Graphene Nanoparticle-Based, Nitrate Ion Sensor Characteristics

Abstract

Gathering and sensing of nitrate ions in the environment due to the abundant use in industry and agriculture have become an important problem, which needs to be overcome. On the other hand, new materials such as carbon-based materials with unique properties have become an ideal choice in sensing technology. In this research, the high-density polyethylene (HDPE) polymer as a carbon source in the melted form was used and carbon nanoparticles in the form of a strand between two electrodes were analyzed. It was fabricated between copper electrodes by the pulsed arc discharge method. Subsequently, the constructed metal–nanoparticle–metal (MNM) contact was employed to recognize the nitrate ions. Therefore, NaNO₃, Pb(NO₃)₂, Zn(NO₃)₂, and NH₄NO₃ samples as a usual pollutant of industrial and agricultural wastewater were examined. All nitrate compounds in ten different densities were tested and sensor I-V characteristic was investigated, which showed that all the aforesaid compounds were recognizable by the graphene nano-strand. Additionally, the proposed structure in the presence of ions was simulated and acceptable agreement between them was reported. Additionally, the proposed structure analytically was investigated, and a comparison study between the proposed model and measured results was carried out and realistic agreement reported.

1. Introduction

Nitrate ions are widespread in the environment due to their high solubility in most liquids. These ions are known as water pollutants, which can be found in industrial and agricultural wastewater and have been considered to be an important threat to environmental waters [1,2]. The main source of nitrate ions produced by the human is non-organic fertilizers, food preservatives, reactive in explosive materials, glass industry, and other chemical processes. In surface water, the normal amount of nitrate ions is in the order of a few tens of ppm, but a high density of these ions has been produced by agriculture and urban wastewater [3]. Nitrate ion accumulation in drinking water can lead to serious and dangerous disease [4,5,6,7]. Up until now, different methods such as fluorescent spectroscopy, Raman spectroscopy, and chromatography have been utilized to find nitrate ions, but they need complicated instruments and are expensive [8,9,10,11,12,13,14,15,16]. However, current-voltage analysis as a sensing element in three or two-terminal devices with high sensitivity, requiring cheap facilities and proper for direct analysis, have been focused on [17]. After the discovery of nanoscale carbon-based materials [18], many studies have been carried out to use them in electronic devices [19,20]. According to their geometry, they can be conductive or semiconductor, which has diversified their application in diodes, transistors, and sensors [21,22,23,24]. A carbon-based sensor, which converts one physical phenomenon to an electrical signal, can be employed in identification systems [25,26,27,28,29]. Among the studies that have concentrated on the utilization of various nanoparticles in nitrate/nitrite detection sensors in the recent five years, K.R. Venugopala Reddy et al. (2020) employed cobalt (II) tetra methyl-quinoline oxy bridged phthalocyanine carbon nanoparticles [30,31,32,33,34,35,36,37,38,39], and Kattar Hanane et al. (2020) suggested tetradodecyl ammonium nitrate (TDAN) application [31]. Additionally, more sensors by Saad S. M. Hassan et al. (2019) Multi-walled carbon nanotubes (MWCNTs) have been investigated [32]; Lei Wu et al. (2018) employed Cu₂O/CNT composites [33]; Yi Zhang et al. (2018) proposed a Ag/Cu/MWNTs/GCE platform [34]; Abdel Hameed et al. (2018) supported Cu@Pt/Gr nanoparticles on graphene configuration [35]; the Ghanei-Motlagh Taher (2018) Ag/HNT/MoS₂ arrangement was configured [36]; Yue Wan et al. (2017) approved the application of a AgNP/MWCNT/GCE structure [37]; Bagheri et al. (2017) hired a Cu/MWCNT/RGO/GCE assembly [38]; and finally, Menart et al. (2015) worked on AgPs [39]. However, in this research, a graphene nanoparticle-based strand was fabricated by the pulsed arc discharge method between two metal electrodes. Due to the Fermi-level difference between the metal electrodes and graphene nanoparticle-based strand, in the shaped sensor, a Schottky-barrier was formed and examined. By adding a small amount of nitrate ion impurity to the sensor, the number of carriers varied and the I-V characteristic was altered, which led to electrical resistance variability in the strand, therefore, the resistance deviation in the nanoparticles was used in the measurement of physical phenomena. Its advantages over other nitrite reported sensors in the form of low manufacturing cost, fast production speed, and high efficiency can be highlighted.

2. Experimental Measurements

The entire experimental setup for carbon nanoparticle growth is presented in Appendix A; however, the schematic of the fabricated graphene nanoparticles (GNPs) in the melted high density poly ethylene (HDPE) composite is shown in Figure 1. A pulsed arc discharge method is employed in the presented work. In this mechanism, the voltage between two metal electrodes can be changed from 1–20 KV in low sequences.

Two metal electrodes fixed on a glass substrate for easy nitrate ion injection into the sensor, hollow stainless-steel electrodes were employed. Three arrangements in the placement of electrodes on the substrate can be recognized such as plane to plane (PTP), tip to the plane (TTP), and tip to tip (TTT) configurations, which deepens the electrode shape. To increase the growth rate in the presented work, the TTT was employed. To synthesize stable carbon nanoparticles, melted high-density polyethylene (HDPE, (C₂H₄)_n), due to a large number of carbon atoms in its chain, was used as a carbon source between the two electrodes. At atmospheric pressure, the HDPE was melted and placed between two electrodes. The high electric field graphene nanoparticles were synthesized between two electrodes within 24 s at the voltage of about 4.4 kV. Initially, the fabricated strand was analyzed by the nano-focus (Mahr Metering Systems GmbH, Göttingen, Germany) as shown in Figure 2e, which indicates the topological presence of carbon nanoparticles in the sensor. For a closer look, images of the scanning electron microscope (SEM) (TESCAN, Brno, Czech Republic) were taken as depicted in Figure 2a for 200 nm, Figure 2b for 500 nm, Figure 2c for 1000 nm, and Figure 2d for 2000 nm.

Among the various kinds of carbon nanoparticles, graphite, graphene, and CNTs, particularly the high electrical conductivity of CNTs, have been reported. Additionally, based on the results presented by [40,41,42] and a comparison of the scanning electron microscopy (SEM) images in Figure 2a–d with SEM images taken by [43], coated CNTs by HDPE can be concluded similarly to the composites presented in [43]. Furthermore, bundled multiwall carbon nanotubes (MWCNTs) coated by HDPE chains with a similar manufacturing process have also been explained in our previous works [43]. Moreover, the Fourier transform infrared (FTIR) analysis as shown in Figure 2f indicates the composite form of GNPs in HDPE. The peaks at 2926 cm⁻¹ and 2853 cm⁻¹ and 725 cm⁻¹ signify the CH₂ groups in hydrocarbons [43]. The peak at 3443 cm⁻¹ [43] and 3412 cm⁻¹ [44] in the Fourier transform infrared (FTIR) (JASCO, Easton, MD, USA) spectrum of the GNPs can be assigned to the O-H vibration in the carboxyl group and the peaks at 1020–1090 cm⁻¹ can be assigned to CNT-COOH and peaks at 1120 cm⁻¹ signify the C=C, CNT, and the backbone of carbon nanotubes and peaks at 3700–3800 cm⁻¹ can be assigned to CNT, OH groups from the unbound [45]. Based on research in [43,44,45,46,47,48,49] and a comparison with the FTIR analysis taken from the sample, it can be concluded that the carbon nanoparticles grown also contained multiwall carbon nanotubes and GNPs.

After the growth of carbon nanoparticles between two electrodes, to examine their sensing phenomenon, solutions prepared in 10 different concentrations of four nitrate ions—NaNO₃, Pb(NO₃)₂, Zn(NO₃)₂, and NH₄NO₃ in distilled water—for injection into the built sensor and the identification of nitrate ions tested. Therefore, the amount of nitrate ions in each solution was measured and prepared according to the minimum allowable amount of different nitrate ions in drinking water in the World Health Organization (WHO) guidelines [50]. First of all, NaNO₃, as a common water pollutant from agricultural and industrial activities in 10 solutions was tested. To prepare the desired solution, the required amount of NaNO₃ was measured and dissolved in distilled water. The 50 ppm, 100 ppm, 200 ppm, 500 ppm, 1000 ppm, 2000 ppm, 3000 ppm, 4000 ppm, and 5000 ppm solutions were selected.

A current-voltage curve was taken from the fabricated sensor before ion injection and then each solution was injected into the device and subsequently an I-V curve was taken for each one. The I-V characteristic of the experimental data for this sensor without/with different NaNO₃ concentrations is plotted in Figure 3, which indicates that the sensor current-voltage characteristic is affected by NaNO₃ ion concentrations. The experimental results can be fitted by the Fourier model as:

f(x) = a₀ + a₁cos(xw) + b₁sin(xw) + a₂cos(2xw) + b₂sin(2xw) + a₃cos(3xw) + b₃sin(3xw) + a₄cos(4xw) + b₄sin(4xw) + a₅cos(5xw) + b₅sin(5xw) + a₆cos(6xw) + b₆sin(6xw) + a₇cos(7xw) + b₇sin(7xw) + a₈cos(8xw) + b₈sin(8xw)

(1)

where the fitting parameters (a_0–8, b_1–8, w) are calculated as shown in

Table 1. Values for parameters of the Fourier model and the corresponding regressions for NaNO₃ exposure.

General Model Fourier: f(x) = a0 + a1cos(xw) + b1sin(xw) + a2cos(2xw) + b2sin(2xw) + a3cos(3xw) + b3sin(3xw) + a4cos(4xw) + b4sin(4xw) + a5cos(5xw) + b5sin(5xw) + a6cos(6xw) + b6sin(6xw) + a7cos(7xw) + b7sin(7xw) + a8cos(8xw) + b8sin(8xw)
	Without NaNO3	50 ppm NaNO3	100 ppm NaNO3	200 ppm NaNO3	500 ppm NaNO3	1000 ppm NaNO3	2000 ppm NaNO3	3000 ppm NaNO3	4000 ppm NaNO3	5000 ppm NaNO3
a0	2577	−4.365 × 109	1.645	2.049	2.825	3.492	−6.718 × 1010	−7.474 × 104	24.02	5.223
a1	−3251	6.984 × 109	−0.7392	−1.475	−1.272	−1.534	1.079 × 1011	6.264 × 104	−4.804	−2.429
b1	−2916	3.161 × 109	−0.9671	−1.044	−1.855	−2.201	5.253 × 1010	1.172 × 105	−36.7	−3.127
a2	283.3	−3.409 × 109	−0.575	−0.7861	−1.003	−1.122	−5.268 × 1010	5.174 × 104	−29.84	−1.644
b2	2608	−3.881 × 109	−0.1857	0.267	−0.265	−0.4149	−6.713 × 1010	−7.71 × 104	1.621	−0.4729
a3	624.7	7.667 × 108	−0.3277	−0.1439	−0.4805	−0.6901	1.012 × 1010	−4.992 × 104	0.7631	−0.8112
b3	−869.5	2.515 × 109	0.1074	0.4591	0.2145	0.1724	4.668 × 1010	−5352	20.6	0.2365
a4	−261.9	1.259 × 108	−0.1208	0.18	−0.1587	−0.2735	4.854 × 109	7346	11.74	−0.4314
b4	57.75	−9.719 × 108	0.1971	0.2272	0.2232	0.3322	−2.006 × 1010	1.899 × 104	−0.3351	0.3087
a5	27.35	−1.329 × 108	0.02184	0.1596	−0.01027	−0.0307	−4.223 × 109	3940	−0.1503	−0.1108
b5	15.43	2.152× 108	0.1266	−0.007545	0.1752	0.2552	5.149 × 109	−4321	−5.373	0.3882
a6	0	3.356 × 107	0.05205	0.04654	0.07374	0.07706	1.372 × 109	−1017	−1.795	0.09396
b6	0	−2.263 × 107	0.0422	−0.07136	0.06253	0.1278	−6.39 × 108	−314.9	0.1281	0.1743
a7	0	−2.985 × 106	0.03	−0.01211	0.02794	0.05692	−2.166 × 108	8.98	0.06285	0.07132
b7	0	5.101 × 105	0.01038	−0.03823	−0.01528	0.02353	−2.25 × 106	92.4	0.3141	0.05917
a8	0	0	0.01524	−0.01777	0	0.03133	1.321 × 107	0	0	0.05525
b8	0	0	−0.0199	0.0005103	0	−0.008749	6.461 × 106	0	0	−0.001115
w	0.7738	0.4315	2.524	2.289	2.485	2.428	0.5159	1.027	1.76	2.592
R2	0.9998	1	0.9996	1	0.9998	0.9999	0.9998	0.9998	1	0.9998
SSE	0.0005779	0.0001708	0.0185	0.002341	0.04767	0.02591	0.08688	0.09976	0.03064	0.153
RMSE	0.006207	0.003941	0.02368	0.007057	0.02628	0.01823	0.03316	0.03329	0.01835	0.03764

for NaNO₃.

Based on the tabulated Fourier model (Table 1), the values for fitting parameters with corresponding regressions of NaNO₃ exposure were re-plotted as shown in Figure 4.

Another common water pollutant, namely NH₄NO₃, has many applications in agricultural fertilizers and explosives production, is known as a nitrate ion source, and needs to be considered [38]. Therefore, ten NH₄NO₃ solutions prepared like the previous sample and injected to the sensitive region of sensor and sensor response to the NH₄NO₃ ions were plotted as shown in Figure 5.

To compare the efficacy of these ion concentrations on the sensor operation, fitting parameters for NH₄NO₃ were calculated from the Fourier model as shown in

Table 2. Values for fitting parameters from the Fourier model and its corresponding regressions for NH₄NO₃ exposure.

General Model Fourier: f(x) = a0 + a1cos(xw) + b1sin(xw) + a2cos(2xw) + b2sin(2xw) + a3cos(3xw) + b3sin(3xw) + a4cos(4xw) + b4sin(4xw) + a5cos(5xw) + b5sin(5xw) + a6cos(6xw) + b6sin(6xw) + a7cos(7xw) + b7sin(7xw) + a8cos(8xw) + b8sin(8xw)
	Without NH4NO3	50 ppm NH4NO3	100 ppm NH4NO3	200 ppm NH4NO3	500 ppm NH4NO3	1000 ppm NH4NO3	2000 ppm NH4NO3	3000 ppm NH4NO3	4000 ppm NH4NO3	5000 ppm NH4NO3
a0	2577	1.907	−5.103 × 109	6.266	6.715	6.618	−4.655 × 106	7.381	11.73	11.23
a1	−3251	−0.5533	8.1 × 109	−2.979	−4.666	−6.012	4.44× 106	−10.42	−7.343	−8.179
b1	−2916	−2.046	3.832 × 109	−3.901	−3.432	−2.714	7.164 × 106	−1.082	−5.883	−6.337
a2	283.3	−0.7164	−3.829 × 109	−2.174	−2.345	−2.283	2.773 × 106	−2.319	−3.641	−4.802
b2	2608	−0.3164	−4.668 × 109	−0.5321	0.7776	2.101	−5.586 × 106	5.58	0.2512	1.815
a3	624.7	−0.4016	7.452 × 108	−1.108	−0.3724	0.5924	−3.716 × 106	2.811	−1.127	−0.6932
b3	−869.5	0.07183	2.98 × 109	0.5151	1.196	1.732	3.543 × 105	3.226	1.258	2.86
a4	−261.9	−0.1694	2.241 × 108	−0.3338	0.4025	1.112	1.076 × 106	2.83	−0.119	1.024
b4	57.75	0.06762	−1.123 × 109	0.6515	0.4651	0.1004	1.409 × 106	−0.4364	0.8636	1.368
a5	27.35	−0.1128	−1.762 × 108	0.1117	0.2453	0.3058	2.282 × 105	0.6937	0.3434	0.9994
b5	15.43	0.1618	2.372 × 108	0.3757	−0.02259	−0.5612	−6.053 × 105	−1.674	0.3403	0.1117
a6	0	0.04741	4.171 × 107	0.1399	0.02285	−0.211	−1.678 × 105	−0.4929	0.1789	0.3885
b6	0	0.07607	−2.222 × 107	0.1197	−0.08791	−0.2558	3.396 × 104	−0.813	0.03491	−0.3732
a7	0	0	−3.531 × 106	0.09189	0	−0.1269	2.033 × 104	−0.4073	0	0.05043
b7	0	0	1.672 × 105	−0.01911	0	0.04976	2.13 × 104	−0.01703	0	−0.2131
a8	0	0	0	0	0	0.005272	598.9	−0.07972	0	−0.02578
b8	0	0	0	0	0	0.03742	−2411	0.1107	0	−0.08339
w	0.7738	2.586	0.4214	2.403	2.129	2.125	1.032	2.102	2.044	2.291
R2	0.9998	0.9971	1	0.9998	0.9999	1	0.9998	1	0.9999	1
SSE	0.0005779	0.2887	0.0001825	0.09508	0.05321	0.000866	0.274	0.07641	0.295	0.1916
RMSE	0.006207	0.09978	0.004776	0.07962	0.05292	0.007137	0.07477	0.05224	0.0918	0.05089

.

When the fitting parameters for each concentration were calculated, the proper linear I-V characteristic was re-plotted as shown in Figure 6.

Like the sodium nitrate by concentration, the current also lifted on the ammonium nitrate (NH₄NO₃), and this similarity can be described by the ion concentration gradient.

Lead nitrate can be leaked to the environment from industrial activities, especially from battery manufacturing companies, which needs to be investigated carefully. Therefore, lead nitrate Pb(NO₃)₂ was tested with the same concentration as other nitrate ions, and the experimental response is plotted as shown in Figure 7.

Again, the Fourier model was used for fitting, and lead nitrate-related fitting parameters were carried out as shown in

Table 3. Values of the fitting parameters of the Fourier model and its corresponding regressions for Pb(NO₃)₂ exposure.

General Model Fourier: f(x) = a0 + a1cos(xw) + b1sin(xw) + a2cos(2xw) + b2sin(2xw) + a3cos(3xw) + b3sin(3xw) + a4cos(4xw) + b4sin(4xw) + a5cos(5xw) + b5sin(5xw) + a6cos(6xw) + b6sin(6xw) + a7cos(7xw) + b7sin(7xw) + a8cos(8xw) + b8sin(8xw)
	Without Pb(NO3)2	50 ppm Pb(NO3)2	100 ppm Pb(NO3)2	200 ppm Pb(NO3)2	500 ppm Pb(NO3)2	1000 ppm Pb(NO3)2	2000 ppm Pb(NO3)2	3000 ppm Pb(NO3)2	4000 ppm Pb(NO3)2	5000 ppm Pb(NO3)2
a0	2577	−9.734 × 108	−192.8	1.571	5.862	3.13	5.437	4.367	7.275	9.145
a1	−3251	1.461 × 109	73.86	−0.9443	0.7388	−1.163	−1.114	−2.161	−2.69	−6.585
b1	−2916	9.46 × 108	338.7	−1.085	−6.503	−1.938	−4.589	−2.807	−5.421	−6.735
a2	283.3	−5.082 × 108	222.6	−0.5231	−4.109	−0.8636	−2.563	−1.751	−3.8	−6.326
b2	2608	−1.132 × 109	−104.2	−0.06866	−3.632	−0.4929	−1.831	−0.1014	−1.165	1.942
a3	624.7	−1.054 × 108	−83.05	−0.2765	−3.572	−0.5539	−2.153	−0.7292	−2.509	−0.4802
b3	−869.5	6.893 × 108	−106.6	0.2509	1.209	−0.1498	0.6529	0.5688	1.685	5.391
a4	−261.9	2.008 × 108	−34.67	−0.01229	−0.286	−0.354	−0.4248	−0.1055	−0.05805	3.412
b4	57.75	−2.268 × 108	42.77	0.1986	2.28	0.1775	1.335	0.5735	2.125	1.687
a5	27.35	−9.469 × 107	13.63	0.1012	0.9668	−0.1713	0.4745	0.1827	1.086	1.597
b5	15.43	2.661 × 107	6.547	0.1031	0.6424	0.1267	0.5844	0.2369	0.7821	−1.619
a6	0	2.152 × 107	0.4214	0.06874	0.4006	−0.0414	0.3186	0.1304	0.6822	−0.4063
b6	0	6.588 × 106	−2.137	0.007466	−0.2295	0.1633	−0.03974	0.02699	−0.2572	−0.9014
a7	0	−2.098 × 106	0	0.02841	−0.0006677	0.01672	0.02892	0.05148	0.08045	−0.3317
b7	0	−2.471 × 106	0	−0.03931	−0.1262	0.0548	−0.07618	−0.02689	−0.3162	0.0615
a8	0	2.989 × 104	0	−0.01232	0	0.003648	0	0	−0.04208	0
b8	0	2.194 × 105	0	−0.01392	0	0.0655	0	0	−0.08274	0
w	0.7738	0.5184	1.245	2.495	2.003	2.516	2.132	2.356	2.175	1.911
R2	0.9998	1	0.9999	0.9996	0.9996	0.9999	0.9999	0.9999	0.9999	0.9999
SSE	0.0005779	1.203 × 105	0.001487	0.01478	0.03313	0.01241	0.0406	0.04764	0.06905	0.158
RMSE	0.006207	0.001001	0.009354	0.03371	0.03715	0.02323	0.02717	0.03087	0.03285	0.04364

.

To obtain the comparable scale in current-voltage relation according to the experimental outcomes, lead nitrate fitting parameters were employed and the I-V performance of the fabricated sensor was plotted as depicted in Figure 8.

Finally, ten solutions of zinc nitrate (Zn(NO₃)₂) were tested by the proposed sensor mechanism for 0 ppm, 50 ppm, 100 ppm, 200 ppm, 500 ppm, 1000 ppm, 2000 ppm, 3000 ppm, 4000 ppm, and 5000 ppm concentrations, the outcomes of which are reported in Figure 9.

In the zinc nitrate case, the best results on regressions were also obtained from the Fourier model, therefore the same fitting model was used and fitting parameters corresponding to the Zn(NO₃)₂ are tabulated in

Table 4. Values for fitting parameters of the Fourier model and the corresponding regressions for Zn(NO₃)₂ exposure.

General Model Fourier: f(x) = a0 + a1cos(xw) + b1sin(xw) + a2cos(2xw) + b2sin(2xw) + a3cos(3xw) + b3sin(3xw) + a4cos(4xw) + b4sin(4xw) + a5cos(5xw) + b5sin(5xw) + a6cos(6xw) + b6sin(6xw) + a7cos(7xw) + b7sin(7xw) + a8cos(8xw) + b8sin(8xw)
	Without Zn(NO3)2	50 ppm Zn(NO3)2	100 ppm Zn(NO3)2	200 ppm Zn(NO3)2	500 ppm Zn(NO3)2	1000 ppm Zn(NO3)2	2000 ppm Zn(NO3)2	3000 ppm Zn(NO3)2	4000 ppm Zn(NO3)2	5000 ppm Zn(NO3)2
a0	2577	129.5	2.106	5.493 × 1010	2.852	−1.08 × 108	3.183	−1.369 × 108	2.712 × 109	−2.359 × 1011
a1	−3251	40.08	−1.696	−8.591 ×1010	−1.577	9.435 × 107	−2.801	1.66 × 108	−3.726 × 109	3.699 × 1011
b1	−2916	−234.8	−0.9627	−4.728 × 1010	−1.581	1.999 × 108	−1.246	2.148 × 108	−3.254 × 109	2.015 × 1011
a2	283.3	−181.1	−0.7006	3.718 × 1010	−1.103	1.012 × 108	−0.851	2.335 × 107	5.598 × 108	−1.62 × 1011
b2	2608	−66.26	0.4566	5.871 × 1010	0.07772	−1.798 × 108	0.917	−2.338 × 108	3.68 × 109	−2.509 × 1011
a3	624.7	−67.73	0.03618	−2.378 × 109	−0.3309	−1.469 × 108	0.2801	−1.025 × 108	1.135 × 109	1.243 × 1010
b3	−869.5	116.4	0.4209	−3.862 × 1010	0.3919	3.64 × 107	0.4845	1.153 × 108	−1.956 × 109	1.66 × 1011
a4	−261.9	61.06	0.1922	−7.092 × 109	0.02523	7.287 × 107	0.2012	6.899 × 107	−9.907 × 108	2.94 × 1010
b4	57.75	51.31	0.09011	1.5 × 1010	0.2749	3.957 × 107	−0.06968	−2.066 × 107	4.143 × 108	−6.517 × 1010
a5	27.35	29.6	0.07581	4.294 × 109	0.08147	−1.199 × 107	−0.01192	−2.204 × 107	3.722 × 108	−1.823 × 1010
b5	15.43	−25.25	−0.06483	−3.107 × 109	0.06808	−3.061 × 107	−0.05002	−4.96 × 106	8.636 × 107	1.382 × 1010
a6	0	−7.76	−0.01349	−1.18 × 109	0.05804	−2.676 × 106	0	3.251 × 106	−6.438 × 107	5.083 × 109
b6	0	−12.73	−0.03419	1.466 × 108	−0.001936	8.545 × 106	0	2.794 × 106	−7.057 × 107	−7.723 × 108
a7	0	−3.72	0	1.554 × 108	0	1.239 × 106	0	−1.41 × 105	2.277 × 100	−6.823 × 108
b7	0	1.581	0	6.189 × 107	0	−8.571 × 105	0	−3.456 × 105	1.477 × 100	−2.467 × 108
a8	0	0.1458	0	−7.082 × 106	0	−1.185 × 105	0	0	4.499 × 105	3.216 × 107
b8	0	0.5853	0	−8.59 × 106	0	−5640	0	0	−1.006 × 106	3.62 × 107
w	0.7738	1.796	2.162	0.5236	2.13	0.6221	1.758	0.4357	0.5108	0.521
R2	0.9998	1	0.9999	1	1	1	1	1	1	0.9998
SSE	0.0005779	0.001893	0.001079	0.0003017	0.0007112	0.0002698	0.0002816	3.573 × 10−5	7.98 × 10−5	0.2171
RMSE	0.006207	0.01946	0.01039	0.006565	0.0154	0.005195	0.006851	0.002989	0.003647	0.1042

.

As a final point, the Zn(NO₃)₂ corresponding fitted current-voltage characteristic was plotted and a sensor behavior assessment was carried out as shown in Figure 10.

Like the other nitrate ions under different ion concentrations, the variation in the current-voltage characteristic was also analyzed for the zinc nitrate solution. The solution concentration effect of the graphene nanoparticle-based sensor in the attendance of nitrate family was almost the same by other nitrate ions. In the other words, by increasing the solution concentration for all nitrate ions, the current was increased, as shown in Figure 4, Figure 6, Figure 8 and Figure 10, which can be described by the same ion creation in the sensing region. Additionally, a comparison study between different nitrate families indicated that current variation under sodium nitrate exposure was lower than other nitrates and ammonium nitrate demonstrated a larger variation in I-V characteristics. It can be deduced that the graphene nanoparticle strand illustrated more sensitivity to ammonium nitrate compounds.

3. Simulation Study

Due to the limitations of the simulator, the simulation study could only be undertaken for the carbon nanotube-based sensors. Therefore, in the simulator, a carbon nanotube (CNT) with chirality (4,4) was positioned on top of a dielectric that was controlled by a metal gate under the dielectric. Ideally, nitrate ion impurities placed around the designed channel region and the device function in the attendance of nitrate ions were examined. First, a bare CNT semi-field effect transistor (CNTFET) was designed (Figure 11a) and its I-V characteristic was carried out in the original form (Figure 11b) and fitted form (Figure 11c) is reported in Figure 11.

In all samples, carbon nanotubes (4,4), which are a conductive nanotube, were used and the C–C bond length in all samples was selected as about 1.42086 A° and the repeated number for the nanotube length was set as C = 6. The lattice parameters were performed by the software itself for each given transistor and then fitted automatically to optimize the lattice. The left and right electrodes were considered to be metal, with a thickness of 2 A°. For each structure, two gates were placed under the simulated carbon nanotube. The metal gate was located at the bottom with a height of 1 A° and a voltage of 1 Volt was applied.

Initially, sodium nitrate was placed at the interaction with the carbon nanotube field-effect transistor (CNTFET) channel and the structure was optimized by the software Optimizer to allow all grafting to apply its effect on the channel region, and then the entire structure was relaxed. At room temperature, a voltage of 0–2 V was applied. Subsequently, the current-voltage feature was analyzed and fitted by MATLAB software as shown in Figure 12.

Similar to sodium nitrate, ammonium nitrate (Figure 13a–c), lead nitrate (Figure 13d–f), and zinc nitrate (Figure 13g–i) were proposed to the CNTFET channel at room temperature as shown in Figure 13.

4. Results and Discussion

To analyze the effect of analyte concentration on the fabricated sensor, in a certain voltage, the sensor responses for different pollutions were extracted from corresponding Figure 4, Figure 6, Figure 8 and Figure 10. Subsequently, its current at a specified voltage (1 V) for each pollutant, namely NaNO₃, Pb(NO₃)₂, Zn(NO₃)₂, and NH₄NO₃ ions in concentrations of 50 ppm, 100 ppm, 200 ppm, 500 ppm, 1000 ppm, 2000 ppm, 3000 ppm, 4000 ppm, and 5000 ppm was calculated and plotted as shown in Figure 14. It can be concluded that by increasing the pollution concentration, the corresponding current was increased, which can be explained in the form of injected carrier increment in the sensitive region. It seems that all pollutants from this family follow the same trends on graphene-based metal–semiconductor–metal (MSM) structures.

Finally, to compare the simulation results, all simulated sensors on the FET platform were plotted in one graph as reported in Figure 15.

Since the simulated platform was not similar to the fabricated graphene nanoparticle-based sensor, therefore perfect agreement was not expected. However, for higher applied voltages, the higher current, the same as the fabricated device, was absorbed, which is related to the ammonium nitrate followed by zinc nitrate and lead nitrate; the lowest level current belonged to sodium nitrate. As the last point, the current–voltage model in the FET platform was tested with the sodium nitrate sensor as an example of a nitrate family sensor, and acceptable agreement between the FET regular model (Equation (2)) and the fabricated sensor is reported as shown in Figure 16.

$$\mathrm{I} = {\mathrm{N}}_{\mathrm{c}}{\mathrm{F}}_{-\frac{1}{2}} (\mathsf{\eta }).\mathrm{qV}$$

(2)

where N_c is the effective density of state of graphene-based materials; q = 1.602 × 10⁻¹⁹ is an electron electric charge; ${\mathrm{F}}_{-\frac{1}{2}}$ (η) is the Fermi integral of order (−1/2); and V is the carrier velocity [29]. Additionally, the proposed model was compared with the experimental and simulated results as shown in Figure 16, but a discrepancy in the different conditions was detected.

It can be concluded that the proposed model is accurate for an ideal case, but simulation results due to software boundary can be adopted for carbon-based devices. However, the fabricated device can be assumed as several parallel carbon nanoparticles-based devices, therefore, a discrepancy was observed with a minimum value of sodium nitrate, as shown in Figure 16.

5. Conclusions

Nitrate ions, as the main industrial and agricultural contaminations, are direct indicators on human health, therefore detection and gathering these ions from the environment are critical issues that need to be overcome. Moreover, new materials such as graphene nanoparticles in the application of this process have been encouraged. In this research, high-density graphene nanoparticles fabricated from a polyethylene polymer in strand form as a sensing environment was employed. Therefore, the current–voltage characteristic of metal–nanoparticle–metal (MNM) contact in the presence of nitrate ions such as NaNO₃, Pb(NO₃)₂, Zn(NO₃)₂, and NH₄NO₃ was explored. Additionally, graphene nanoparticle-based sensor experimental results were compared with the simulation and theoretical model. It was concluded that sodium nitrate ions under identical applied voltage produce less current between nitrate families, which can vary from 0 up to 10 mA. The current variation under lead nitrate was about 0–14 mA and zinc nitrate indicated a current variation of about 0–16 mA, which is closer to the lead nitrate effect. However, the current deviation in the graphene strand for ammonium nitrate was 2.5 times greater than sodium nitrate, 1.79 times of lead nitrate, and 1.56 times than that of zinc nitrate. Therefore, a graphene nanoparticle strand structure as a sensing platform in the ionized medium is suggested.