds fed larger 
nanoparticles.  No studies were found that describe the mechanism by which silver 
nanoparticles affect the uptake, translocation and growth of plants.   
High Concentration of Ag
0
 
Low Concentration of Ag
0
 
Phytoremediation Nanoparticles uptake? 
Figure 1.1.2.2. Diagram of phytoremediation versus nanoparticle uptake 
illustration (http://polaris.umuc.edu/cvu/envm/phyto/html/org_Ia.jpg)
 7
2. OBJECTIVES OF RESEARCH 
 
Silver nanoparticles have recently been heavily used in cosmetic and medical 
products which have raised growing concerns about the environmental effect that these 
nanoparticles may have on the vegetation since it is well known that silver is highly toxic 
to microorganisms. The objectives of this thesis were: 
? To determine whether silver nanoparticles can be absorbed by tomato plants.  
? If silver nanoparticles are absorbed, where in the plant do the silver nanoparticles 
accumulate? 
? To observe the effect of the silver nanoparticles on the growth of tomato plants. 
 
 
 
 
 
 
 
 
 
 
 8
3. SYNTHESIS OF SILVER NANOPARTICLES 
 
 Silver nanoparticles can be synthesized using several different techniques such as 
chemical reduction, laser ablation, photochemical and electrochemical methods. The 
most popular method is chemical reduction of silver nitrate (AgNO
3
) using sodium citrate 
(C
6
H
5
O
7
Na
3
) or sodium borohydride (NaBH
4
). The preparation is simple, but requires 
care and cleanliness (Sileikaite et al., 2006). In this research, spherical silver 
nanoparticles were prepared using both borohydride and citrate reduction of silver nitrate.  
 The 50 nm particles were prepared according to (Lee and Meisel, 1982). 16.9 mg 
(1 mM) of silver nitrate was dissolved in 100 mL of distilled water. The solution was 
heated to a boil using a heating plate while the solution was vigorously agitated with a 
magnetic stirrer. After 2 minutes of boiling, 2 mL of 1% trisodium citrate was rapidly 
added. The solution turned a grayish color within a few minutes, indicating the synthesis 
of Ag nanoparticles. The solution was kept boiling for 10 minutes, which determined the 
size of nanoparticles, by allowing more chemical reduction time. The solution was then 
removed from the heating plate and allowed to cool. 
The reaction that occurred to form the 50nm silver nanoparticles is as follows: 
 4Ag
+
 + C
6
H
5
O
7
Na
3
 + 2H
2
O           4Ag
0
 + C
6
H
5
O
7
H
3
 + 3Na
+
 + H
+
 + O
2
   
 
 9
The 10 nm Ag nanoparticles were prepared using borohydride reduction method. 
In 100 ml of deionized water, 16.9 mg (1 mM) of silver nitrate was dissolved and the 
solution was kept in ice bath for half an hour. The ice cold silver nitrate solution was 
added slowly to 300 mL of vigorously stirred ice-cold (2 mM) NaBH
4
. A yellow Ag 
particle solution was obtained. 
The reaction that occurred to form the 10nm silver nanoparticles is as follows: 
                   AgNO
3
 + NaBH
4
            Ag
0
 + 1/2H
2
 + 1/2B
2
H
6
 + NaNO
3
   
Shape and size of the silver nanoparticles were analyzed with scanning electron 
microscopy (SEM) and transmission electron microscopy (TEM). Typical images of the 
silver nanoparticles are shown in Figure 3.1 and Figure 3.2. 
 
 
Figure 3.1.  SEM image of 50 nm Ag nanoparticles 
 
 10
 
Figure 3.2. a) TEM image of 50 nm Ag nanoparticles; b) TEM image of 10 nm Ag 
nanoparticles 
 
The concentration of the silver nanoparticle solutions was calculated, assuming all 
silver atoms were reacted and became particles. The atomic radius and density of Ag is 
1.6E-8 cm and 10.5 g/cm
3
, respectively. The atomic mass of Ag, A.M., was calculated by 
multiplying the volume (eq. 1.1) and the density. By replacing the atomic radius with the 
particle radius measured through SEM and TEM investigations, the particle mass, P.M., 
was also obtained. Dividing P.M. by A.M provided the number of Ag ions per particle, 
N
ion-particle
. The number of Ag ions in 1 mM of solution, N
ion-solution
, was calculated by 
multiplying the number of moles, the number of grams, and Avogadro?s number. 
Therefore, the number of Ag particles per mL, N
particles/mL
, was determined by  
 N
particles/mL
 = [N
ion-solution 
/ N
ion-particle
] * (1/ 100 mL).  (3.1) 
A list of the size and concentration of the silver nanoparticle solutions is given in Table 
3.1 below. 
Particle Size (nm) # of Particle per mL 
50 1.57E+11 
10 1.97E+13 
Table 3.1. Size and concentration of silver nanoparticle solutions  
b) a) 
 11
4. EXPERIMENTS 
 
4.1 Detection of silver nanoparticles 
 The presence of the silver nanoparticles in tomato plants must be detected so that 
uptake can be determined. There are many methods for detecting silver nanoparticles, but 
the detection in plant tissue requires specialized sample preparation. Most preparation 
methods involve harsh chemical treatment or high temperature ashing of the tissue. In an 
attempt to avoid lengthy preparation procedures for plant tissue analysis, XRD was 
attempted based on previous research literature where X-ray diffraction was used in a 
similar way to confirm formation of silver nanoparticles in white rot fungus 
(Vigneshwaran et al., 2006). Energy Dispersive Spectrometry (EDS), Raman 
Spectroscopy and UV/vis Spectrophotometry were also used.  
 
4.1.1 X-ray Diffraction (XRD) 
XRD is commonly used for determining the chemical composition and crystal 
structure of a material; therefore, detecting the presence of silver nanoparticles in plants 
tissues can be achieved by using XRD to examine the diffraction peaks of the plant. In 
Figure 4.1.1.1 a typical schematic of a XRD powder diffractometer is illustrated. 
 
 
 12
 
Figure 4.1.1.1. Schematic of a typical XRD powder diffractometer (Siemens manual) 
 In order to detect the silver nanoparticles in plant tissues, the diffraction patterns 
of silver must be established first. This is done by performing XRD on the silver 
nanoparticles themselves. Using Bragg?s Law, the (111) peak was calculated. The silver 
nanoparticles particles were found to be amorphous and had to be crystallized before 
using XRD. The silver nanoparticles were prepared for XRD by pipetting a small amount 
of well-mixed particle solution on a silicon wafer and then heat treated in an Argon 
atmosphere at 400 ?C for 1.5 hr. to crystallize the particles. Figure 4.1.1.2 shows the 
XRD plots of the first 2? peaks of 10 nm and 50 nm sized silver nanoparticles.  
 
 
 
 
 
 
 
a) b)
Figure 4.1.1.2. XRD plots of the first 2? peaks for a) 10 nm and b) 50 nm sized silver 
nanoparticles. 
37.0 37.5 38.0 38.5 39.0
0
50
100
150
200
250
300
In
tensity
2 Theta
 ~50nm
37.0 37.5 38.0 38.5 39.0
0
50
100
150
200
250
300
350
Intensity
2 Theta
 10nm
FCC(111) FCC(111)
 13
The first 2? peak, the (111) peak, is focused on because it has the smallest 
diffraction angle which tends to produce the greatest peak amplitude and is more likely to 
penetrate the plant tissue. Peaks for both particles sizes were in agreement with the 2? 
peak value of 38.25? for the first 2? peak for silver in the Joint Committee on Powder 
Diffraction Standards (JCPDS). From the peaks, the expected size of the particles was 
estimated as ~ 6.5 nm and ~53.4 nm using the Debye Scherrer equation seen below: 
 
D = 0.9 ? / ? cos ?               (4.1) 
  
where ? ? wavelength of radiation, ? - FWHM (Full width at half maximum), and  ? - the 
angle of the maximum of diffraction. It is critical to note that the Debye Scherrer 
equation has size limitations causing errors in size estimation for particles less than 20 
nm. In addition, there may be errors due to particle distribution within the sample.  
Preparation of tomato plants for XRD was as follows:  
For two weeks, two tomato plants were introduced to10 nm and 50 nm sized silver 
nanoparticles, each at a low concentration of 10
4
 particles in 16 ml of nutrient solution. A 
reference plant without exposure to silver nanoparticles was used as a control. The 
leaves, stems, and roots were cut off and analyzed for silver particle uptake. A picture of 
the plants after two weeks is shown in Figure 4.1.1.3. The plant samples were then heat 
treated in an Argon atmosphere at 400 ?C for 1.5 hour to crystallize the particles and to 
burn away the organic tissues.  Upon XRD analysis, there were no diffraction peaks 
visible in the leaves, stems and roots. Figure 4.1.1.4 shows the absence of the diffraction 
peaks in the XRD plots for the roots of the tomato plants. The absence of the peaks could 
 14
be due to the low concentration of silver nanoparticles absorbed. Therefore, XRD is not 
sensitive enough to detect the low concentration of silver nanoparticles in plant tissue. 
 
 
 
 
 
 
 
 
 
 
 
Figure 4.1.1.3. Tomato plants after two weeks for XRD 
 
 
 
 
 
 
 
Figure 4.1.1.4. The XRD plots for the tomato plants root (10 nm and 50 nm) 
 
Reference
10 nm 
50 nm
37.0 37.5 38.0 38.5 39.0
Intensity (A.U.)
2- theta
10 nm 
10
4
 concentration Root
37.0 37.5 38.0 38.5 39.0
In
te
n
s
i
t
y (A
.U.)
2-Theta
50nm 
10
4
 concentration Root
 15
4.1.2 Energy Dispersive Spectrometry (EDS) 
 Energy Dispersive Spectrometry (EDS) micro-analysis is performed by measuring 
the energy and intensity distribution of X-ray signals generated by a focused electron 
beam on a specimen. The data is used to obtain the elemental composition of the 
material. This method is a good candidate for identifying silver in the tissue of tomato 
plants. 
 The plants prepared for XRD analysis were also used for this analysis. It is 
assumed that the 10 nm silver nanoparticles are more likely to be absorbed in the plant at 
higher concentrations than the 50 nm silver nanoparticles. The leaf, stem and root of the 
plant fed 10 nm particles were used in this analysis. The results of the EDS scans are 
shown in Table 4.1.2.1 below. 
  
 
 
 
 
 
 
 
 
 
Table 4.1.2.1. EDS scans on the leaf, stem and root of tomato plant in 10nm nano-silver 
100.00100.00Total
0.000.002.984Ag
0.952.181.922Y
4.164.333.691Ca
70.9472.013.313K
19.8418.262.622Cl
3.682.962.013P
0.440.261.041Na
At%Wt%keVElement
100.00100.00Total
0.000.001.922Y
0.000.001.041Na
0.000.002.984Ag
7.397.873.691Ca
65.4668.003.313K
14.9714.102.622Cl
12.1810.022.013P
At%Wt%keVElement
100.00100.00Total
0.150.422.984Ag
1.192.751.922Y
27.6628.763.691Ca
48.8449.543.313K
7.186.602.622Cl
14.4711.622.013P
0.500.301.041Na
At%Wt%keVElement
 
Leaf Stem Root
 16
 The EDS output showed very little to no silver in the atomic percentage of the 
elemental composition of the plant.  The data showed a small amount of silver in the root; 
however, the concentration of silver nanoparticles in the root is lower than the detection 
limit of the EDS system, which means the test is inconclusive.   
 
4.1.3 Raman Spectroscopy 
 In Raman Spectroscopy, light is scattered inelastically from molecules providing 
information about the molecules? vibrational quantum states. The scattered signal is 
rather weak, but can be greatly strengthened if the molecules are attached to metal 
nanoparticles. A schematic of Raman Spectroscopy operation can be seen in Figure 
4.1.3.1 below. 
 
Light  
Source 
(??) 
(??) 
Figure 4.1.3.1 Schematic of Raman Spectroscopy 
(http://newton.ex.ac.uk/research/biomedical/optics/sers.html) 
 17
Silver nanoparticles are a common material used in Raman Spectroscopy and data 
of their Raman scattering are widely documented. In Figure 4.1.3.2, a plot of Raman 
spectra for synthesized silver nanoparticles is displayed. This plot was used as a reference 
for the silver nanoparticles peaks in this research. 
 
 
Figure 4.1.3.2.  Raman spectra for citrate synthesized silver nanoparticles (Puebla et 
al.,2004) 
  
 
 
 Two tomato plants were exposed to silver nanoparticles, 10 nm and 50 nm in size 
respectively.  Both had a concentration of 10
11 
particles in 16 ml of nutrients solution for 
two days. A reference plant without exposure to silver nanoparticles and the two plants 
exposed to silver nanoparticles were cut and separated into leaves, stems and roots for 
analysis. The Raman spectra of the leaves and stems of the plants are shown in Figure 
4.1.3.3. The roots were not used because they were too small to analyze with this method. 
 18
Raman spectra of tomato plant (Leaves)
-500
1500
3500
5500
7500
9500
11500
13500
15500
17500
19500
900 1100 1300 1500 1700
Raman Shift (cm-1)
Int
e
ns
it
y (
 a.u.)
Control
10nm Ag
50nm Ag
Raman Spectra of tomato plant (Stems)
1388.73
1431.39
1075.87
1574.53
-500
0
500
1000
1500
2000
2500
3000
3500
900 1100 1300 1500 1700
Raman Shift (cm-1)
Intensity (a.u.)
Control
10nm Ag
50nm Ag
Figure 4.1.3.3. Raman spectra for leaves (top) and stems (bottom) of tomato 
plants 
 19
 From the Raman spectra shown in Figure 4.1.3.3, the stems of the plants had 
several more spectrum than the control plant.  These extra peaks are in good agreement 
with the peaks seen in Figure 4.1.3.2, which confirms that the peaks are from silver 
nanoparticles.  The spectra from the leaves of the tested plant are not much different from 
the control plant. Therefore, silver nanoparticles were absorbed in the plant through the 
roots and within the stem but not to the leaves within the two days. Raman Spectroscopy 
proved to be an effective method in determining the uptake of silver nanoparticles in 
plants.  
 
4.1.4 UV/vis Spectrophotometry (UVS) 
UV/vis Spectrophotometry (UVS) is a technique for characterizing and 
measuring the absorbance of a species in solution by passing white light through a 
cuvette containing the sample. The intensity of the transmitted beam at different 
wavelengths is related to the concentration of the species in solution. Figure 
4.1.4.1 below is a schematic illustration of UVS operation. The absorbance 
spectrum between 400 nm to 450 nm is known to be for spherical silver 
nanoparticles (Sileikaite, et al., 2006). 
 
Figure 4.1.4.1. UVS operation schematic diagram (http://en.wikipedia.org/wiki/Image:UV-vis.png) 
 20
The equipment used in this test was a computer-controlled Ultrospec 2100 
pro spectrophotometer using the SWIFT II software. It is well known that the 
absorption (A) of light by the solution is dependent on three parameters: the 
extinction coefficient (?), the concentration (c), and the path length (L) as 
described by Beer-Lambert Law as 
A = ?*c*L                          (4.2) 
 
For the instrument used in this study, L= 1 cm. For a certain solution placed in a 
fixed cuvette, equation 4.2 indicates that the absorption is proportional to the 
concentration of the solution. If the reduction in the intensity of the transmission beam is 
due to the reduction of the concentration of the solution, then the transmission beam can 
be utilized to determine the plant absorption of the silver nanoparticles by determining 
the concentration using equation 4.2.  
 
4.2 UVS Sample Preparation 
The Supersweet 100, Hybrid tomato seeds were obtained and grown 
hydroponically. After two weeks the plants were big enough so that stems with leaves 
about 3? long were cut from the plants as test samples, which were then immediately 
planted in cuvettes used for spectrophotometry. Each cuvette, the top of which was 
punctured with a small hole, was filled with 1 ml of solution to be tested, as shown in 
Figure 4.2.1. The solutions to be tested were: 1) citrate solution made with 2 ml of citrate 
mixed with 100 ml of distilled water; 2) 10 nm silver particles in borohydride solution 
with a  
 21
concentration of 1.97x10
13
 particle/ml; 3) 50 nm silver particles in citrate solution with a 
concentration of 1.57x10
11
 particle/ml. 4) borohydride solution made with 300 ml of 
borohydride mixed with 100 ml of distilled water;  The plants were positioned in cuvettes 
so that the stems would not interfere with the spectrophotometer beam. Reference 
cuvettes with the same test solutions but without plants were also prepared and used in 
this experiment. 
 
 
 
 
The tomato plants and the reference solutions were stored under natural lighting 
and at room temperature. This ensured transpiration-pull to provide the needed water and 
nutrient uptake within the 10 ? 160 ?m diameter xylem vessels of the tomato stem (Van 
der Schoot, 2005). The UVS spectrum was obtained on each test solution several times 
over a 24-36 hour period.  Before each UVS run, the cuvettes were gently vibrated to 
reduce clustering of silver nanoparticles and their attachment to the outside stem walls.  
The test cycle was repeated five times after which the plants appeared to stop absorbing 
silver nanoparticles. The test procedure for a cycle was as follows: 
 
a) b) 
Figure 4.2.1. a) Tomato plant in a  cuvette filled with 1mL of citrate solution of 50 nm 
silver nanoparticles; b) Tomato plant in a cuvette with 1mL of borohydride solution of 10 
nm silver nanoparticles. 
 22
1. Take UVS scans right after the plant was placed in the cuvette or at the beginning 
of each subsequent cycle run.  
2. Take UVS scans 6 hours after step 1; 
3. Take UVS scans 24 hours or 36 hours after step 1; 
4. If needed, refill the cuvettes to the beginning level with the test solutions and 
repeat steps 1-3 of the experiment as a new cycle. 
The refilling procedures of the silver nanoparticle solution throughout this study 
were as the following as seen in Table 4.2.1.   
Test 
Solution 
Nanoparticle 
Size (nm) 
Cycle 
 # 
Solution 
Added  (ml) 
2 10 1 1 
2 10 2 0.4 
2 10 3 0.2 
2 10 4 1 
2 10 5 1 
3 50 1 1 
3 50 2 1 
3 50 3 0 
3 50 4 1 
3 50 5 1 
 
Table 4.2.1. Refills performed in experiment 
 23
4.2.1 Citrate/Borohydride Control Test 
An experiment was conducted on whether any chemical product of the tomato 
plant and citrate or borohydride reference solutions (test solutions #1 and #4) would react 
with the silver nanoparticles.  A schematic of the test procedure is shown in Figure 
4.2.1.1.  A UVS spectrum was obtained on a citrate reference solution as shown in Figure 
4.2.1.2a and on a borohydride reference solution as shown in Figure 4.2.1.2b.  Cuttings 
from a tomato plant were placed in citrate and borohydride solutions for 24 hr.  The 
plants were removed and 50 nm silver solution was added to citrate solution.  In addition, 
10 nm silver solution was added to borohydride solution and UVS spectra immediately 
taken. Thirty six hours after the nanoparticles introduction another UVS scan was 
performed.  All spectra are shown in Figure 4.2.1.3. 
 
 
 
 
 
 
 
 
There was no evidence of a detrimental reaction product between the plant and 
the citrate solution or borohydride that would affect the silver nanoparticles.  The 
absorbance wavelengths and peaks of the UVS spectra are identical for spectrums taken 
36 hours apart as shown in Figure 4.2.1.3a and b. 
Figure. 4.2.1.1. Schematic of citrate/borohydride control test. 
36 hours 
Citrate or Borohydride 
solution  
Plant Reactions with 
Citrate (PRC) or  
Borohydride (PRB) 
PRC or PRB 
and silver 
particles 
24 hours 
UVS scan UVS scan 
UVS scan 
PRC or PRB 
and silver 
particles 
 24
 
 
 
 
 
 
 
 
 
Figure 4.2.1.2. a) UVS spectra of pure citrate solution. b) UVS spectra of pure 
borohydride solution. 
 
 
 
 
 
 
 
 
 
 
 
Figure 4.2.1.3. a) UVS spectra of 50 nm silver particles in plant immersed citrate solution 
using the sequence shown in Figure 4.2.1.1. b) UVS spectra of 10 nm silver particles in 
plant immersed borohydride solution using the sequence shown in Figure 4.2.1.1. 
 
300 350 400 450 500 550 600
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Absorbance (a.u.)
Wavelength (nm)
 Start of Run
 After 36 hours
50nm Citrate 
300 350 400 450 500 550 600
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Absorbance (a.u.)
Wavelength (nm)
 Start of Run
 After 36 hours
10nm Borohydride
 
a) 
b) 
300 350 400 450 500 550 600
0.00
0.02
0.04
0.06
Absorbance (a.u.)
Wavelength (nm)
 Citrate
 
300 350 400 450 500 550 600
0.00
0.02
0.04
Ab
so
rb
an
ce
 (a
.u
.)
Wavelength (nm)
 Borohydride
 
a) 
b) 
 25
5. RESULTS AND DISCUSSIONS 
 
5.1 Experimental Results 
UVS and Raman Spectroscopy were used to monitor the uptake of silver 
nanoparticles by the tomato plants and the distribution of the particles within the plants. 
The effect of the silver nanoparticles on the tomato plant was visually observed. The 
UVS spectra from each test cycle are shown in Figure 5.1.1 through Figure 5.1.5. The 
UVS spectra were taken from samples and references at different times, as described in 
section 4.2. The data in Figure 5.1.1 shows that in the first 6 hours the concentration of 
silver nanoparticles in the cuvettes decreased by a significant amount as indicated by the 
height of the absorption peak while there was no change in the reference solutions. This 
indicates that the silver nanoparticles are either being taken up by the tomato plant or are 
collecting on the plants? stems. It is assumed as uptake by the plant because of moderate 
vibration performed to prevent collection on the stem. It is also evident that the location 
of the absorption peak shifted towards the long wavelength. Visual observation during 
this run showed that the color of both the 10 nm and 50 nm silver particle solutions 
changed to slightly clear in color, but no physical change in the plants was observable.   
 
 
 
 26
 
 
300 350 400 450 500 550 600
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
A
b
s
o
rbanc
e (a.
u
.
)
Wavelength (nm)
 Start of Run1
 After 6 hours
10 nm sample
300 350 400 450 500 550 600
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Ab
sor
banc
e (a.
u
.
)
Wavelength (nm)
 Start of Run 1
 After 6 hours10 nm Reference
 
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
Absorbanc
e
 (a.
u
.
)
Wavelength (nm)
 Start of Run1
 After 6 hours
50nm sample
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
A
b
so
rb
an
ce
 (
a
.u
.
)
Wavelength (nm)
 Start of Run1
 After 6 hours
50nm Reference
 
 
 
 
 
 
 
 
 
Figure 5.1.1. Cycle 1, UVS spectra of tomato plant samples in 10 nm 
and 50 nm Ag nanoparticles solutions and reference solutions.  
 27
In cycle 2, corresponding data as seen in Figure 5.1.2 shows a reduction in 
the absorbance peaks as was observed in cycle 1, but magnitude of the reduction 
in cycle 2 is much smaller compared to cycle 1. 
 
250 300 350 400 450 500 550 600 650
0.00
0.02
0.04
0.06
0.08
0.10
A
b
s
o
r
b
a
n
ce
 (
a
.u
.)
Wavelength (nm)
 Start of Run 2
 After 6 hours
 After 24 hours
10nm Sample
300 350 400 450 500 550 600
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Abs
o
rbance (a.
u
.)
Wavelength (nm)
 Start of Run2
 After 6 hours
 After 24 hours
10nm  Reference
 
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
Absorbanc
e (a.
u
.)
Wavelength (nm)
 Start of Run2
After 6 hours
 After 24 hours
50nm Sample
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
A
b
s
o
rba
n
ce
 (
a
.u.)
Wavelength (nm)
 Start of Run2
 After 6 hours
 After 24 hours
50nm Reference
 
 
 
 
 
 
 
 
Figure 5.1.2. Cycle 2, UVS spectra of tomato plant samples in 10 nm 
and 50 nm Ag nanoparticles solutions and reference solutions.  
 28
 
In cycle 3, corresponding data as shown in Figure 5.1.3, the 10 nm silver particle 
solution showed a greater reduction in the absorption peak than the 50 nm silver particle 
solution. Visually, the plant in the 10 nm silver particle solution began to turn brown at 
the tip of the stem, but the plant in the 50 nm silver particle solution did not show any 
observable color change and even began growing small leaves. 
250 300 350 400 450 500 550 600
0.00
0.02
0.04
0.06
0.08
0.10
Absorbance (a.u.
)
Wavelength (nm)
 Start of Run 3
 After 6 hours
 After 36 hours
10nm Sample
300 350 400 450 500 550 600
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Ab
so
rba
n
c
e
 (
a
.u
.)
Wavelength (nm)
 Start of Run3
 After 6 hours
 After 36 hours
10nm Reference
 
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
A
b
s
o
rbance (a.
u
.
)
Wavelength (nm)
 Start of Run 3
 After 6 hours
 After 36 hours
50nm Sample
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
3.0
 Start of Run 3
 After 6 hours
 After 36 hours
Abso
rba
n
ce
 (
a
.u.)
Wavelength (nm)
50nm Reference
 
 
 
 
 
 
Figure 5.1.3. Cycle 3, UVS spectra of tomato plant samples in 10 nm 
and 50 nm Ag nanoparticles solutions and reference solutions.  
 29
 
In cycle 4, shown in Figure 5.1.4, the absorbance peak decreased for the 10 nm 
silver particle solution but increased in the 50 nm silver particle solution. Also, the 
reference solutions of both silver nanoparticle solutions in this run showed an increase in 
the absorbance peaks, which would be likely due to small clustering of the nanoparticles 
over time. The stem continued turning brown farther up the plant in the 10 nm silver 
particle solution. 
 
250 300 350 400 450 500 550 600
0.00
0.05
0.10
0.15
0.20
A
b
s
o
rbance (
a
.u)
Wavelength (nm)
 Start of Run4
 After 6 hours
 After 36 hours
10nm Sample
300 350 400 450 500 550 600
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
Ab
sorb
ance
 (a
.u)
Wavelength (nm)
 Start of Run 4
 After 6 hours
 After 36 hours
 
10nm Reference
 
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
 Start of Run4
 After 6 hours
 After 36 hours
Absorbanc
e (a.
u
.)
Wavelength (nm)
50nm Sample
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
3.0
A
b
so
rb
an
c
e
 (a.u
.
)
Wavelength (nm)
 Start of Run4
 After 6 hours
 After 36 hours
 
50nm Reference
 
 
 
 
Figure 5.1.4. Cycle 4, UVS spectra of tomato plant samples in 10 nm 
and 50 nm Ag nanoparticles solutions and reference solutions.  
 30
 
In cycle 5, shown in Figure 5.1.5, the absorbance peaks of the 10 nm silver 
solutions had a very small decrease, and the 50 nm silver particle solutions began to 
resemble the reference. This shows that the plants may not take up nanoparticles due to 
the death of the plant or the plants may have reached their saturation point. The leaves of 
the plant in 10 nm silver solution, began to wither and turned brown. 
 
250 300 350 400 450 500 550 600 650
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
A
b
s
o
r
b
ance (a.u
.)
Wavelength (nm)
 Start of Run 5
 After 36 hours
10nm Sample
300 350 400 450 500 550 600
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
Abs
o
rba
n
ce (a.
u
)
Wavelength (nm)
 Start of Run 5
 After 36 hours10 nm Reference
 
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
A
b
so
rbanc
e (
a
.u.
)
Wavelength (nm)
 Start of Run 5
 After 36 hours
50nm Sample
300 350 400 450 500 550 600
0.0
0.5
1.0
1.5
2.0
2.5
3.0
A
b
sorbanc
e (a.
u
.)
Wavelength (nm)
 Start of Run 5
 After 36 hours50nm Reference
 
 
 
 
 
Figure 5.1.5. Cycle 5, UVS spectra of tomato plant samples in 10 nm 
and 50 nm Ag nanoparticles solutions and reference solutions.  
 31
The plant in 50 nm silver particle solution looked healthy throughout the test and 
continued to grow. The plant in the 10 nm silver particle solution began dying and 
eventually died after cycle 5. Figure 5.1.6 displays an image of the samples and 
references after cycle 5. From left to right, the samples are the 10 nm reference, 10 nm 
sample, 50 nm sample, and 50 nm reference.  
This experiment was repeated three times with six different samples and results 
were very similar to what is reported above, confirming the results. 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
 After cycle 5 of the UVS test, the leaves of the plants were analyzed for silver 
nanoparticles using Raman Spectroscopy, but a good output spectrum above 0.1% of the 
power of the light source at a wavelength of 514 nm was not achievable because of high 
intensity scattering on the detector. Previous Raman spectra were achieved at 5 % of the 
power of the light source at a wavelength of 514 nm.  The high intensity scattering is 
likely due to the leaves being saturated with silver nanoparticles. The Raman spectra are 
shown in Figure 5.1.7 below. 
Figure 5.1.6 Image of samples and reference 
10nm 50nm
After 5 runs
 32
 
 
 
 
 
 
 
 
 
 
 
Figure 5.1.7 Raman spectra of leaves after UVS cycle 5 with 0.1% of power of 514 nm 
light source. 
 
5.2. Conclusions 
 
This study showed several approaches in determining whether tomato plants 
uptake silver nanoparticles and if the silver nanoparticles have an affect on the plant 
growth. When tomato plants were exposed to silver nanoparticles, the concentration of 
nanoparticles within the solution decreased with time, which indicated the uptake of 
nanoparticles by the tomato plant. It is also found that the 10 nm particles have a stronger 
or faster influence on the plants than the 50 nm particles. Raman spectroscopy showed 
that silver nanoparticles were present in the stems of the plants, meaning that they were 
absorbed through the roots at a slow rate. However, by removing the roots for the UVS 
experiments, the uptake rate of silver nanoparticles was increased.  
 
Raman Spectra of tomato plants leaves after UV/vis run 5
0
2000
4000
6000
8000
10000
12000
500 700 900 1100 1300 1500 1700 1900
Raman Shift (cm-1)
In
t
e
n
s
i
t
y
 (a
.u
.)
10nm
50nm
 33
Based on the concentration change obtained from the UVS spectra displayed in 
Figures 5.2.1 and 5.2.2 below, combined with the visual observation of the plant growth, 
we may conclude: 1) the smaller the silver nanoparticles, the faster the uptake by the 
tomato plant through its stem, and 2) the absorption (uptake) of silver nanoparticles by 
the tomato plant results in the death of the plant. It must be recognized that the death of 
the plant as presented in the UVS test may not be the same results if the tomato plants 
had roots.  
 
 
 
 
 
 
 
Figure 5.2.1. Slopes of 10nm Ag nanoparticles uptake in tomato. 
 
 
 
 
 
 
 
Figure 5.2.2. Slopes of 50nm Ag nanoparticles uptake in tomato. 
Slopes of 10 nm Silver nano particles
 Concentration versus Time
y = -2E+12x + 2E+13
y = -5E+10x + 9E+12
y = -1E+11x + 1E+13
y = -5E+10x + 2E+13
y = -5E+10x + 2E+13
0
5E+12
1E+13
1.5E+13
2E+13
2.5E+13
0 20 40 60 80 100 120 140 160
Time (hours)
C
onc
e
n
tr
a
t
i
on (
 #
 P
a
r
t
i
c
l
e
s
/
m
L
)
Slopes of 50 nm Silver nano particles
 Concentration versus Time
y = -2E+10x + 1E+11
y = -6E+08x + 1E+11
y = -3E+08x + 1E+11
y = 4E+08x + 1E+11
y = 6E+08x + 1E+11
0
5E+10
1E+11
1.5E+11
2E+11
2.5E+11
0 20 40 60 80 100 120 140 160
Time (hours)
C
o
n
c
e
n
tr
ati
o
n
 ( # P
a
r
t
i
c
l
es/
m
L
)
 34
6. FUTURE WORK 
 
 Future work must be done to discover the mechanism that is happening inside the 
plant that causes the deadly effect. The determination of how small the size of the silver 
nanoparticle has to be to cause it to be toxic to the tomato plant also must be attempted. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 35
7. PLANT MONTORING SYSTEM 
 
Smart Agriculture is based on real-time monitoring of crops and fields to optimize 
plant growth. To effectively achieve smart cultivation it is essential that the needs and 
issues of the plant are met in a timely manner. Common issues most plants encounter are 
insect infections, droughts, improper pH in soil and/or solution, diseases/mold, exposure 
to harsh weather conditions, and late harvesting. Sensor networks and data acquisition 
systems play a significant role in establishing these needs and issues so that a swift 
reaction to make the proper provisions can occur. However, there are many disadvantages 
with current sensor networks and data acquisition systems, such as their short battery life, 
narrow temperature ranges, and numerous wire connections. Current sensor networks 
also tend to output minute signal responses, making them difficult to detect. Wireless 
communication can be used as a solution for the numerous wired connections between 
data acquisition systems and monitoring stations, as well as to increase the 
communication distance between the two. Figure 7.1 is a diagram illustrating the concept 
of Smart Agriculture with wireless communication. 
 
 
 
 36
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7.1. Illustration of the concept of Smart Agriculture with wireless communication 
 
 
 
 
 
 
 
GATHERING DATA 
SENSORS 
TRANSFERRING DATA
PM
S 
RFID Communication
Long range 
transceiver
Multiple short range transceivers 
DATA COLLECTION
Maximize Growth 
Improve the conditions
when needed. 
(Photo courtesy USDA ARS Image Library, 
http://www.ars.usda.gov/is/graphics/photos/ k5182-18.htm)  
Monitoring Stations
 37
8. OBJECTIVES OF RESEARCH 
 
The objective for the Plant Monitoring System (PMS) research is to develop a 
monitoring system that will track soil temperature, soil moisture, insect infestation, and 
fruit ripening via a wireless system. This system will in turn help maximize plant 
growing conditions, product yield, and product quality. The system will take readings 
from a network of sensors, recording everyday conditions of the plant?s environment, 
keeping close watch on issues that might be harmful to the plant?s growth. The system 
will output the recorded data in a useful format so that the user can make swift 
provisions. Ultimately, the system will be a low-cost, portable, long life battery-powered 
(~10 years), user-friendly, multi-sensor, plant monitoring system that operates between 
the temperatures of -50 ?C and 80?C. 
 
 
 
 
 
 
 
 
 38
9. CIRCUIT DESIGN 
A circuit was designed to produce a low-cost, portable, battery-powered, user-
friendly, and multi-sensor plant monitoring system to perform the data acquisition. The 
process for circuit design began with a block diagram of the system and then component 
selection to meet the various criteria. Below is a block diagram of the PMS (Figure 9.1). 
Showing the major modules of the system. 
 
Figure 9.1 ? Block diagram of PMS 
 
 39
The microcontroller was the key component of the system that was carefully 
selected from the thousands of industry devices. The microcontroller was required to 
meet all specifications set for the system. The wireless communication module also 
needed the same care in its selection. The other modules of the system were readily 
available and generally met the necessary specifications. 
 
9.1 Microcontroller Selection 
Microcontrollers are self-contained computers on a chip. They contain a CPU 
(central processing unit), fixed and variable memory, serial and/or parallel interfaces, 
data acquisition circuits, special timers, and various other circuit blocks. There are 
thousands of microcontrollers provided by many manufacturers. Each manufacturer has 
its own architecture variation and programming instruction sets. With so many varieties 
of architectures available and unique instruction sets, each microcontroller could rightly 
belong to a category of its own. This is why selection of a microcontroller must be done 
with care so that it operates as needed.  
Temperature range and low-power consumption were major issues that were 
considered for the PMS. Typically, most manufacturers guarantee operation in the 
temperature range of -40 to 85 ?C. The PMS goal of operation between -50 to 80?C 
limited the available microcontrollers. An intensive search for microcontrollers was 
conducted. This search led to two leading microcontroller manufacturers: Texas 
Instruments (TI) and Atmel. Table 9.1.1 displays a list of the microcontrollers? 
operational specifications. The manufacturers only provided specifications for operation 
at 25?C and 85 ?C.  
 40
The TI MSP430F155 microcontroller was chosen based on the provided data, for 
its lower power consumption in sleep and power save modes compared to the other 
microcontrollers. The selected microcontroller was tested for operation at -50?C to 80 ?C; 
the results are given later in the result section.  
 
 
Table 9.1.1. List of selected low-power microcontrollers  
 
9.2 Wireless Communication Selection 
 Wireless technologies have grown rapidly over recent years. This growth is 
partially due to the vast growth in the cell-phone industry (Wang et al., 2006). 
The types of wireless protocols used range from simple IrDA that uses infrared light for 
short range point-to-point communications, to wireless personal area network (WPAN) 
point-to-multi-point communications such as Zigbee and Bluetooth, to long-distance 
cellular phone systems, such as Global System for Mobile communications (GSM) 
(Wang et al., 2006). A comparison of the different wireless protocols is shown in Figure 
 41
9.2.1. Although many industries are using wireless communication, the usage in 
agriculture and food applications are still rare. 
 Several wireless protocols were studied to select a wireless protocol that would 
closely meet the requirements of the PMS. Table 9.2.1 shows a list of wireless modules 
researched. Each protocol had its strength and weaknesses, WiFi?s (802.11 a, b,g,) power 
consumption and other important features did not meet the needed specifications. For 
example, its typical range is up to eight kilometers for transmitting and receiving, 
exceeding the needed range and requiring too much power for transmitting that distance. 
Bluetooth (802.15.1) has a moderately high current draw to transmit and receive, though 
it would have been acceptable. Also, the distance for a class 1 Bluetooth system seems 
reasonable, with a range of 100 meters (over 300 ft.). However, since it is limited to only 
seven nodes for devices to be connected it does not meet the criteria for multi-sensors in a 
large crop field. Alternatively, Zigbee (802.15.4) offers low power consumption, has an 
operating range of more than 100 meters (approx. 300 ft.) with line of sight 
communication, and has the ability to connect to 255 devices at once with minimal cross 
talk.  
    The list of devices was narrowed down to one or two devices from the Zigbee, 
Bluetooth and WiFi groups for further comparison. From the Zigbee group, Atmel?s 
AT86RF210 was selected because of its low power consumption and large node 
connectivity of the device. From the Bluetooth group, Texas Instruments? BRF6150 was 
selected because it had the lowest power consumption of the group, even though its range 
presented a challenge. Within the Bluetooth group, National Semiconductor?s LMX5252 
was also selected. It had a slightly higher current consumption than the BRF6150, but its 
 42
range was far greater. From the WiFi group, Cirronet?s WIT2410 was selected. It has the 
lowest current consumption in the WiFi group, but its current was still much higher 
compared to the currents used by the devices selected from the other two groups. 
    Based on the study and comparison of the RF modules gathered for this analysis, the 
initial protocol chosen was Zigbee Atmel?s AT86RF210. Later, a shift was made to 
Bluetooth Texas Instruments? BRF6150 because of its popularity and adaptability and 
Zigbee?s lack of technical support. The Bluetooth module is displayed in Figure 9.2.2. 
 
 
 
 
 
 
 
 
 
 
 
 
 43
 
Figure 9.2.1. Comparison of the different wireless protocols (www.BB-elec.com) 
 
 
Figure 9.2.2. Bluetooth Module 
 
 
 44
 
Table 9.2.1. List of wireless technology modules and their parameters  
 
 
 
 
 
 
 45
9.3 Board Layout 
After components were chosen, a schematic diagram of the circuit board was 
made to show how the components were connected electrically to operate as a system. 
This required that each component?s characteristics be studied to make sure that they met 
the temperature and stability requirements and that they were connected according to 
their interface requirements. Connectors had to be chosen and mounted so that power, the 
development system, and other input/output devices could be connected. It was also 
necessary to keep in mind how the system?s power and other parameters would be 
measured once the unit was assembled.  
 The circuit board was designed and the layout was completed in Orcad board 
layout software. In order to design the board, each component?s mechanical size was 
determined and a mounting ?footprint? with the proper spacing and hole sizes was 
selected from an existing list; otherwise a new footprint was designed. Next, components 
were placed on the board so that copper etch connections could be made between 
component pins. Finally this design was converted to machine readable format so that a 
prototype printed wiring board (PWB or PCB) could be made. The PWB is bare boards 
made of an insulator of fiberglass epoxy with a copper etch pattern and holes. After this 
board was received, parts were soldered onto the PWB so that the prototype board 
(assembly) could be tested. The prototype board is shown in Figure 9.3.1 below. One of 
the main parameters that must be met was power consumption. Power consumption was 
measured by inserting a digital multimeter (setup for current measurements) into the wire 
between the external power supply and the board. The power consumption of the device 
 46
is shown in Table 9.3.1, the circuit board schematic is shown in Figure 9.3.2, and the Bill 
of Material is shown in Table 9.3.2 below. 
 
 
Figure 9.3.1. PMS prototype board 
 
 
 
 
 
Table 9.3.1. PMS power consumption 
 
 
 
 
 
PMS State Current Draw 
Sleep mode ~ 0.350 mA 
Operational 
mode 
~ 23.4 mA 
 47
 
 
Figure 9.3.2. PMS circuit board schematic drawing 
 48
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 9.3.2. PMS Bill of Materials 
 49
 
9.4 Software 
 This project required the use of the C programming language software. IAR 
Embedded Workbench IDE and Dev-C++ IDE were used for the C code programming 
environment. The MSP430 Serial Programming Adapter was used in programming the 
code from the software to the microcontroller chip. The MSP430 USB Debugging 
Interface and HyperTerminal were used for in-circuit debugging the code and chip to 
ensure proper operations. Collectively, all of the above software and devices were used in 
the development of the PMS? software functions.  
The PMS development in C allows for fast program execution and small source 
code size. Essentially, the PMS has a primary source code file and several header files 
that serve as modules for the many functions of the system. The operating software is an 
interrupt-driven program that allowed for better power conservation and operation in 
low-power modes. The system software was designed in a modular fashion so that any 
problems that occurred could be easily assessed and updates could be easily made. 
Modular design is considered good programming practice since it allows changes to be 
inserted or deleted without affecting other parts of the program. The C-programming 
flow-diagram is shown in Figure 9.4.1 below. 
 
 
 
 
 
 50
 
 
Figure 9.4.1. PMS C-programming flow-diagram 
 
 
 
Set-up Wireless 
Interface & Interrupt
 
Go to Sleep 
Initialize Set-up 
Ports & Timers 
Wireless 
Interrupt?
Scan all 8 Sensors
Get Time & 
Sensors Data 
Timer 
1 hour?
Store Data in Flash 
Memory 
 
Send to Transceiver
Get Data from 
Flash Memory 
Wake-up
Yes
Yes 
No No 
 51
10. SENSING NETWORK 
 
The PMS prototype sensing network is comprised of 4 sensors: a chemical-
resistor, temperature and humidity sensors (bundled in one package), and a soil monitor.            
The chemical-resistor device changes resistance when exposed to a gas coming from 
nearby plants being eaten by insects or from plants in their ripening stages. 
The temperature and humidity sensor were both part of a single package, a HIH-
3602-C Relative Humidity (RH) sensor manufactured by Honeywell. Both relative 
humidity and temperature sensing were combined in a TO-5 housing with a hydrophobic 
sintered stainless steel filter. The laser trimmed thermoset polymer capacitive sensing 
elements had on-chip integrated signal conditioning. The temperature sensor was 
thermally connected with the RH sensor making the HIH-3602-C ideal for measuring 
dew point and other absolute moisture terms. Factory calibration data supplied with each 
sensor allowed individually matched downstream electronics and ? 2% RH total 
accuracy. Sensor construction consisted of a planar capacitor with a second polymer layer 
to protect against dirt, dust, oils and other hazards.  
The soil monitor was a granular matrix sensor made by Watermark, part number 
200SS. It was an indirect, calibrated method of measuring soil water. Through an 
electrical resistance type sensor it could be read by data logging equipment or in our case, 
the plant monitoring system.  
 52
11. EXPERIMENTS 
 
 To investigate the basic operation of the PMS, two tests were performed. The 
first test was conducted to investigate two major functions of the system: 1) The system?s 
ability to collect data as well as transmit the data to an external computer; 2) The proper 
operation of the sensor networks, namely to gather data that can be analyzed to ensure 
that plant growth of tomato plants could be sustained. Secondly, a temperature test was 
performed to obtain the maximum temperature operating range of the system.  It should 
be noted here that the processor chosen for low power did not have specifications to 
cover the temperature range; thus, test was done to verify useable operation outside the 
specified parameters. 
 
11.1 Set-up 
A 20 gallon aquarium was filled with sand (Figure 11.1.1), and two tomato plants 
were transplanted into the aquarium, creating a simulated growing environment.  Sand 
was used to see if the PMS was capable of assisting in tomato plant growth in one of the 
most difficult soils to grow plants in.  The setup was monitored by a computer using 
HyperTerminal software via Bluetooth for several weeks.  The Bluetooth transmitted 
shorter distances than expected.  The output data was captured (Figure 11.1.2), during 
 53
fifteen minute intervals. Temperature, humidity, soil moisture and resistance of a 
variable resistor, simulating the chemical-resistor, were recorded. The output response of 
the temperature and humidity of the PMS was compared to the output of a commercial 
temperature/humidity device.  
Several PMS devices were then placed into an environmental chamber, with each 
devices output signal monitored, to test the operation of the devices in the extreme 
temperature ranges (-50? C and 80? C). The chamber is shown in Figure 11.1.3. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 11.1.1. Experiment Setup for PMS testing. 
 54
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 11.1.2. Screen print of HyperTerminal capturing PMS output data 
 
 55
 
 
Figure 11.1.3. Environmental chamber used for temperature test 
 
 
 
 
 
 
 
 56
12. RESULTS AND DISCUSSIONS 
The PMS outputs data was taken in fifteen minute intervals. Hourly samples were 
taken from the recorded PMS data and compared with the recorded reference data. Data 
is shown in Table 12.1.  From the experiment, the mean temperature reading for the PMS 
was ~ 25.74 degrees Celsius, while the mean reference temperature was ~ 25.66 degrees 
Celsius. The mean PMS humidity reading was ~ 45.05 %RH, while the mean reference 
humidity reading was measured at ~ 44.51 %RH.  
The soil monitor was tested by saturating the soil with plant nutrients and then 
allowing the soil to dry as seen in Figure 12.1. To effectively show its proper operation, 
this process was drawn-out over 4 weeks. The soil monitor began to reach its driest state 
over a period of 5 days (under deliberate drying conditions). At this point, the soil was 
again saturated and allowed to repeat the cycle. During this period of time the tomato 
plants soil moisture needs were met and allowed for plant growth. Tomato plants wilt 
rapidly when the roots do not have enough moisture.  Observation of the plants for 
wilting was used as the criteria to determine that moisture needs were met. 
The results of the temperature test are displayed in Table 12.2. The TI 
microcontroller used in the PMS, stopped operating below the temperature of -45 ?C but 
begin normal operation when the temperature was increased to -40?C.  Although this 
board did not operate at the low temperature, it did recover once the temperature returned 
 57
to a higher temperature.  Note that virtually no plants are cultivated at these cold 
temperatures.  However, it could be used for monitoring storage temperatures of grains 
and other frozen foods. Several simple boards were fabricated using the Atmel 
Atmega165p and they passed both extremes. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Time  PMS Temp PMS Hum Ref. Temp 
Ref. 
Hum 
8:00 AM 25.98 47.48 24.46 45.82 
8:15 AM 23.87 47.17 24.41 44.24 
8:30 AM 24.4 46.76 24.82 44.24 
8:45 AM 25.98 44.99 24.82 44.14 
9:00 AM 26.51 47.42 25.81 47.9 
9:45 AM 23.87 46.88 24.26 47.66 
10:00 AM 24.4 48.87 24.8 49.62 
11:00 AM 25.98 49.47 24.42 45.2 
12:00 PM 27.57 44.48 27.9 44.03 
1:00 PM 29.69 40.03 29.91 40.73 
1:30 PM 26.51 40.09 27.41 40.79 
2:00 PM 27.04 44.8 28.14 45.5 
3:00 PM 27.04 43.19 24.96 42.27 
3:45 PM 23.87 41.2 24.76 41.91 
4:00 PM 23.34 42.9 24 43.6 
          
Average 25.73666667 45.04866667 25.65866667 44.51 
. 
Table 12.1. Temperature and humidity data recorded (PMS and reference) 
 58
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 12.2. Results of temperature extreme operation test 
 
Soil Moisture
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500 600 700 800 900 1000
Measurements
Vo
l
t
ag
e (
V
)
Soil 
 
Figure 12.1. Graph of PMS soil readings [The soil monitor?s voltage readings ranged 
from 0 .2 Volts (dry) to 1.0 Volt (wet)] 
 
   
ATmega165P 
 
   
MSP430F155 
80?C - 50?C Microcontroller 
 59
12.1 Conclusion 
? The average temperature measurements were approximately 0.1 degrees from the 
reference while the average humidity measurements were seen to be about .54% 
off.  
? Current draw of the PMS in sleep mode was low (~0.4 mA), but needs to be 
lower. 
? The soil moisture sensor detected the plant moisture needs for 4 weeks helping in 
the growth of the plant.  
? The TI MSP430F155 didn?t operate in low end of temperature range needed. 
? Bluetooth had short transmission distance. 
In conclusion, the PMS unit responses preformed reliably and can subsequently 
be used as a wireless smart agriculture device. 
 
 
 
 
 
 
 
 
 
 
 
 60
13. FUTURE WORK 
 
Future work would be to develop a new system using the ATmega165P which 
operates in the temperature range needed. Also reduction of the current draw of system is 
a must, in the order of (~20 ?A). This would allow 10 year battery life of most standard 
batteries. The wireless communication protocol should be converted from Bluetooth to 
RFID, because RFID might provide longer communication capabilities. Finally, the 
system needs to be tested in the field for operation. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 61
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