Silica and graphene mediate arsenic detection in mature rice grain by a newly patterned current-volt aptasensor

Abstract

Arsenic is a major global threat to the ecosystem. Here we describe a highly accurate sensing platform using silica nanoparticles/graphene at the surface of aluminum interdigitated electrodes (Al IDE), able to detect trace amounts of arsenic(III) in rice grain samples. The morphology and electrical properties of fabricated Al IDEs were characterized and standardized using AFM, and SEM with EDX analyses. Micrometer scale Al IDEs were fabricated with silicon, aluminum, and oxygen as primary elements. Validation of the bare Al IDE with electrolyte fouling was performed at different pH levels. The sensing surface was stable with no electrolyte fouling at pH 7. Each chemical modification step was monitored with current-volt measurement. The surface chemical bonds were characterized by fourier transform infrared spectroscopy (FTIR) and revealed different peaks when interacting with arsenic (1600-1000 cm^-1). Both silica nanoparticles and graphene presented a sensitive limit of detection as measured by slope calibration curves at 0.0000001 pg/ml, respectively. Further, linear regression was established using ΔI (A) = 3.86 E^-09 log (Arsenic concentration) [g/ml] + 8.67 E^-08 [A] for silica nanoparticles, whereas for graphene Y = 3.73 E^-09 (Arsenic concentration) [g/ml] + 8.52 E^-08 on the linear range of 0.0000001 pg/ml to 0.01 pg/ml. The R² for silica (0.96) and that of graphene (0.94) was close to the maximum (1). Modification with silica nanoparticles was highly stable. The potential use of silica nanoparticles in the detection of arsenic in rice grain extract can be attributed to their size and stability.

Figure 1

Morphological characterization using high resolution microscopy. Images captured for silica nanoparticles with FESEM (a) & FETEM (b); Graphene with FESEM (c) & FETEM (d). X-ray diffraction spectra (e). The comparison of diffraction peaks received between silica nanoparticles and graphene is displayed.

Figure 2

Design and characterization of bare Al IDE. (a) Design specifications of the proposed Al IDE were drawn using AutoCAD software. All measurements and dimensions were well presented. (b–d) Images of Al IDE under a scanning electron microscope at 5.50 and 500 µM. The fingers of channel gaps, the electrode pad, and the working electrode of the Al IDE are presented. The result indicated that the fabricated electrode followed all the specifications and dimensions provided by the layout scale. The figure inset represents a magnification of an SEM image. (e) EDX pattern analysis indicated the device contained the elements Si, Al, and O. (f) AFM topography images showed in detail that the finger gaps did not suffer any damage. (g) The height of the finger gap was measured starting at the bottom line to confirm the specifications of Al IDE.

Figure 3

Electrical characterization measurements with a Source Meter Keithley 2450, using the Kick Start software. (a) Probe station with Al IDE biosensor. (b) I–V characteristics of bare Al IDE with Si substrate. The figure inset indicates the reproducibility using five bare Al IDEs. (c) Electrolysis is a procedure where an electric current is passed through a liquid to produce a chemical transition. The experiment was performed in an electrolytic system, an apparatus composed of positive and negative electrodes separated and immersed in a solution of positive and negative ions. In this case, the mechanism for transferring ions between Al IDE finger electrodes. (d) I–V measurements with different pH ionic solutions. The electrodes were tested in environments ranging from acidic (pH 1, 3, 5) to alkaline (pH 9, 11, 12). The figure inset indicates the difference in the current corresponding to each ionic solution and its respective pH.

Figure 4

Schematic representation of the strategy for the detection of arsenic using Al IDE. (a) Sample preparation of rice grains. At first, 1.3 g of grains were smashed into small pieces and placed in conical flasks. Next, the samples were perfused with 15 ml of nitric acid, followed by 5 ml perchloric acid, and heated at 90 °C. At this stage, the samples acquired a yellow color and they were heated and filtered. All filtered solutions were diluted with 50 ml of distilled water and ready to be used. (b) I–V measurement based on surface immobilization and functionalization. CDI and streptavidin were used to modify the sensor surface of Al IDEs. Then, the remaining surface was blocked by ethanolamine. The biotinylated aptamer was immobilized on the IDE sensing surface. (c) FTIR analysis was used to evaluate the surface functionality at every stage of the modifying process of the Al IDE until reaching the target measurement. (d) Analysis of the interaction between biotinylated aptamer and arsenic at concentrations ranging from 0.01 µg/ml to 0.001 ng/ml. (e) Current measurements at the different concentrations of arsenic using the biotinylated aptamer Al IDEs.

Figure 5

Surface functionalization measured by voltammetry signal. The inset graph represents the current values recorded with bare, CDI-silica or CDI-graphene, and streptavidin Al IDEs modified with (a) silica nanoparticles and (b) graphene. The figure insets represent enlarged portions of bare IDE, CDI-Silica or CDI-Graphene and Streptavidin. We evaluated the impact of the interaction between (c) CDI-silica nanoparticles or (d) CDI-graphene. The current measurements for different concentrations of arsenic detected with (e) CDI-silica and (f) CDI-graphene Al IDEs are presented here.

Figure 6

The current changes at various arsenic concentrations at 1 V, indicating the limit of detection (LOD) measured after modifications with (a) silica nanoparticles or (b) graphene. Analysis of spikes of the mature rice grains was conducted to determine the realistic application of silica or graphene nanomaterials-modified sensor surface for the detection of the target analyte, As(III). (c) A stability test was conducted for four weeks using I–V measurement to evaluate the reproducibility and precision of the developed sensor for both nanomaterials. (d) Determination of non-fouling in the extract of rice grains at a dilution of 1:100. (e) Analysis of spikes with a real sample using the silica nanoparticles modification.