IEEE SENSORS JOURNAL, VOL. 14, NO. 5, MAY 2014 1467

Nanobiosensor for Diclofop Detection Based onChemically Modified AFM Probes

Carolina Castro Bueno, Adriano Moraes Amarante, Guedmiller S. Oliveira, Daiana Kotra Deda,Omar Teschke, Eduardo de Faria Franca, and Fábio L. Leite

Abstract— Highly sensitive and selective functional nanobio-breaksensors are being developed because they have significantapplications in the sustenance and conservation of naturalresources and can be used in projects to identify degraded andcontaminated areas (of both soil and water) and as environmentalquality indicators. In the present study, a nanobiosensor wasdeveloped based on using theoretical models (molecular dockingand molecular dynamics simulations) based on biomimicry ofthe action mechanism of herbicides in plants coupled withatomic force microscopy (AFM) tools. The herbicide moleculeswere detected at very low concentrations using a unique sensorconstruction: the AFM probes and the substrate were chemicallyfunctionalized to favor covalent bonding and promote molecularflexibility, as well as to achieve reproducible and accurate results.Computational methods were used to determine the bindingenergies associated with the enzyme-herbicide interactions, whichwere compared with experimental results for adhesion forces.The theoretical results showed that the diclofop herbicide couldbe assembled and attached onto the mica substrate surface andthe ACCase enzyme on the AFM probe without damaging thediclofop molecule. The experimental results showed that usinga specific agrochemical target molecule was more efficient thanusing other nonspecific agrochemicals. On average, there was a90% difference between the values of specific recognition (diclo-fop) and nonspecific recognition (imazaquin, metsulfuron, andglyphosate). This result validated the selectivity and specificityof the nanobiosensor. The first evidence of diclofop detection bythe AFM probe sensors has been presented in this paper.

Index Terms— AFM, agrochemical detection, diclofop, enzyme-based nanobiosensor, molecular docking, molecular dynamicssimulation.

I. INTRODUCTION

FOOD production was greatly increased in the nineteenthcentury by technological improvements in agricultural

Manuscript received October 15, 2013; revised December 27, 2013;accepted January 8, 2014. Date of publication January 28, 2014; date ofcurrent version March 18, 2014. This work was supported in part by CAPES,in part by CNPq, in part by FAPESP under Grant 2007/05089-9, 2013/09746-5, Proc. 2009/09120-3), CAPES, CNPq, nBioNet and in part by the Instituteof Physics Gleb Wataghin. The associate editor coordinating the review ofthis paper and approving it for publication was Dr. Chang-Soo Kim.

C. Castro Bueno, G. S. Oliveira, A. M. Amarante, D. K. Deda, andF. L. Leite are with the Nanoneurobiophysics Research Group, Departmentof Physics, Chemistry and Mathematics, Federal University of São Carlos,Sorocaba 18052-780, Brazil (e-mail: carolcastrob@gmail.com;guedmuller@gmail.com; amarante_adriano2@hotmail.com; daianakdn@gmail.com; fabioleite@ufscar.br).

E. de Faria Franca is with the Chemistry Institute, FederalUniversity of Uberlândia, Uberlândia 18052-780, Brazil (e-mail:eduardoffranca@yahoo.com.br).

O. Teschke is with the Institute of Physics Gleb Wataghin, Uni-versidade Estadual de Campinas, Campinas 13083-970, Brazil (e-mail:oteschke@ifi.unicamp.br).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSEN.2014.2301997

techniques, such as the use of modern machinery, fertilisers,and agrochemical molecules [1], [2]. The indiscriminate useof agrochemicals on crops resulted in environmental con-tamination and toxic effects on human health [3], such ascarcinogenic effects [4], [5] and in vivo and in vitro genotoxiceffects on mammalian cells from chromosomal aberrations [6].

Current agrochemical detection methods rely almost entirelyon mass spectroscopy (MS) [7], [8], magnetic solid phaseextraction (MSPE) [9], gas chromatography-electron cap-ture (GC-ECD) [10], [11] via bacterial bioluminescentresponse [12], and high performance liquid chromatography(HPLC) [13], [14]. It is quite difficult to increase the detectionlimit (the sensitivity range) of these methods, and samples arefrequently reported to thermally degrade before the detectionprocedure can be performed. In regard to its limits of detectionfor agrochemicals, it was reported 1µg/kg (HPLC) [15] and0.1–4.6 µg/L (GC-MS) [16], for example. These values are notcompensatory when confronted with the disadvantages of thesetechniques: costly apparatus, organic solvents, and purificationof samples prior to assay, limiting the number of samples thatcan be examined.

Recently, experimental studies and theoretical models havebegun to address the challenge of establishing a causallink between subjective computational models and activitythat can be measured in the laboratory in practice. Atomicforce microscopy (AFM)-based sensors and biosensors (callednanobiosensors) have been fairly promising in exhibiting out-standing performance for detecting molecules with high speci-ficity and selectivity [17]–[19]. Understanding the theoreticaland experimental physicochemical conditions and architec-tures of the molecules involved in nanosensor constructionrequires precise experimental management, such as to identifythe ideal solution concentrations and pH of the medium,simulate the behavior of a smart surface, maintain systemstability, and confirm that the sensory and target moleculesretain their original characteristics, such as the ability toinhibit and to be inhibited. Thus, chemically modified AFMcantilever/probes can be matched with specific detection meth-ods and theoretical results for the entire process to create asensor that can detect a wide variety of substances at very lowconcentrations. Atomic force spectroscopy (AFS) is generallyused with AFM-based nanosensors [20], [21]. The AFS tool iscentred using force-distance curves (force curves) that provideimportant data on the measurement of recognition events oreither specific or nonspecific bonds, which are fundamentalfor creating and analyzing nanobiosensors [22]–[24]. Equallyessential and useful in these biosensing systems is a tool

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1468 IEEE SENSORS JOURNAL, VOL. 14, NO. 5, MAY 2014

called functionalization. This technique is used to changethe chemical properties at the surface, such that a standardmolecular design can be used to immobilize target analytesand sensory molecules [25]–[27].

In this respect, the aim of this work was to develop anAFM-enzyme-based nanosensor to detect agrochemicalmolecules, in particular, the diclofop molecule. Diclofop[(R,S)-2-[4-(2,4-dichlorophenoxy)-phenoxy]propanoate acidmethyl ester (DM)] is a post emergence herbicide used tocontrol wild oats and annual grasses in wheat and barley [28]and belongs to the aryloxyphenoxypropionates (APPs) classof herbicides. Diclofop related studies are important due to thelarge volume used in agriculture worldwide. As an example,it was published that the total annual usage of diclofop methylin China in 2006 was one to five million kilograms [29]. Inaddition, it has been informed that almost 75% of the activeingredient on diclofop molecule may fall onto soil surfacesupon application [30], and because of that, this compoundmay become common in the environment [28]. Regarding therelevance of diclofop detection on environment, our researchgroup has been reported two important published papersthat addressed theoretically and experimentally data over therelevance of this kind of analysis. Franca et al. (2011) [31]reported through a combination of molecular docking,molecular dynamics and quantum chemical calculations thatit is possible to establish a bridge between the experimentalcalculated adhesion force and the theoretical simulatedresults. Oliveira et al. (2013) [32] reported the modeling ofthe proper enzyme orientation on an functionalized AFMtip. It was showed that after 50 ns of MD simulations theorientation assumed by the enzyme has influence on theexposition of the enzyme binding sites. Also, in another studyfrom our research group, has been investigating the numberof enzymes that cover the AFM tip by using statisticalcalculations of the enzyme dimensions and AFM tip area(results not reported yet).

For sensor’s purposes, the mode of action of diclofopchemical group is important: they inhibit the homomericplastidic ACCase (acetyl-CoA carboxylase) enzyme [33], [34].This enzyme is present in almost all weed species, andtherefore, the diclofop herbicide is applied to crops in post-emergency operations. Inhibiting the action of the ACCaseenzyme interrupts the biosynthesis of fatty acids, resulting inplant death of grass species [35], [36].

The design and construction of the nanobiosensor are basedon the biomimicry of the natural process of host-guest interac-tions, i.e., the nanobiosensor design is based on the diclofopaction mechanism, which consists on the specific binding ofthe herbicide to the enzyme ACCase to inhibit the actionof the enzyme inside a plant cell. Thus, the assembly ofthe molecules in the functionalization process is the key toachieving selectivity and specificity. The correct arrangementof molecules results in a unique architecture of the targetand sensory molecules, in such way that their active sitesremain available for reaction and the formation of the specificcomplex.

The ACCase enzyme belongs to a multigene family andis regulated by a complex network of developmental and

environmental signals in response to both internal and externalstimuli [37]. This enzyme is a biotin-dependent moleculethat catalyses the irreversible carboxylation of acetyl-CoA tomalonyl-CoA through the two catalytic activities of biotincarboxylase and carboxyl transferase. The most importantpurpose of ACCase is to enable the malonyl-CoA substrateto control lipid biosynthesis in the chloroplasts and plas-tids of non-green tissues [38], [39]. The diclofop inhibitsACCase in several stages. First, the herbicide reduces theelectrochemical potentials across the plant cell membranes anddisrupts the transmembrane proton gradient (via electrolyteleakage), resulting in membrane peroxidation and the subse-quent uncontrolled release of the cell’s electrolytes inside theplasma membrane. The plasma membrane and the subcellularmembranes are essential for grouping and supporting cellularphysiological processes; thus, damaging the plasma membranenegatively affects the biochemical functions and the viabilityof the cell [39]. Once inside the plasma, the diclofop moleculespromote ACCase inhibition by causing plant disease symp-toms, such as a decline in chlorophyll and carbohydrate pro-duction, ending in chlorosis and subsequent plant death [40].This action mechanism was used to design the nanobiosensorby incorporating the architecture of biomaterials that mimiccomplex biological structures. Biomimicry incorporates con-cepts and strategies from nature into sustainable, useful, andefficient materials engineering of nanosensing applications byusing AFM and natural binding pathways.

In this context, through the use of force curves, the adhe-sion force between two surfaces or molecules, or the forcerequired to break the interaction between them, can be mea-sured [41], [42]. Studies have been and are being advancedby our research group using AFS to succeed the detectionof agrochemical through recognitions events of biomolecules,such as enzymes and antibodies. In this study, we first present abrief overview of relevant theoretical studies, such as dockingcalculations for different regions of the sensing element toshow cluster formation, and the use of molecular dynamicsto measure the interaction energies in a system to guaranteethat the original characteristics of molecules are retainedfollowing their insertion into the system. These theoreticalresults were used to adjust and optimize the experimentalparameters in this study to delineate a recognition mecha-nism based on AFS principles. Finally, this work is the firstreport of the development of an enzyme-based nanobiosen-sor and its application to diclofop detection using AFMprobe sensors in conjunction with theoretical and experimentalstudies.

II. MATERIALS AND METHODS

A. Computational Procedure - Molecular System

The enzymatic model of the ACCase enzyme was takenfrom the Protein Data Bank online repository, PDB ID:1UYR and all missed residues were rebuilt according to theFranca et al. (2011) considerations [31]. In addition, theAPTES and diclofop molecular structures were built usingJmol program [43] and the optimized geometry of both mole-cules were scanned quantum mechanically using ORCA 2.8.0

BUENO et al.: NANOBIOSENSOR FOR DICLOFOP DETECTION 1469

TABLE I

SIMULATED SYSTEM

program [44]. All the undetermined force field parametersto run molecular dynamics (MD) simulations were extractedfrom the harmonic fitting curves through quantum mechanicalcalculations.

B. Computational Procedure - Molecular Docking

To carry out docking calculations, the enzyme and herbicidemolecules were used in the AutoDock 4.0 program [45]. Theherbicide (diclofop) was restrained within a tridimensionalgrid and was docked on a rigid ACCase enzyme usingGasteiger-Huckel method [46] for partial charges considera-tion. A pre-defined 3D grid was created in several regionsalong the enzyme to evaluate the clustered configurationswith favorable binding energies. Lamarckian genetic algorithm(LGA) was applied to survey the conformational space ofthe unit system to obtain a set containing a 10 LGA dockedstructure.

C. Computational Procedure - MolecularDynamics Simulation

The clustered docked positions of the diclofop andthe linker-diclofop set were used to perform MD simula-tions in order to quantify the interaction energies resultingfrom van der Waals and electrostatic considerations. TheseMD calculations were performed using NAMD 2.7 soft-ware [47], and the results were analyzed using Tool CommandLanguage (TCL) scripts implanted in the VMD program [48].Langevin thermostat and piston [49] were applied to thesystem to minimize, calibrate and equilibrate the energy at310K, and 1 atm. The NVT ensemble was used, and a cutoff of 1.4 nm was employed for the short-range electrostaticand van der Waals interactions. The long-range contribu-tions were modeled using the Particle Mesh Ewald (PME)method [50]. All force field parameters were implementedon the CHARMM Force Field [51] protocol. The simulatedsystems can be observed on Table I, where the systemACCase+diclofop, was defined by E-I (enzyme-inhibitor), andthe system ACCase+APTES+diclofop was defined by E-F-I(enzyme-functionalized-inhibitor).

D. Experimental Procedure - Materials

The acetyl-CoA carboxylase enzyme was purchased fromCUSABIO (Hubei Province, P. R. China) as a part of thebovine acetyl-CoA carboxylase Synthetase ELISA Kit (whichis used to perform the standard method for a specific molecularligand-receptor). An animal ACCase enzyme was commer-cially available and was therefore used instead of a vegetal

Fig. 1. Schematic representation of showing the nanosensor design:(A) cantilever; (B) AFM tip; and (C) structure of APTES-glutaraldehydesystem bound to the Si3N4+ acetyl-CoA carboxylase (molecular formula:C23H38N7O17P3S) substrate (drawing is not to scale and all bonds are notshown).

ACCase enzyme and because of crystallographic evidence forthe similarity and mechanism of action between animals andplants. It is also known that some aryloxyphenoxypropionateherbicides and their analogues interfere with the maintenanceof transmembrane electrochemical gradients in animal sys-tems; similar observations have been reported for plants [52].The ACCase enzyme from the kit used in this work was inits purified form and it was brought to room temperature30 min before usage. The process of enzyme reconstitutionis very quick and, at first, consists on the centrifugation ofthe standard vial at 8,000 rpm for 30s. After this process,the standard is reconstituted with 1.0 mL of sample diluentprovided by the manufacturer. Finally, it is allowed to thestandard to sit for a minimum of 15 minutes with a gentleand uniform agitation (to avoid foaming). All the ACCasestandards were prepared fresh for each assay and used in afour-hour period (lifetime of the enzyme). This reconstitutionsolution of the ACCase enzyme from now on will be referredas ACCase standard. Super pure-grade materials were used inthe functionalization procedure: 3-aminopropyltriethoxysilane(APTES), triethylamine (TEA) and glutaraldehyde (as a25% aqueous solution), which were all purchased fromSigma-Aldrich (St. Louis, MO). The herbicides diclofop,imazaquin, glyphosate, and metsulfuron were also purchasedfrom Sigma-Aldrich (St. Louis, MO). AFM cantilevers withsharpened pyramidal tips (Si3N4 tips) were purchased fromThermoMicroscopes Microlevers (Sunnyvale, CA).

E. Experimental Procedure - Nanobiosensor Assembly

The fabrication of the nanobiosensors was based on thework of Etchegaray et al. [17] and da Silva et al. [22]. TheAFM cantilevers were first placed in a sterile Petri dish andsterilised under UV light for 30 minutes. The first step inthe functionalization of the AFM cantilevers and probes wasthe silanisation of the surface with APTES [Fig. 1(a)]. Thisreaction produced molecular arrays on the AFM cantileversurface via interactions between the silicon oxide groups onthe cantilever surface and the silanol group in the silane.The AFM tips were exposed to APTES and triethylaminevapours for over 30 minutes to bind the APTES to the

1470 IEEE SENSORS JOURNAL, VOL. 14, NO. 5, MAY 2014

cantilever surface. The next step in the treatment was theintroduction of the bifunctional reagent glutaraldehyde [seeFig. 1(b)], which provided a reactive termination (aldehydegroup) that could react with the amine (−NH2) groups ofthe APTES [see Fig. 1(c)] on the AFM tip surface. At thisstage, the glutaraldehyde acted as a spacer to provide flexibilitybetween the enzyme and the herbicide, thereby enhancing thesensitivity of the sensor. The AFM cantilevers were immersedin a 1 × 10−3 molL−1 glutaraldehyde aqueous solution andallowed to react for 40 minutes. MilliQ water was thenused to wash off any substances that were not covalentlyattached to the AFM cantilever surfaces. After the washingprocess, the sensing element (i.e., the ACCase enzyme) wasinduced to covalently bind to the terminal fragment of theaforementioned glutaraldehyde molecule by immersing theAFM cantilevers in a 4 µgL−1 ACCase enzyme solution.The rapid washing process ensured that the MilliQ water didnot modify the enzyme structure and thereby affect the forcemeasurements. These samples were incubated for 35 minutesat room temperature. At the end of the entire procedure, thecantilevers were used for AFM/AFS measurements.

F. Experimental Procedure - Immobilization ofTarget Molecules

A similar procedure to that described in Section II.Ewas used to assemble and covalently immobilize the targetmolecules (the herbicides) on the substrate system (i.e., a freshmuscovite mica layer). The first step in the functionalizationprocedure was the formation of a covalent bond betweenthe hydroxyl groups present on the surface of the micamuscovite and the silanol group of the APTES molecules.Next, the amino group of the APTES molecule was chemicallyreacted with the carboxyl groups (-COOH) of the diclofopherbicide molecule. This reaction formed a peptide bondbetween the groups; the geometry of the herbicide moleculeswas such that they could inhibit the enzyme, therebyremoving the need to introduce a glutaraldehyde spacer intothe target substrate system. After exposure to APTES andTEA vapours, the substrates were immediately immersed intothe 1 mM agrochemical solutions. The target molecules wereimmobilized on the surface and covered the entire area.

G. Experimental Procedure - AFM and AFS

The Atomic Force Microscope ThermoMicroscope Auto-Probe CP was used in the AFS experiments. In addition,according to the manufacturer, the curvature radius of thesharpened pyramidal tip used is <20 nm. The cantilevers werecalibrated using the method described by Sader et al. [53],i.e., the system was subjected to a vibration and the resonantfrequency in air was measured. The force measures wereobtained by approximating the tip retraction rate as 10 µms−1.The contact mode was used to measure all the force curves;these analyses were performed in a 20 × 10−3 molL−1

sodium phosphate buffer at pH 7.0. Twenty measurementswere made for each probe, corresponding to approximately19 statistically valid measurements. All the measurementswere obtained using two cycles at a scan rate of 0.043 nm/s

Fig. 2. Representation of the diclofop molecule and the E-I system in bindingsite mode for the ACCase enzyme: (A and B) schematic and crystallographicrepresentation of the interactions between the diclofop and the ACCasebinding site, showing diclofop (in green) and binding mode (in magenta);(C) diclofop docked at a binding site mode; and (D) E-I system docked at aACCase binding site mode.

and Si3N4 tips suitable for the contact mode with a springconstant (k) of 0.038± 0.003 N/m (which was calculated byaveraging the values for 10 tips). The TopoMetrix SPMLab 4.0and OriginPro 8 programs were used to perform the statisticalanalysis.

H. Experimental Procedure - Raman Spectroscopy

For the RAMAN spectroscopy, one drop of the ACCasestandard solution was dispensed on a silicon nitride surface(tip material), in order to verify the mechanism of absorptionand its film assembly on Si3N4. All the functionalization stepswere also carried out on a silicon nitride surface with the samepurpose. RAMAN spectra were obtained using a hand-heldspectrometer (FirstGuardTM/Raman Analyser V2.2.0), with a1064 nm laser as the excitation source. The output of the laserpower was set at 300 mW, and the integration time was set at5000 ms. The program OriginPro 8 was used for the statisticalanalysis.

III. RESULTS AND DISCUSSION

A. Molecular Docking and Molecular Dynamics

The herbicide diclofop inhibits ACCase enzyme by blockingone of the two available active sites [54]. The blockageoccurs via electrostatic and van der Waals interactions ofthe diclofop to the following residues: Tyr1738A, Gly1998B,Val2002B, Gly1734A, and Phe1956B Ala1627A (see Fig. 2).The Phe1956B residue was removed from Fig. 2(b)–(d) inorder to make the binding site easy to view. The processof the ACCase inhibition was reproduced according to thedocking methodology [Fig. 2(a)] and the residues were purplecoloured as can be seen in Fig. 2(b)–(d). Fig. 2(b) shows thebest conformation and position of the diclofop (in green), itwas obtained from crystallographic data, and Fig. 2(c) and (d)show the best positions and conformations obtained from themolecular docking calculations to the diclofop and E-I systemset. The final docked position is almost the same from thecrystallography achievements.

Molecular Dynamics simulations were carried out to deter-mine the binding energies of the clustered docking posi-tions. These calculations were performed considering thefluctuations and mobility of the set in aqueous solution.

BUENO et al.: NANOBIOSENSOR FOR DICLOFOP DETECTION 1471

TABLE II

CALCULATED INTERACTION ENERGIES OF THE

REPRESENTED SYSTEM. THE ENERGIES

WERE SCORED IN kcal/mol

Each system was initially energy-minimised and energy-equilibrated during 1 ns.

Table II shows the electrostatic and van der Waals (vdW)contributions to the interaction forces that kept the ligandsattached to the ACCase enzyme. When the inhibitor diclofopoccupied the binding site of the ACCase enzyme, the electro-static interactions were the primary contribution to the totalenergy. In contrast, when this inhibitor bonded to the cross-linker, the vdW interactions were the primary contribution forthe total energy.

At the end of the inhibition process, one of the oxygenof the carboxyl group from the diclofop was available insolution, whereas the remaining oxygen was incorporatedinto a hydrophobic pocket formed by the residues in thereaction. Comparing the binding energies of the E-I systemand the diclofop inhibitor in Table II, it shows that the oxygeninteraction has facilitated the inhibition process in the E-Isystem. This result corroborated the experimental results.Theoretically, the diclofop herbicide could be assembled andattached to the mica substrate surface and the ACCase enzymeon the AFM probe without damaging the molecule’s abilitiesto inhibit and be inhibited.

Therefore, the binding energy results from the simulationsshowed that the diclofop retained its features of being aspecific inhibitor of the ACCase enzyme even after immobi-lization on mica substrate. In the following section, we presentexperimentally how the functionalization method works andthe results from experimental analysis, where the signal ofthe AFM force curves comes from the herbicide-binding siteinteraction. In this context, we have determined the properorientation of the enzyme on a functionalized AFM tip throughMD simulations [32]. Also, enzyme-enzyme interaction hasbeen simulated at the presence of the functionalized surface(results not reported) and we have estimated the number ofactive interactions by assuming an energetically favorableorientation of the enzymes on a surface. Therefore, it ispossible to infer that the number of enzyme binding sitesavailable by calculating the unbinding energy of one diclofopmolecule through steered molecular dynamics. Depending onthe number of binding sites available, it is possible to integratethe simulated force curve in order to infer a theoretical valueto a specific substrate interacting with a target enzyme.

B. Using Raman Spectroscopy for the Analysis andMonitoring of the Nanobiosensor Assembly

The probe functionalization process was characterized usingRaman Spectroscopy. The Raman spectrum for the ACCase

TABLE III

COMPARISON OF BAND ASSIGNMENTS FOR THE RAMAN SPECTRA OF THE

ACCASE AND AFTER THE FINAL FUNCTIONALIZATION STEP

was characterized by five peaks, which could be associatedwith the final Raman spectrum of the nanobiosensor (whichis labelled in light grey in Table III).

Table III shows that functionalization with the ACCaseenzyme produced similar peaks in the 450-460 cm−1 regionin the Raman spectra. These peaks could be assignedto carbon out-of-plane bending vibrations [55]; in the990-1050 cm−1 region, the peaks corresponded to the C-Nvibration / NH2 vibration [56]: the signal increased inthis region in the nanobiosensor spectrum because ofthe large number of C-N bonds in the enzyme structure.The signals in the final regions of the spectrum (1367and 1462 cm−1) could be attributed to COO− stretchingvibrations / CH deformation / C-NH2 stretching and CH3and / or CH2 deformations [56], [57]. These similar values(nanobiosensor and standard) show that the enzyme wascovalently attached to the silicon nitride surface.

C. Agrochemical Detection by Nanobiosensor

Topographical images were used to verify the absorption ofthe molecules on mica. The profile studies (data not shown)provide the dimensions for the surrounding area of the filmassembled on mica, from where can be deduce that the heightof this films corresponds to 2.4 nm and 2.93 nm. Basedon the number of carbon-carbon bonds and considering theaverage distance from 0.1 to 0.15 nm for each connectionat the molecule, it is suggested that a monolayer of APTESform a self-assembled monolayers [42] of 0.5 nm of height.Likewise, one glutaraldehyde molecule might have a heightof approximately 1 nm. The data from the three-dimensionalstructure of molecules of diclofop and imazaquin, for example,is estimated to have a height of ∼1.5 nm and 1.4 nm, respec-tively. Added to an arrangement with APTES-glutaraldehydepolymerization of 1.5 nm formed in the functionalizationprocess, corresponding to the total height of the film.

The AFM force curves were used to quantify the inter-actions between the AFM tip and the sample. The curvescorresponded to two force components: a curve approximation(not shown) representing a repulsion event and a withdrawalcurve, which could have negative components when therewas a chemical interaction between the AFM tip and thesubstrate. In this paper, the contact mode, which produces

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Fig. 3. AFS force curves recorded in a PBS solution buffer at pH 7.0 for thedeveloped agrochemical nanobiosensor: the blue curve represents a specificrecognition event for a single biomolecular complex formed by the interactionbetween ACCase and diclofop (the specific chemical that inhibits the enzymeaction); in contrast, the other curves represent nonspecific recognition eventswith low, almost null adhesion forces.

force curves as a function of distance, was used to confirm thesystem architecture and the specific recognition between theenzyme and the herbicide. The force curves were performedin an aqueous medium, in which the interactions were weakand were thus in agreement with the results of [58] andNoy et al. [59]. Fig. 3 presents the force curves obtained forthe functionalized AFM tip and substrates modified with fourdifferent herbicides (i.e., 3 nonspecific herbicides, imazaquin,metsulfuron, and glyphosate, and 1 specific herbicide, diclo-fop). A simple interpretation of the force curves shown inFig. 3 from the aforementioned data is that the shape of theadhesion curve (i.e., the withdrawal curve) can be attributedto the selectivity and specificity of the nanobiosensor for theherbicide under consideration (diclofop). More than one set ofmolecules may have participated in the recognition process.

Fig. 3 shows that the magnitude of the diclofop adhesionforces was 85% higher (6.0±1.0nN) than those of the her-bicide imazaquin (0.8±0.1nN) and 93% higher than thoseof the herbicide metsulfuron (0.3±0.1nN). These differencesin the adhesion forces were expected because these herbi-cides (imazaquin and metsulfuron) inhibit the action of theenzyme acetolactate synthase (ALS) [60]–[62] and conse-quently are non-specific ligands of (i.e., do not inhibit) theenzyme ACCase. Likewise, the magnitude of the diclofopadhesion forces was 90% higher than the herbicide glyphosate(0.6±0.1 nN). This difference in the adhesion forces wasalso expected because the herbicide glyphosate inhibits theenzyme EPSP (5-enolpyruvyl shikimate-3-phosphate synthase)[63]–[65] and consequently, is also a non-specific ligandof (i.e., does not inhibit) the enzyme ACCase. The totaldifference between the values for specific recognition (forthe diclofop inhibitor) and nonspecific recognition (for the

Fig. 4. AFS force curves recorded in a PBS solution buffer at pH 7.0 forthe control experiment for substrates without any agrochemical coating andby saturating the tip with the complementary blocking agent (inset).

non-inhibitors imazaquin, metsulfuron and glyphosate) wasapproximately 90%. This result validated the selectivity andspecificity of the nanobiosensor.

Control experiments, in which the biorecognition processwas inhibited or invalidated, were used to confirm the speci-ficity of the detected specific recognition events on statisticalgrounds, besides to verify and calibrate the influence ofcoatings by functionalization process. The control experimentswere performed on substrates without any agrochemical coat-ing (Fig. 4) and by saturating the tip with the complementaryblocking agent (i.e., the anti-ACCase antibody) (Fig. 4 inset).On average, a significant decrease of 98% of the probabilityof adhesion in the force curves was observed for these controlexperiments. The modulation in the vertical lines showed inthe force curves of the control experiments occurred due theprobe–sample interactions. Interactions of this nature generallyalter the overall spring constant of the system.

Since the ACCase enzyme is inhibited by diclofop in vivo,and this inhibition depends on a specific recognition process,the control experiments showed lower values of adhesionforce compared to the results previous discussed and occursessentially by van der Waals interactions or by electrostatic,hydrophilic, and hydrophobic interactions, among others [42].

As a final point, the histograms in Fig. 5 were used todetermine several specific adhesion force values. The mostimportant results were as follows: at a given loading rate,the most probable adhesion force (which can be used tostandardise sensor operation on a large scale); the differencebetween the adhesion forces for each group of herbicides(specific and nonspecific); and the statistical probability of theadhesion values that appear in the experimental measurements.All the peaks for the different statistical distributions of theherbicides, such as the adhesion force of the recognition event,could be fit with a Gaussian function.

Comparing this system with other biosensors defined inthe literature is challenging because of the many differencesbetween our system and the systems in other studies, suchas the physical and chemical nature of the molecules, thesurface topography and characteristics, the charges involvedin the recognition event, the different transduction modes,

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Fig. 5. Adhesion force histograms (n=19) for the occurrences (relativefrequency) of adhesion force values for the sensing systems at the nanoscale:(A) ACCase/imazaquin; (B) ACCase/metsulfuron; (C) ACCase/glyphosate;and (D) ACCase/diclofop; the histograms were fitted to Gaussian functionsto identify the most probable adhesion force value to determine a standardvalue for specific and nonspecific recognition events and signals.

and the functionalization method used for the nanobiosensoractive area. This is the first report of the detection of an APPherbicide by an AFM-tip-based nanobiosensor.

IV. CONCLUSION

The results from this study provide important guidanceon designing highly sensitive and selective AFM-tip-basednanosensors using biomimicry of the action mechanism ofherbicides on plants. Surface functionalization was success-fully used to attach sensory and target molecules to the AFMtip, which enabled the detection of small concentrations inreal time under near-physiological conditions and withoutlabelling. In this context, the novelty and advantage of AFMsensors are, among other facts, the detection limits (as AFMresolution limits) that this system can reach: the range ofpictogram and, in the best scenario, the detection of a singlemolecule with a very narrow and sharpened tip, such asa carbon nanotube tip and a controlled and standardizedfunctionalization process.

The experimental results showed that the specific inter-action was the primary contribution to the greater adhesionforce, especially for the ACCase-diclofop interaction, becausediclofop is a specific inhibitor for ACCase. The differencebetween the specific and nonspecific recognition values was90% on average. The theoretical results also provided adirect interface with the experimental research, showing thatnanosensor molecules could be assembled and attached to themica substrate surface and the AFM probe without damagingthe molecules’ mode of action. Studies are currently beingcarried out to determine the theoretical and experimental forceof the interactions between the herbicides and their inhibitorsand we have already begun investigating the response behaviorof the sensor for samples in which several agrochemicalgroups interact in a solution. More work is needed to improvethe AFS signals, and because of that, our research group isconcentrating efforts to figure how the immobilization processworks, in order to estimate, as accurate as possible, the

forces involved on herbicide-enzyme interactions as well asthe improvements to the nanoscale detection device to monitorresidues in the environment.

ACKNOWLEDGMENT

The authors would like to thank Mr. L. Bonugli forhis significant contributions to the AFM measurements,Mr. J. R. Castro for technical assistance and the Institute ofPhysics Gleb Wataghin for the experimental assays support.Also, the authors acknowledge FAPESP (Proc. 2007/05089-9; Proc. 2013/09746-5; Proc. 2009/09120-3), CAPES, CNPq,FAPEMIG (CEX - APQ-02176-11) and nBioNet for theirfinancial support.

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Carolina Castro Bueno received the Degree inenvironmental engineering from Pontificia Univer-sidade Catolica de Campinas in 2009, and theM.S. degree in materials science - nanotechnol-ogy and nanoscience in the area of microscopyand atomic force spectroscopy with emphasis onnanoscopy/nanobiosensors for monitoring environ-mental quality in agriculture in 2013. She hasworked in the areas of nanotechnology, nanoscienceand atomic force microscopy, with emphasis on stud-ies that involve the construction of smart surfaces

for creating nanobiosensors. Currently, she is pursuing the Doctoral degree inenvironmental sciences working with Biochar development and engineeringat Universidade Estadual Paulista.

Adriano Moraes Amarante was born in Soro-caba, Brazil. He received the Degree in bio-physics technology from the College of TechnologySão Paulo, FATEC, Sorocaba, in 2006, the B.Sc.degree in physics from the University of São Carlosin 2013, and the M.Sc. degree in materials scienceand engineering from the University of São Carlosin 2013. Since 2007, he has been developing severaldifferent types of computational simulation to inter-pret biological sensors-based to be used as probesfor agriculture and diseases detection. His research

activity includes molecular dynamics simulation and experimental-theoreticalinterpretation. Since 2009, he has been a Researcher with the FederalUniversity of São Carlos, Brazil, and a member of the NanoneurobiophysicsResearch Group.

Guedmiller S. Oliveira received the B.S. and M.S.degrees in physical chemistry from the FederalUniversity of Uberlândia and the Ph.D. degree inphysical chemistry from the Federal University ofSão Carlos, Brazil, in 2006, 2009, and 2013, respec-tively. Since 2007, he has been working with com-puter simulation of the matter, providing an atomisticpoint of view of the experimental procedures. In2012, he was visiting the University of Houston,Biology and Biochemistry Laboratory, supervised byProf. J. M. Briggs, where he developed a theoretical

model to interpret the immobilization of biological systems on functionalizedsurfaces for nanosensors applications. His knowledge base incorporates quan-tum mechanics theory, molecular dynamics simulation, and it combines resultsfrom experimental and theoretical analysis through statistical thermodynamicsto better understand macromolecular phenomena. Currently, he is a Post-Doctoral Researcher at the Federal University of São Carlos, Sorocaba.

Daiana Kotra Deda received the B.Sc. degree inchemistry from the State University of Midwestern,Brazil, in 2006, and the Ph.D. degree in inorganicchemistry from the University of São Paulo, Brazil,in 2011, for research on the development of nanoen-capsulated photosensitizers for cancer treatment byphotodynamic therapy. Currently, she is a post-doctoral with the Chemistry Institute, Universityof São Paulo. Her main research interest is thedevelopment and characterization of nanomaterialsand the study of their interaction with biological

systems, in vitro and in vivo toxic effects and their application as alternativemethods for treatment and diagnosis of diseases.

Omar Teschke was born in Santo Angelo, Brazil,in 1944. He received the B.S. degree in electri-cal engineering from Universidade Federal do RioGrande Sul, Porto Alegre, in 1967, the M.S. degreein electrical engineering from Universidade Catolicado Rio de Janeiro in 1969, under the supervisionof Prof. S. M. Rezende, and the Ph.D. degree inelectrical engineering from the University of Cali-fornia Berkeley in 1975, under the supervision ofJ. R. Whinnery. After teaching at the UniversidadeCatolica do Rio de Janeiro from 1969 to 1971, he

was a Visiting Staff Member with Bell Laboratories Holmdel, NJ, USA, from1975 to 1977. He joined the Departamento de Fisica Aplicada, Instituto deFisica, UNICAMP, in 1977. Currently, he is a Professor and his researchinterest are interfacial processes including self-organization and self-assemblyand experimental techniques for probing the liquid state, specially interfacialprocess in living species.

Eduardo de Faria Franca is a Professor of the-oretical chemistry with the Federal University ofUberlândia. He received the Degree from the FederalUniversity of Uberlândia in 2003, and the Ph.D.degree in chemical physics from the Federal Uni-versity of São Carlos in 2009, where he workedwith Prof. L. C. G. Freitas. As a trainee in thePacific Northwest National Laboratory, he was withProf. R. D. Lins and developed a new force-field forthe chitin and chitosan biopolymers and elucidatednew insights about these natural polymers using

molecular dynamics simulation. In 2009, he was a post-doctoral with thepolymer group: Prof. Bernhard Gross, University of São Paulo, to helpon the development of a new nanobiossensor using an AFM microscope.His theoretical work assisted the construction of a sensible and selectivebiosensor capable to detect herbicides in nanomolar concentration. He joinedthe Federal University of Uberlândia as a Professor of chemistry in 2010. Hehas been the author or co-author of many publications in theoretical chemistryand crystallography since 2002. His actual projects include the developmentbiosensors, antitumor drugs, biofuels, and the use of biopolymer chitosan forwaste water treatment and production of artificial tissues.

Fábio L. Leite was born in Itanhaem, Brazil.He received the B.Sc. degree in physics fromSão Paulo State University, UNESP, Rio Claro, in2000, and the M.Sc. and Ph.D. degrees in mate-rials science and engineering from the Universityof São Paulo, São Carlos, in 2002 and 2006,respectively. Between 2007 and 2008, he was aPost-Doctoral Researcher with the Alan GrahamMacDiarmid Institute of Innovation and Business,Embrapa Agricultural Instrumentation (Embrapa)with Dr. O. N. de Oliveira Jr., Dr. L. H. C. Mattoso

(Embrapa) and Alan Graham MacDiarmid, University of Pennsylvania NobelPrize in chemistry in 2000. His efforts in the MacDiarmid Institute focusedon conducting polymers, nanosensors and atomic force microscopy withenvironmental applications. Since 2009, he has been an Assistant Prof. andResearcher with the Federal University of São Carlos, Sorocaba, and theHead of the Nanoneurobiophysics Research Group. He is the author ofover 40 published papers, two books, ten book chapters, and two patents.His research interests are related to the development of nanobiosensors usingAFM and computational nanotechnological for application in studies of avariety neurodegenerative and autoimmune diseases.