Nanosensing of Pesticides by Zinc Oxide Quantum Dot: An Optical

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Nano sensing of pesticides by zinc oxide quantum dot: An optical and electrochemical approach for the detection of pesticides in water Dibakar Sahoo, Abhishek Mandal, Tapas Mitra, Kaushik Chakraborty, Munmun Bardhan, and Anjan Kumar Dasgupta J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b04188 • Publication Date (Web): 14 Dec 2017 Downloaded from http://pubs.acs.org on December 15, 2017

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Journal of Agricultural and Food Chemistry

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Nano sensing of pesticides by zinc oxide quantum dot: An optical

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and electrochemical approach for the detection of pesticides in

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water

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Dibakar Sahoo a*, Abhishek Mandal b, Tapas Mitra a, Kaushik Chakraborty c, Munmun

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Bardhan d and Anjan Kumar Dasgupta a*

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Abstract:

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Present study reveals the low concentrations (~ 4 ppm) of pesticide sensing vis-à-vis

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degradation of pesticides with the help of nontoxic zinc oxide quantum dots (QD). In our

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study, we have taken 4 different pesticides v.i.z., Aldrin, Tetradifon, Glyphosate, and

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Atrazine, which are widely used in agriculture and have structural dissimilarities/diversity.

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By using optical sensing techniques such as steady state and time resolved fluorescence we

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have analysed the detailed exciton dynamics of QD in the presence of different pesticides. It

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has been found that the pesticide containing good leaving groups (-Cl) can interact with QD

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promptly and has high binding affinity (~107 M-1). The different binding signatures of QD

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with different pesticides enable us to differentiate between the pesticides. Time resolved

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fluorescence spectroscopy provides significant variance (̴ 150 ns to 300 ns) for different

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pesticides. Furthermore, a large variation (105 Ω to 7×104 Ω) in the resistance of QD in the

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presence of different pesticides was revealed by electrochemical sensing technique.

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Moreover, during the interaction with pesticides, QD can also act as a photocatalyst to

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degrade pesticides. Present investigation explored the fact that the rate of degradation is

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positively affected by the binding affinity i.e., greater the binding greater is the degradation.

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What is more, both optical and electrochemical measurements of QD, in tandem, as described

ACS Paragon Plus Environment


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in our study could be utilized as the pattern recognition sensor for detection of several

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pesticides.

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1. Introduction:

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Over the last six decades the pesticides have been used to control insects, bacteria, weeds,

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nematodes, rodents pests etc. Consistent usage of pesticides has contaminated the food chain

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through air, water and soil posing serious damage to the human and animal health1,2.

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Therefore, it becomes imperative for environmental scientists to detect these toxic elements

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in our ecosystem and it is also necessary to invent novel methods to biodegrade or catalyze

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hazardous as well as toxic pesticidal waste into harmless, non-hazardous metabolites/

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compounds so that the former may no longer linger as a threat to the environment. Many

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techniques like high performance liquid chromatography (HPLC)3, gas chromatography-mass

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spectroscopy (GC-MS), Liquid mass spectroscopy (LC-MS), enzyme-linked immunosorbent

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assays (ELISAs)4 and also tandem techniques like LC-MS/MS, GC-MS/MS have been used

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for effective detection of pesticides in food, water, etc. in past years. These methods are

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costly, rely upon sophisticated instruments and skilled manpower, which is why, there is a

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need for newer strategies and de novo techniques, which are fast, reliable, practically

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economical, analytical and highly sensitive as well as selective for their detection. Recently

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sensor based optical and electrochemical techniques are emerging as a promising alternative

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on this regard due to their rapidity, specificity, and ease for mass fabrication and field

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applicability1, 5.

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Nowadays, the luminescence of semiconductor quantum dots (QDs) has attracted

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considerable attention of the researchers due to their unique optical properties that are

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different from their bulk structure components5a,

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medical8, biological 9, and environmental applications10. In our study we have extended the

6,7

. QDs are widely used as sensor for


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use of QDs for the detection of pesticides as well as degradation of pesticides. In our study

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we have used ZnO QDs for their unique electrical and optical properties11,12,13. Among all

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the techniques for preparing ZnO QD the sol-gel technique at room temperature is the best

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choice to obtain ZnO QD with highly visible emission, because the resulting samples are

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small enough and contain lots of defects14.

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In our investigation in order to prevent ZnO QD from undergoing spontaneous growth and

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aggregation we have employed shells like coating with 3-amminopropyltrimethoxysilane

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(APTES) containing SiO2. As the siloxane has the ability to form the covalent bond with

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metal oxide surface these molecules are found to create the shielding barrier11b, 15 that protect

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the QD at core and stabilize ZnO QD from decomposition in water .

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The pesticides that were used for detection in the study are (1) Aldrin (2) Glyphosate (3)

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Tetradifon (4) Atrazine (Scheme 1)16. Here in our study we have used four pesticides which

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are widely/popularly used and structurally dissimilar to each other except for Aldrin, which is

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an organochlorine insecticide that was widely used until the 1999, when it was banned in

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most countries. Before the ban, it was heavily used as a pesticide to treat seed and soil.

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Till date there are several reports available for the detection of different pesticides using

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different materials17. But only few reports were found to discuss using metal oxide quantum

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dots as a sensor for pesticides. Here for the first-time interaction dynamics of QDs with

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pesticides has been carefully analysed by steady state, time resolved fluorescence

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spectroscopy as well as electrochemical methods via resistance. We have elucidated the

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effect of exciton quenching of QD in presence of different pesticides. Beyond sensing,

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unique surface area and the surface activity of our ZnO QD make them desirable candidate in

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the catalytic reactions which further degraded pesticides into harmless and useful

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components. In addition understanding fundamental mechanism of excited state processes of



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QD in presence of pesticides will also have direct applications in agriculture and

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environment.

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2. Materials and methods:

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2.1. Chemicals:

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Analytical grade tetradifon [purity-98%, octanol-water partition coefficient (Pow) - 4.61,

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water solubility - (10 °C) 0.05 %, (20 °C) 0.08 %, (50 °C) 0.34 % at pH 7, GUS potential –

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4.10, DT50 – 0.33 years]; atrazine (purity - 98.9%, Pow - 2.82, water solubility - 33 mg L−1 at

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pH 7, GUS potential – 3.75, DT50 – 14-109 days); aldrin [purity-98%, octanol-water partition

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coefficient (Pow) – 6.50, water solubility – 0.027 mgL-1 (20-25 °C), GUS potential – -0.35,

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DT50 – 5-10 years]; and glyphosate [purity-98.9 %, octanol-water partition coefficient (Pow) –

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3.2, water solubility – 157,000 mg L-1 (20-25 °C, pH 7), GUS potential – -0.69, DT50 – 12-91

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days] were purchased from the Sigma-Aldrich, India. Solvents used were purchased locally

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and were of HPLC grade.

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2.2. Preparation of QDs:

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Preparation of APTES capped ZnO QDs is a two-step procedure as of previously reported

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literature18. First 0.1 M zinc acetate solution of methanol and 1 M KOH solution in methanol

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were prepared separately. At room temperature KOH solution was added to zinc acetate

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solution by dropwise with constant stirring. The resulting solution was homogenized by

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stirring continuously for 1 h with a magnetic stirrer. The solution thus obtained was found to

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show yellow luminescence under UV excitation, thereby indicating formation of ZnO

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particles. At this stage, 0.25 ml of 3-Aminopropyl triethoxysilane (APTES) solution was

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added into the ZnO solution to control particle growth. Immediately after this, 0.5 ml of

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distilled water was injected to the colloidal solution for mild sol–gel reaction of silica on


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particle surfaces. The as-prepared colloid was separated by centrifuging and washed several

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times by methanol followed by distilled water to remove unreacted molecules. Finally, the as-

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obtained colloid was dispersed in water medium. The schematic representation of ZnO QD

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synthesis is given in Scheme S1. The aqueous dispersions of colloids thus obtained were

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characterized for their structural, microscopic and optical properties.

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2.3. Methodology to Characterize QDs:

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XRD Methodology:

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X-ray diffraction (XRD) analyses were carried out using a Philips PW 1710 X-ray

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diffractometer, equipped with copper Kα radiation (generator voltage-40 kV, tube current 20

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mA) as the X-ray source in 2θ range of 3–70°.

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FTIR Methodology:

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Infra-red absorbance spectra of ZnO quantum dots were recorded on Bruker ALPHA, FTIR

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system (Typically 24 scans, Resolution - 4 cm−1) at wave numbers from 400 to 4000 cm−1.

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Samples were mixed with KBr and ground to a fine power to prepare a KBr pellet containing

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0.1% of ZnO QDs.

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2.4 Particle size and stability measurement:

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The diameter of the APTES capped QDs has been determined by TEM (JEOL JEM 2100 HR

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with EELS TEM (JEOL, Japan) operated at 200 kV) and dynamic light scattering method and

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the zeta potential of the same materials has been determined by electrophoretic light

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scattering using a DLS instrument (Malvern Nano-ZS, ZEN 3600). For particle size and zeta

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potential measurement, sample volume taken were 40 µl and 1 ml respectively.

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2.5 Steady State Measurement:



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For measuring absorption and fluorescence spectra, deionized water (Millipore) was used.

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The absorption spectra at 300 K were recorded with a Shimadzu spectrophotometer (Model

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UV-2104 PC), and emission spectra were obtained with a Hitachi F-7000 fluorescence

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spectrophotometer. All the experiments were done in aqueous solution. During the

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experiment the concentration of QD (5 ×10-5 M) kept constant for all time. The concentration

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of pesticides for all the experiments are given in the figure caption. After addition of

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pesticides with QDs we constantly vortex shake it for one minute followed by the steady state

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measurements. The equilibration time of 1 min

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equilibrium because emission spectra remain unchanged after 1 min, validated by several

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times discrete scanning of each system.

is enough to for the system to attain

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Measurement of Quantum Yield:

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The following equation was employed to measure the quantum yield of QD and QD-pesticide

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complex using Rodamine 6G (ΦR = 0.95 in water)19 as the reference.

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Φ= Φ

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Where Φ is fluorescence quantum yield, I is the integrated fluorescence intensity, n is the

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refractive index of solvent, and OD is optical density (absorption). The subscript

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reference fluorophore of known quantum yield.

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2.6 Time Resolved Measurements:

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For time-resolved fluorescence measurements, the samples were excited at 336 nm using a

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picosecond diode (IBH Nanoled- 07). The emission was collected at a magic angle

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polarization using a Hamamatsu MCP photomultiplier (2809U). The time correlated single-

………………………(1)


R

refers to

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photon counting (TCSPC) set up consists of an ortec 9327 CFD and a Tennelec TC 863 TAC.

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The data are collected with a PCA3 card (Oxford) as a multichannel analyzer. The typical

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FWHM of the system response is about 25 ps. The channel width is 12 ps per channel. The

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fluorescence decays were de-convoluted using IBH DAS6 software. The concentration of

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pesticides during TCSPC measurements kept same as the steady state measurements and for

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pesticides we take the highest concentrations only which were used in steady state

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measurements.

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2.7 Electrochemical Measurement:

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We measure the resistance of different pesticides with our instrument using ARM based open

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hardware board and DropSens® Platinum Inter Digitated Electrode (IDE). We have used a

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cell containing 2 ml sample in a cuvette and IDE is used as an electrode of the cell.

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According to the Scheme S2, we assume that the resistance of cell is R1.

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In the system, we have used a known resister with resistance R Ω. To measure the voltage V1

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across R1, we have used open hardware device. We have measured the resistance of the cell

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using following expression:

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= V

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=> = R ( …………………………….(3) )

( )

…………………………………(2)

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To get the conductivity, we have used the relationship

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G=1/R1,

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where G is the measured conductance of cell. In this experiment, we have logged the

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conductance data using Arduino due to open hardware platform. Data has been collected in

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one sample/ second over 90 seconds, with 16 bit precession resolution ADC. We have used a



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29.1 KΩ resister as R and 3.3 Volt Source as V. The whole set up for measuring the

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conductance is shown in scheme S2 (d).

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3. Results and Discussion:

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3.1 Characterization of synthesized ZnO QD:

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The ZnO QDs crystallinity was confirmed by XRD analysis (Supplementary Information

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Figure S1(a)). In the XRD spectrum of dry ZnO QDs broad peaks are observed, at 31.8

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(001), 34.48 (002), 36.4 (101), 47.65 (102), 56.66 (110), 62.85 (103) and 67.86 (112),

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respectively. The XRD pattern fits well with a wurtzite structure. The average crystal

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(diameter), evaluated by the Scherrer equation:20

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= …………………..(4)

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where k is structural constant, λ is the wavelength of X-Ray, d is the size of the QD and β is

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full width at half maximum, θ is the Bragg angle. For the calculation of the size of QDs we

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used the signal of the (102) where no overlap of consequent spectrum with 2θ = 47.65°, and

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the diameter was found to be equal to 6 nm.

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For the detection of the capping state of APTES molecules on ZnO QDs, FT-IR spectra

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have been carried out.(Supplementary Information Figure S1(b)). N-H stretching vibration of

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primary amines on the outer surface of the QDs corresponds to the signals at 3431 and 1599.5

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cm-1 . The peak at 2930 cm-1 is due to the asymmetric stretching vibration of C-H bond and

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the presence of Si-O-Si groups (polymerization of APTES 10 molecules) was confirmed by

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the peak at 1110 cm-1. The peak at1380 cm-1 is attributed to C-H bonds vibration while the

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peak at 1010 cm-1 is due to the presence of C-N bonds. The peak at 669 cm-1 is to in plane

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bending of C-C-C groups, and the signal at ~ 400 cm-1 is due the Zn-O bond20 .

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The synthesized APTES capped ZnO QD solution exhibited yellowish colour under UV

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radiation which was known as the signature of ZnO QD figure 1(a). The fluorescence


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emission spectra and the UV-absorption spectra of ZnO QD was shown in figure 1(b). The

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maximum absorption peak is at 337nm.

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In order to investigate the size, shape and stability of QD we used DLS, Zeta potential and

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TEM. The DLS study shows that the hydrodynamic radius of capped QD is – 40nm and zeta

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potential is -16 mV which is good for stability (Supplementary Information Figure S2). The

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TEM images shows that the average size of the QD is 5nm (Supplementary Information S2).

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Also the histogram plot of TEM image indicates that there is no aggregation at all. The good

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Gaussian fit and peak at 5nm suggests uniformity of the particle size. Also the fluorescence

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of this particles proves it as QDs.

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3.2 Fluorescence Quenching Spectroscopy:

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In our study we have excited the QD solution at 340 nm and got a broad emission spectrum

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having peak maximum at 525 nm figure 1(b). The broad emission peak of QDs which is

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intrinsic is nature, is dominated by the excitonic transition at the surface of QD and defect-

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mediated origin of green fluorescence21. Van Dijiken et al claimed that the visible emission is

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due to recombination of electron from conduction band with deep trap electron centre of

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V0++. On the other hand Van heusden et al proposed that the recombination of isolated V0+

203

centres with the photo-excited holes are responsible for green emission. So the broad

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emission peak can be decomposed in two components. One is centred at 555 nm (2.2 eV)

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while is another is at 500 nm (2.5 eV). It is already reported that the emission around 555 nm

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occurs from the defects states near the surface layer whereas the shorter wavelength at 500

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nm originates from defects near bulk of QD21b. The intrinsic fluorescence of QD (peak at

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525nm) is very much sensitive to different pesticides environments namely Aldrin,

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Tetradifon, Glyphosate and Atrazine. After gradual introduction of different pesticides, the

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fluorescence of QD got quenched gradually without having any shift (Figure 2).



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Interestingly this quenching phenomena is different for different pesticides. Therefore, the

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measurement of intrinsic fluorescence of QD in presence of pesticides can add valuable

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information regarding the sensing property of QD for pesticides.

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Quenching of fluorescence are of two types: Dynamic and static quenching. Static quenching

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is involved in ground state complex formation between fluorophore and quencher and this

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complex is non-fluorescent. The dynamic quenching is resulted from collisional encounters

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between the excited-state fluorophore and the quencher22. Here to clarify the quenching

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mechanism between QD and pesticides we have used Stern-Volmer equation (Eq-2)23.

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F0/F=1 + KSV[Q]= τ0/τ…………………………..(5)

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KSV=Kq. τ0………………………………………(6)

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Where F0 denotes the steady-state fluorescence intensity of QD, F is the steady state

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fluorescence intensity of QD in presence of different concentration of pesticides; KSV is the

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Stern Volmer quenching constant for QD; [Q] is the concentration of pesticides; kq is the

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quenching rate constant of QD; τ0 is the average fluorescence life time QD without

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pesticides. τ is the average lifetime of QD in presence of pesticides. Depending on the nature

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of S-V plot we can determine the nature of quenching. If S-V plot is linear then only one kind

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of quenching mechanism is involved i.e. only dynamic or only static. But if plot shows an

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upward or downward curvature then both type of quenching process are involved. Here the S-

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V plots are found linear in nature for all pesticides with good fitting linearity R>0.99 (Figure

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3). The linearity of the plots indicates the presence of only one type of quenching in each

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interaction.

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Table 1 displays different value of KSV for different pesticides. Now to address which

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particular type of quenching is responsible for each interaction we measure the same


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quenching effect in different temperatures. The dynamic quenching depends upon diffusion

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by rule. With the increasing temperature the diffusion coefficient increased and the

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bimolecular quenching constants also increased. However, for static quenching the reverse

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effect would be observed. For static quenching the KSV values decreased with an increase in

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temperature. In present study, the KSv values increases with increasing temperature

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(Supplementary Information, T1). This reveals that the type of quenching should be primarily

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dynamic in nature. To further confirm the mode of quenching we also measured fluorescence

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lifetime of QDs in time resolved decay section in support.

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From Table 1 it may be inferred that the quenching constant is different for different

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pesticides. The quenching constant is found to be highest for Aldrin and lowest for Atrazine

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due to structural differences of the target pesticides. The different quenching constants of

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QDs for different pesticides help us to detect pesticides easily. These differences in the

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quenching constants suggest different binding interactions of each pesticide with QD. In

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order to investigate the binding affinities, we have studied the binding interaction of QDs

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which is described in the next section.

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3.3 Analysis of the binding constants and the stoichiometry of binding:

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The value of binding constant (Kb) that describes the binding ability of pesticides to QDs is

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helpful to understand the interaction state of the pesticides with QDs. This degree of binding

252

affinity of QD-pesticides complex could also be used to distinguish between the different

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pesticides. In order to analyse thoroughly the equilibrium between free and bound molecules

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and rationalize our experimental data on QDs-pesticides systems, following equation has

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been employed 23.

256

!"#($% $)] $

= '()*+ + -'()#.]…………………….(7)



257

Where F0 and F are fluorescence intensity of the QD in absence and presence of different

258

concentration of pesticides respectively. Kb is the binding constant and n is the stoichiometry

259

of binding. According to equation (6) the Kb and n values can be obtained by the plot of

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log[(F0-F)] versus log [Q] (Figure 4).

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The values of n and Kb at 300 K for the pesticides are listed in Table 1. For all pesticides, the

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Kb values are equal or more than 107 M-1 order which indicates the higher affinity of

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pesticides to QDs. From Table 1 it may also be noticed that the binding constant is highest for

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Aldrin (̴ 1013 M-1) and lowest for atrazine (107 M-1). From these hugely different values we

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can easily distinguished different pesticides of our concern.

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The stoichiometry of binding (n) with QDs is also different for different pesticides. For

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Aldrin “n” value is found to be 3 but for other pesticides the value is 2. This indicates that 3

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sites of Aldrin are accessible by QDs, whereas, for the other pesticides, 2 sites of each can be

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reacted by QDs.

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The variation in quenching constants, binding constants and stoichiometry ratio for different

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pesticides can be explained by taking into account the structural variation of pesticides. The

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structures of the pesticides are shown in Scheme 1. Among all the target pesticides, Aldrin

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and Tetradifon has a number of good leaving group (-Cl). The APTES capped ZnO QDs

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contain number of free primary amine group which takes part in the interaction between QD

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and pesticides. The Cl¯ ions of Aldrin are replaced by primary amine group of QDs through

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nucleophilic substitution reaction (Scheme 2). In Aldrin there are 6 no. of Cl¯ ions which act

277

as leaving groups for the above said substitution reaction. Existence of many leaving group

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compels Aldrin to bind stronger with APTES capped QD and this also reflects from our

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experimental data showing strong binding constant for Aldrin in Table 1. The obtained lower

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value of binding constant of Tetradifon than Aldrin is due to the presence of lesser number of


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leaving groups. The above-mentioned reaction helps the pesticides to bind covalently with

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the QD.

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For Atrazine as well, there happens to be one Cl¯ ion but as there is also a possibility of steric

284

hindrance due to presence of alkyl amine group in Atrazine, QD cannot easily interact with

285

the former. Due to this steric hindrance, the binding affinity is also low which is revealed in

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our result by the lowest binding constant among all the pesticides.

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As for Glyphosate, the -COOH group is the reaction centre for the interaction with QD.

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Here -COOH group of Glyphosate converts to COO¯ whereas free primary amine group of

289

QD convert to NH3+ and ionically interact with each other (Scheme 2). There is also

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possibility for the formation of hydrogen bonding interaction between –COOH groups of

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Glyphosate and –NH2 group of APTES capped ZnO QD. Therefore, both the ionic and

292

hydrogen bonding interaction shows higher binding constant of Glyphosate compared to

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Atrazine.

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So the structural differences of pesticides create different binding affinity with QDs which in

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other words QDs has the ability to detect different pesticides.

296

3.4 Time-resolved fluorescence decay:

297

The fluorescence decay kinetics was measured for different pesticides maintaining the same

298

concentration of QD (Figure 5). From table T2 it is noticed that the QD gives three

299

exponential fitting of fluorescence decay profile from which we obtained three life time of

300

QD. Upon addition of different pesticides, the lifetime of QD having the longer value

301

changes significantly.

302

Interestingly the lifetime of QD is changed as a function of concentration of all pesticides.

303

(Supplementary Information T3). In our previous discussion we have stated that if the



304

quenching of QD in presence of pesticides is dominated by collision phenomena then there

305

should be changed of life time by varying the concentration of pesticides. Here our result

306

infers that there is a certain change in lifetime as a function of pesticide concentration which

307

implies the quenching phenomena is completely dominated by dynamic quenching.

308

The multi-exponential fitting of fluorescent decay curve for QD indicates that there are

309

multiple trapping levels which significantly contribute to the radiative transitions (Table T2).

310

In general, in II – VI semiconductor QDs commonly have the defects. These defects create

311

different band gap having extra life time along with the normal lifetime. For bulk

312

semiconductors these trapping levels are inactive for its forbidden optical transition. Here for

313

ZnO QDs the photoexcited QDs can return to the ground state via three different process: (1)

314

exciton emission (2) trap emission (3) non-radiative recombination. The first two processes

315

contribute to the radiative emission and also in fluorescence life time which was reflected in

316

our result. Due to this trapping state a broad emission band is observed in our study which is

317

already reported24.

318

The increase of long life time with the addition of pesticides is due to alternation of

319

transition route of energy transfer due to presence of pesticides. The long-life time is the

320

dominating factor as it has the larger amplitude. And the value of this long lifetime is greater

321

for Aldrin as it binds strongly with QD and smaller for Atrazine as it binds weakly than other

322

pesticides. The rest two components having small amplitude and having lifetimes of the order

323

of 10-8 S and 10-10 S correspond to amino silane induced decays which fails to integrate with

324

the major component.

325

To avoid the complexity, we calculate the average lifetime, radiative and non-radiative rate

326

constants with the help of following equations.

327

τf=a1 τ1+ a2 τ2……………………(8)


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Φf=Krτf…………………………..(9)

329

1/τf=Kr +∑Knr…………………….(10)

330

τf is average life time component of QD and Kr, Knr, Φf are the radiative constant, non-

331

radiative constant and fluorescence quantum yield of QD respectively.

332

The average life time is also increased in the presence of pesticides. This increment of

333

average life time of QD follows the opposite trends to that observed in the steady state profile

334

for which we observed a decrease in quantum yields in presence of pesticides. This can be

335

explained by taking account of modulation of the radiative and non-radiative decay pathways

336

(Eq. 9,10). To rule out the complexity we take the average lifetime to estimate Kr and Knr

337

values. The enhanced average life time and the reduced quantum yield values of QD in

338

presence of pesticides suggests that the decrease in radiative rate constant Kr is significantly

339

higher than decrease in the non-radiative rate constant after interaction of pesticides with QD

340

which reflects in our results (Table 2).

341

The radiative rate constant is also a signature of binding affinity of QD with different

342

pesticides. Here the fluorescence is going to decrease with the interaction of pesticides. So

343

the radiative rate constant has significant contribution in binding of QD with pesticides. The

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plot of radiative rate constant vs binding constant in figure S3. Here it was noticed that the

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strong binding of pesticides with QDs highly facilitates the decrease of radiative rate

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constant.

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From Table 2 it could be apparent that presence of different pesticides non-radiative decay

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constant of QD also decreases. This reduction rate of non-radiative decay may be attributed

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to the restriction of production of e- which is responsible for emission by recombination with

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hole. This e- must be used to bind the pesticides.



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So the fluorescence lifetime measurement conclude that the average lifetimes increases in the

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following way Atrazine< Glyphosate

Nanosensing of Pesticides by Zinc Oxide Quantum Dot: An Optical

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