Designing a simple semi-automated system for preconcentration and determination of nickel in some food samples using dispersive liquid–liquid microextraction based upon orange peel oil as extraction solvent

Salman S. Alharthi; Hamed.M. Al-Saidi

doi:10.1016/j.arabjc.2022.104094

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Original article

09 2022

:15;

104094

doi:

10.1016/j.arabjc.2022.104094

Designing a simple semi-automated system for preconcentration and determination of nickel in some food samples using dispersive liquid–liquid microextraction based upon orange peel oil as extraction solvent

Salman S. Alharthi^{^a}, Hamed.M. Al-Saidi^{^b,⁎}

a Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

b Department of Chemistry, University College in Al – Jamoum, Umm Al–Qura University, 21955, Makkah, Saudi Arabia

⁎Corresponding author. hmsaidi@uqu.edu.sa (Hamed.M. Al-Saidi)

Received: 2022-3-25, Accepted: 2022-7-1,

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

A simple semi-automated dispersive liquid–liquid microextraction system was designed and used for off-line preconcentration and determination of nickel (II) ion in some food samples. The methodology submitted in the present work is based upon the microextraction of [Ni(C₃₈H₂₈O₂N)₂] complex employing an orange peel oil (OPO) as an extractant without a dispersing solvent and the estimation of nickel (II) ion in sediment by electrothermal atomic absorption spectrometry (ETAAS). 3 mL of sample solution, 0.5 mL of acetate buffer (pH 6), and 300 µL of the complexing agent (1-[4-[(2-hydroxynaphthalen-1-yl)methylideneamino] phenyl]ethanone) (HNE) dissolved in OPO were automatically transferred to separation tube and the cloudy solution was obtained by dispersing OPO as droplets throughout the aqueous phase using nitrogen gas. Under the optimized conditions, the analytical methodology used in this study provided a good efficiency in term of sensitivity and selectivity where the calibration plot was linear in the range of 5–150 ngL^–1, while, the enhancement factor (EF) and the detection limit were 300 and 0.87 ngL^–1, respectively for a sample volume of 3 mL. On the other hand, the method was completely free of most potential interferences. Certified reference wheat sample (NCS ZC11018) and some food samples were used to evaluate the developed method and results were compared using ICP–MS. Moreover, the new [Ni(C₃₈H₂₈O₂N)₂] complex was synthesized and characterized using diverse characterization techniques.

Keywords

Automation

Nickel

Determination

Characterization

Preconcentration

Food samples

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1 Introduction

Nickel is widely available in nature where it is present in waters, plants and in the form of ores such as garnierite and pentlandite (Rekha et al., 2007; Korn et al., 2008; Soylak and Yilmaz, 2011 ). Nickel and its compounds have become increasingly important because of their wide industrial applications in modern technologies (Han et al., 2018; Taher et al., 2014; Weldeabzgi et al., 2017; CONTAM, 2015 ). Nickel can accumulate in the human body through food chain to achieve to concentration levels above the allowed limit. The increase of nickel levels in human body may lead to serious health problems such as lung fibrosis, skin allergies and respiratory cancer (Tariq and Adnan, 2019; Cempel and Nikel, 2006; Büyükpınar et al., 2017 ). Therefore, it is necessary to monitor ultra–trace levels of nickel in several matrixes especially in environmental, and food samples. Thus, many researchers have developed various analytical methods for preconcentrating and determining nickel in variety of samples. These methods include different extraction and detection techniques (Ghaedi et al., 2007; Temel et al., 2018; Chen and Liu, 2018; Arain et al., 2008; Zhou et al., 2014; Fakhari et al., 2005; Fernández-Turiel et al., 2000; Nelson et al., 2015; Karaaslan and Yaman, 2013 ). However, some of previously developed methods suffer from a number of limitations e.g., high cost, low sensitive, and the use of large amounts of both toxic organic solvents and samples (Han et al., 2018; Şaylan et al., 2020; Satti et al., 2016; Balal Arain et al., 2016; Maryam et al., 2021a, 2021b ). Dispersive liquid–liquid microextraction (DLLME) is an extraction technique based upon the use of very small volumes of organic solvents and samples (Heydari et al., 2014; Heydari, and Azizi, 2015; Rashidipour et al., 2019 ). Thus, DLLME is an eco–friendly extraction technique compared to conventional liquid–liquid extraction (Heydari, and Bazvand, 2019; Heydari, and Mousavi, 2016). However, disperser solvent used in DLLME is a main drawback of this technique because the use of disperser may reduce partition coefficient of analyte between two phases (Erulas et al., 2020). To overcome this drawback, different approaches of dispersion have been developed using different kinds of kinetic energy as an alternative to disperser (Hosseini et al., 2014; Heydari and Zarabi, 2014; Moradi et al., 2021; Shah et al., 2012; Unutkan et al., 2020 ). Various types of kinetic energy e.g., magnetic stirring, ultrasound energy, vortex stirring and pulsed flows have been used to obtain small droplets of extractant. Although ultrasonication is more effective than milder approaches such as air flow and vortex stirring, it can cause sample alteration due to excessive heating. Air assisted spraying has used recently to generate fine droplets of extraction solvent as an alternative to disperser when determination of nickel in tea (Erulas et al., 2020).

Recently, replacement of hazardous solvents with green ones has become common in DLLME. Among green solvents, biobased solvents have low toxicity because they are usually readily biodegradable. The typical examples of biobased solvents include limonene, menthol, glycerol, and 2-methyltetrahydrofuran. However, limonene is the most used solvent in extraction processes (Tobiszewski, 2019; Maryam et al., 2020a, 2020b ). It is the major component of oils extracted from of citrus fruit peels. Although pure limonene has been employed as an extractant in many methods that use DLLME technique (Tobiszewski, 2019; Pourreza and Naghdi, 2017; Aissou et al., 2017 ), the use of natural oils rich in this solvent with DLLME is still limited until now.

The automation of DLLME provides many advantages such as protection of samples from contamination, improvement of repeatability, and minimization of external interventions (Alexovič et al., 2017; Akiba et al., 2022; Maryam et al., 2020a, 2020b, 2021a, 2021b ). Some automation systems, especially in flow mode, suffer from the difficulty of the centrifugation use, hence, less efficient alternatives are used in these systems for phases separation. On the other hand, some systems are costly due to the use of a lot of devices e.g., pumps, tubes, and valves. Although centrifugation represents an effective method to maximize solvent recovery and achieve excellent phases separation, it is a challenge of full automation of DLLME. In fact, the automation of centrifugation is still in a developing stage. Semi-automated systems can be considered as a good alternative to full automation of DLLME. Such systems are low cost and centrifuges can be used with them. Combination of semi-automated DLLME with detection techniques equipped with an auto-sampler improves the analysis efficiency where the possibility of samples contamination is very low.

(1-[4-[(2-hydroxynaphthalen-1-yl) methylideneamino] phenyl] ethanone) (HNE) was synthesized, for the first time, by the reaction of 3-hydroxynaphthalene-2-carboxaldehyde with 1-(4-aminophenyl) ethenone (Al-Saidi and Alharthi, 2021). On the other hand, the complex formed between Co (II) ions and HNE ligand was used for the spectrophotometric determination of cobalt in waters and pharmaceutical preparations using DLLME and microcells with long optical paths (Al-Saidi and Alharthi, 2021). Our preliminary investigations revealed that HNE reagent can react with some transition metal ions, therefore, the analytical applications of this reagent may be expanded. Therefore, the present work is aim to develop a simple, low cost and green methodology based upon organic solvent-free DLLME and ETAAS to extract and determine nickel in some food samples. A semi-automated system will be designed for performing DLLME to improve the efficiency of extraction and avoid contamination or loss of sample. The oil extracted from orange peel will be tested as an extraction solvent to extract the complex formed between Ni (II) ions and HNE ligand. Moreover, this complex will be characterized using different techniques.

2 Experimental

2.1

2.1 Reagents and chemicals

Unless otherwise mentioned, all chemicals used were of a high degree of purity and were used as received from the source. The HNE reagent was synthesized according to the procedure previously mentioned in our work (Al-Saidi and Alharthi, 2021). Nickel (II) chloride hexahydrate (NiCl₂·6H₂O) obtained from MilliporeSigma (Saint Louis, USA) was used for the synthesis of [Ni(C₃₈H₂₈O₂N)₂] complex and the preparation of the standard solutions of Ni(II) ions. Nitrate salts of some metal ions procured from MilliporeSigma (Saint Louis, USA) were used to obtain stock solutions of metal ions. High purity organic solvents were obtained from Thermo Fisher Scientific (Hampton, New Hampshire, USA). Deionized water was used to dilute the standard solutions. HNE reagent solution was prepared by dissolving 0.02 g in 50 mL OPO and few drops of dimethylformamide (DMF) were added to remove the turbidity. Acetate buffer was employed to control the solution pH. Certified reference wheat sample (NCS ZC11018) obtained from Labmix 24 (Hamminkeln, Nordrhein-Westfalen, Germany) was employed to validate the accuracy of the proposed method. According to manufacture, the sample contains 15 elements. However, the substrates of the standard reference material can be obtained from the website of company.

2.2

2.2 Instrumentation

Spectrophotometric measurements in the spectral range of 190 to 1100 nm were recorded using an UV-1280 Shimadzu UV–visible spectrophotometer (Kyoto, Japan). A Perkin Elmer CHN/O analyzer model 2400 (Waltham, Massachusetts, USA) was used for the determination of the elemental compositions of [Ni(C₃₈H₂₈O₂N)₂] complex. JASCO FT-IR spectrometer model 4600 (Tokyo. Japan) was employed for recording ATR-FTIR spectra in the range of 400–4000 cm⁻¹. The samples were introduced to the spectrometer using the unit of attenuated total reflectance (ATR PRO ONE). Bruker X-ray diffractometer model D8 ADVANCE (Billerica, Massachusetts USA) was used for the collection of XRD data of [Ni(C₃₈H₂₈O₂N)₂] complex. X-ray diffractometer was equipped with Cu Kα (λ = 1.54056 Å) operated at 40 kV and 40 mA. The scanning angle was in the range of 3°-40° with the scanning speed of 0.2°/min. Elemental distributions and morphological images of the Ni (II) complex with HNE ligand were recorded using scanning electron microscope combined with energy dispersive X-ray spectroscopy model JEOL JEM-6390 (Peabody, Massachusetts, USA). The GC–MS analysis of OPO was carried out using single quadrupole GC–MS system model ISQ 7000 from Thermo Scientific (Waltham, Massachusetts, USA). The GC–MS system is equipped with a column model TG-MS guard (30 m × 0.25 mm I.D × 0.25 µm particle size). Sample injector temperature was 250 °C. The oven temperature was held at 50 °C for 3 min, then, the temperature was increased from 50 to 150 °C at 5 °C/min, held at 150 °C for 3 min. Finally, the temperature was raised from 150 to 300 °C at the rate of 20 °C/min, then, held at 300 °C for 3 min. Ismatec peristaltic pump model EW-78001–68 (Rankinestr. 1, Landsberg, Germany) with Tygon pumping tubes was used to inject the solutions into the separating tube as shown in Fig. 1. The peristaltic pump is completely controlled by built-in software. The determination of nickel in the sediment was carried out by A Shimadzu atomic absorption spectrometer model AA6650 equipped with an auto-sampler and a graphite furnace atomizer, models ASC-6100 and GFA-EX7, respectively (Kyoto, Japan). Deuterium lamp background correction was used for the correction of nonspecific absorbance. The light source was A Shimadzu hollow-cathode lamp model L233-28NQ operated under the manufacturer's conditions (Kyoto, Japan). The temperature program of the atomizer was optimized and listed in Table.1.

The semi-automated microextraction system used for nickel determination by DLLME technique based on OPO as an extraction solvent.

Table 1 The program of furnace temperature for the nickel determination by the proposed method*.

Step	Furnace temperature, (°C)	Time, (s)	Ar gas flow rate, (mL min⁻¹)
Drying	90	30	45
Drying	300	20	25
Ashing	1000	7	3
Ashing	1000	2	0
Atomization	2550	3	0
Tube cleaning	2600	15	1000

* Other spectrometer parameters: wavelength, 232.0 nm; lamp current, 12.00 mA; slit width, 0.2 mm. Signal measurement, peak height.

2.3

2.3 Preparation of [Ni(C₃₈H₂₈O₂N)₂]

Methanolic solutions of HNE (2 mmol, 30 mL) and NiCl₂·6H₂O (1 mmol, 10 mL) were mixed and refluxed at 70 °C for 3 h. The formed complex in its solid state was obtained by evaporation of solvent using rotary evaporator. The recrystallization of complex was performed using ethanol and its purity was confirmed by thin layer chromatography. Elemental analysis of [Ni(C₁₉H₁₄O₂N)₂] (MW − 635.35 g/mol): Calculated (C %, 71.84; H %, 4.44; N %, 4.41; Ni%, 9.24). Found (C %, 72.27; H %, 4.19; N %, 4.26; Ni%, 9.71).

2.4

2.4 Extraction of OPO

OPO was extracted according to the method previously mentioned in (Fakayode and Abobi, 2018). About 470–600 g of the fresh orange peels collected from fruit market in Saudi Arabia was pureed using a cheese grater and placed inside a suitable round-bottomed flask. Amount of distilled water (≈ 200 mL) was added into the flask and the mixture is then distilled using steam distillation at temperature of 96–100 °C for 2 h. The OPO was obtained from the distillate by solvent extraction using hexane. The extraction was repeated four times, and the pure OPO was obtained by solvent evaporation (hexane) at 69 °C. The pure OPO was collected and stored in a refrigerator at 5 °C. The OPO was characterized using ATR-FTIR, electronic spectra and GC–MS analysis.

2.5

2.5 Recommended procedure for nickel determination

The [Ni(C₃₈H₂₈O₂N)₂] complex was extracted from aqueous phase using the semi-automated microextraction system shown in Fig. 1. Line 1 and line 2 were operated simultaneously to transfer the samples solutions and buffer solution, respectively, to the separation tube. 3 mL of samples solutions or nickel standard solutions (5–150 ngL^-1) was passed into the tube at flow rate of 4 mL min⁻¹ by line 1, and in the same time, line 2 was operated at 800 µL min⁻¹ flow rate to transfer 0.5 mL of acetate buffer (pH 6). 300 µL of HNE complexing agent (1.00 × 10^-7 mol L^-1) dissolved in OPO was introduced to the previous mixture through line 3. Then, nitrogen gas was passed through the separating tube for 20 s to disperse the OPO and form the cloudy solution. The obtained cloudy solution was completely transferred to centrifuge by the small tube attached to the bottom of the separating tube and mixture was centrifuged for one min at 5000 rpm to obtain a supernatant containing analyte. 20 µL of supernatant was injected to ETAAS instrument for the determination of nickel according to operation conditions mentioned above. After each run, the separation tube was washed by acetate buffer (pH 6).

2.6

2.6 The validation of developed method using certified reference material

0.5 g of certified reference wheat sample was mixed with HNO₃ (5 mL, 65%) and H₂O₂ (1 mL, 3%) in teflon beaker. The wheat sample was digested using microwave according to the digestion programming presented in Table.2. Then, the contents of beaker were cooled, diluted with deionized water, and filtered into a volumetric flask (50 mL). The volume of flask was completed by deionized water and a suitable volume of this solution was subjected to recommended procedure to analyze the nickel.

Table 2 The microwave programme used for the digestion of wheat sample*.

Step No	Power, (W)	Time, (min)
1	250	3
2	0	3
3	250	5
4	450	4
5	550	4

* The temperature applied in steps 1 and 2 were 400 and 500 °C respectively.

2.7

2.7 Analysis of nickel in muscle tissue of fish

Hamor fish (Epinephelus tauvina) was obtained from Saudi market (Jeddah city, Kingdom of Saudi Arabia). Dorsal muscle of fish was used in this study because such tissues are the main store of metals. 5 g of dry fish tissues were transferred into a 50 mL beaker containing 5 mL of H₂SO₄ and 5 mL of HNO₃. After the end of the reaction of fish tissues with the previous mixture, the beaker contents were heated at 60 ⁰C for 30 min. After cooling, 10 mL of HNO₃ was added and the mixture was heated at 150 °C until the solution turns black. Then, the mixture was allowed to cool before adding H₂O₂. The solution was mixed with H₂O₂ until it became clear. The content of the beaker was neutralized by 5 mol L^-1 NaOH and transferred then into a volumetric flask. The obtained solution was diluted by deionized water and a part of this solution was subjected to the recommended procedure for nickel determination.

2.8

2.8 Analysis of nickel in black tea

The black tea sample was obtained from Saudi market (Jeddah city, Kingdom of Saudi Arabia). 5 g of tea sample was weighed and soaked in 25 mL of boiling deionized water for 5 min. The tea sample was then completely digested by a mixture of 65% HNO₃ and 70% HClO₄ with heating at 125 W for 15 min. The decomposed tea sample was filtered and the solution was diluted using deionized water in a volumetric flask (100 mL). A part of diluted solution was used to analyze the nickel by the recommended procedure.

3 Results and discussion

3.1

3.1 Spectroscopic studies of [Ni(C₃₈H₂₈O₂N)₂] complex

The electronic absorption spectra of HNE and its complex with Ni(II) ions were recorded in the 190–630 nm spectral range against ethanol as a blank (Fig. 2). The UV–Visible spectrum of HNE (Fig. 2, Black) shows peaks at 212, 230, and 324 nm that are believed to belong to π-π* transitions in aromatic benzene, naphthalene rings and azomethine group, respectively (Lever, 1968). The peak corresponding to n-π* transition appears at 464 nm. The UV–Visible absorption spectrum of Ni(II)-HNE complex reveals that the peaks at 212, and 230 nm observed in free ligand spectrum have been moved to 220, and 272 nm, respectively after complex formation as shown in Fig. 2, blue. On the other hand, the peak at 324 nm corresponding to azomethine group was observed at same wavelength after formation of Ni(II)-HNE complex. However, there is a large increase in absorbance at this peak as shown in Fig. 2, blue. The absorbance intensity at 464 nm significantly decreased after formation of Ni(II)-HNE complex. The absorbance changes at the peaks of 324 and 464 nm indicates the participation of nitrogen and oxygen atoms in the formation of Ni(II)-HNE (Korn et al., 2008). The geometry of Ni(II)-HNE complex was studied using solid state electronic spectrum recorded using nujol mulls method. Figure S1 shows three well-defined bands at 11500, 16528, and 22700 cm⁻¹. The bands at 11,500 and 16528 cm⁻¹ corresponding to ³T₁(F) → ³A₂ and ³T₁(F) → ³T₁(P), respectively are characteristic to the environment of tetrahedral around Ni(II) ion (Lever, 1984; Nair et al., 2012). The value of the magnetic moment is 3.86 BM. This value supports tetrahedral geometry of Ni(II)-HNE complex (Cotton and Wilkinson, 1998).

Absorption spectra of (Black) HNE ligand, and (Blue) Ni (II)–HNE complex recorded in methanolic solutions: [HNE] = 4 × 10-2 molL-1 and [Co2+] = 1 × 10-4 molL-1.

ATR–FTIR spectra of HNE ligand and Ni(II)-HNE complex are shown in Fig. 3, Red &Black, respectively. In the ATR–FTIR spectrum of Ni(II)-HNE complex, the band at 1622 cm⁻¹ characteristic of C = N was shifted to 1590 cm⁻¹ referring to participation of the amido nitrogen in the coordination with Ni(II) ion. The coordination of nitrogen of azomethine group with nickel (II) ion was also confirmed by the appearance of band at 480 cm⁻¹ for ν(M−N) in IR spectrum of complex. Disappearance of weak OH band at 3330 cm⁻¹ and the appearance of band at 587 cm⁻¹ for ν(M−O) in the spectrum of Ni(II) complex with HNE confirm coordination of Ni (II) ion with the oxygen atom in Schiff base.

ATR–FTIR spectra of HNE (Red) and Ni(II)–HNE (Black).

3.2

3.2 SEM and EDX analysis

The surface morphology of Ni(II)-HNE complex was tested by scanning electron microscope at different magnifications. The SEM images shown in Figure S2 reveal that Ni(II)-HNE complex is microcrystalline in nature. Such morphologies were observed in nickel complexes with Schiff bases synthesized from indole-3-carboxaldehyde (Joseyphus and Nair, 2010). Careful examination of SEM images shows agglomerated particles with a rough surface. The sizes of agglomerated particles are in the range of 18.27–35.87 µm. However, particles with sizes less than 100 nm were also detected that clump together to form larger agglomerates. The EDX analysis of Ni(II)-HNE complex showed signals at 0.8, 1.00, 7.50 and 8.4 keV corresponding to nickel element confirming the presence of this element in complex (Fig. 4, A). The EDX spectrum of HNE ligand is shown in Fig. 4, B for comparison.

EDX spectra of (A) the [Ni(C38H28O2N)2] complex and (B) the HNE ligand.

3.3

3.3 X-Ray diffraction studies

Although single crystal of Ni(II)-HNE complex were not obtained, powder X-ray diffraction technique provided good information about crystal system, cell parameters, and the cell volume of prepared complex. The XRD patterns indicate that Ni(II)-HNE complex is crystalline in phase as shown in Fig. 5. Lattice parameters shown in Table.3 were extracted using Rietveld method (Rietveld, 1969). The values shown in Table.3 reveal that Ni(II)-HNE complex crystallizes in monoclinic system where a ≠ b ≠ c and α = γ = 90, β ≠ 90°. This crystal system is similar to that of Co(II)-HNE complex previously studied by our research team (Al-Saidi and Alharthi, 2021). Monoclinic system was also observed in nickel complexes with Schiff bases synthesized from indole-3-carboxaldehyde and m-aminobenzoic acid (Nair et al., 2012). These bases serve as bidentate ligands through an oxygen and nitrogen atoms similar to the current ligand (HNE). Moreover, the average crystallite size (S) of Ni(II)-HNE complex was calculated using Scherer’s formula (Al-Saidi and Alharthi, 2021). The S value of Ni(II)-HNE complex was 83 nm.

The XRD diffraction patterns of Ni(II)-HNE complex.

Table 3 Crystallographic data obtained from XRD technique of Ni(II)-HNE complex.

	[Ni(L)₂]
Formula	[Ni(C₃₈H₂₈O₂N)₂]
Crystal system	Monoclinic
Space group	P21/n
a (Å)	6.761(8)
b (Å)	13.93(3)
c (Å)	15.081(15)
α (°)	90.00
γ (°)	90.00
β (°)	98.07(13)
Cell volume (Å³)	1688(5)

The molar conductance (Λ_M) of Ni(II)-HNE complex recorded at room temperature in DMSO for concentration of 1 × 10^-3 mol L^-1 was in the range of 7.19–29.36 Ω⁻¹ cm² mol⁻¹ indicating that the prepared complex is non-electrolytic nature (Ali et al., 2013). Thus, we suggested the chemical formula of [Ni(C₃₈H₂₈O₂N)₂] based upon the previous results. Figure S3 shows the proposed chemical structure of this complex.

3.4

3.4 Characterization of orange peel oil (OPO)

Knowing the composition of OPO is important before its use as a solvent. Therefore, GC–MS analysis, and spectral measurements of OPO were carried out.

3.4.1

3.4.1 GC–MS analysis of OPO

GC technique is suitable and useful to characterize the oil extracted from orange peel since the volatile components represent around 85–99 % of total content of this oil (Acar et al., 2015). The GC–MS analysis of OPO revealed that monoterpene hydrocarbons are main compounds detected in this oil. GC chromatogram of OPO shown in Fig. 6 revealed that limonene is predominant ingredient with a percentage of 98.55%. The composition of OPO obtained by GC–MS is shown in Table. 4. The findings of GC–MS were in a good agreement with some previously published studies about the extraction of OPO using different techniques (Yang et al., 2009; Toan et al., 2020; Farhat et al., 2011 ).

GC–MS chromatogram of OPO. The temperature programming was mentioned in the text.

Table 4 The main components in OPO detected by GC–MS.

Peak No	Retention time	Compound	Molecular formula	Concentration, (%)
1	5.5	p-mentha-1(7),8-diene	C₁₀H₁₆	0.033
2	7.1	β-Myrcene	C₁₀H₁₆	1.28
3	9.4	Sabinene	C₁₀H₁₆	0.31
4	11.5	Limonene	C₁₀H₁₆	98.55
5	13.2	β-Pinene	C₁₀H₁₆	0.38

3.4.2

3.4.2 Spectral measurements of OPO

ATR-FTIR and UV–Visible spectra recorded for OPO are shown in Fig. 7 and Figure S4. ATR-FTIR spectrum recorded in 4000–400 cm⁻¹ spectral range confirms that monoterpene hydrocarbons are the main components in OPO where the bands corresponding to OH, and C = O groups are not observed in this spectrum as shown in Fig. 7. This is in good agreement with GC–MS analysis. On the other hand, ATR-FTIR spectrum of OPO is somewhat similar to that of pure limonene confirming that this compound is the predominant component in the OPO. The bands at 2926, 1650, and 1446 cm⁻¹ are corresponded to the stretching vibrations of C-H in alkanes, stretching frequencies of C = C in terpenes, and bending vibrations of C-H group in alkanes, respectively (Cebi et al., 2021). Bending vibration of C = C group in terpenes was clearly observed at 889 cm⁻¹. On the other hand, the electronic spectrum of OPO shows well defined peak at 290 nm as shown in Figure S4 confirming the high purity of the oil obtained from orange peels.

3.5

3.5 Development of a simple semi-automated microextraction system for nickel estimation

The previous reaction between Ni (II) ions and HNE reagent was used to extract the nickel (II) ions from aqueous media using DLLME in the presence of OPO as an extraction solvent. A simple semi-automated system has been designed to perform DLLME. The analysis methodology proposed in this study consists of (i) the designed semi-automated system in which solutions injection and dispersion process are carried out for protecting the samples from contamination or loss during these stages, (ii) centrifugation, (iii) the determination by ETAAS.

3.5.1

3.5.1 The optimization of graphite furnace temperature

The application of temperature program suggested by the manufacture was not suitable in out methodology since the background signal was high. Therefore, the graphite furnace temperature was optimized and the results are shown in Tabe.1. Under this temperature program, the background signal was minimum and the shape of peaks was normal. Taking into account the boiling point of OPO (176 °C), the use of the drying temperature of 300 °C for 20 s is necessary to evaporate the oil completely. The temperatures of 1000 and 2550 °C were used for ashing and atomization, respectively. Thus, the proper results were achieved and modifier was not required.

3.5.2

3.5.2 The optimization of semi-automated system

All the variables, whether chemical or instrumental, that affect the efficiency of semi-automated microextraction system were optimized.

3.5.2.1

3.5.2.1 Chemical parameters

Since the proposed microextraction methodology includes the prior formation of hydrophobic complex of Ni²⁺ ions with HNE to be extracted by DLLME, the influence of pH on complex formation was tested in a wide range of pH values (2.0–12.0). An aqueous mixture of HCl and NaOH was used to adjust the pH in the above-mentioned range. Fig. 8 demonstrates that the optimized absorbance was observed at pH 6. The protonation constants of the HNE molecule were previously studied and pK_a of the N atom was 4.98 (Al-Saidi and Alharthi, 2021). Therefore, the pH must be higher than five since the formation of [Ni(C₃₈H₂₈O₂N)₂] complex requires the deprotonated form of the N atom. Moreover, the type of buffer was tested. A mixture of HCl and NaOH and acetate buffer were studied at pH 6. However, acetate buffer provided the minimum value of relative standard deviation (RSD). Therefore, the subsequent experiments were adjusted using acetate buffer at pH 6.

Influence of aqueous phase pH on the extraction of Ni (II) in the form of the [Ni(C38H28O2N)2] complex using the developed semi-automated system and DLLME. DLLME conditions: sample volume, 3 mL; Ni (II) ions concentration, 150 ngL-1; [HNE] = 1.0 × 10-7 mol L-1; Dispersive, N2 gas.

The extraction solvent used in DLLME should have excellent extraction efficiency of analyte and low solubility in aqueous media. In contrast, solvent viscosity plays an important role in its movement through flow systems since the higher the viscosity of the solvent, the lower its flow rate. Therefore, the extractor used in the automated and semi-automated systems must have a low viscosity. Hence, the viscosity of OPO was measured to be 3.02 cSt at 25 °C. It is found that this value is suitable for using the OPO in the designed semi-automated system. The density of the oil measured at 25 °C was less than that of water (0.78 g cm⁻³). Whereas, the solubility of OPO in deionized water at 25 °C was 12.7 mg L^-1. Such properties of OPO makes the phases separation in DLLME occur easily. The influence of OPO volume on the effectiveness of DLLME was studied depending on the values of extraction recovery (ER) calculated using Eq. (1):

(1)

E R = \frac{V_{o} {\times C}_{o}}{{V_{a} \times C}_{a}}

where V_o and V_a represent the volumes of the organic phase and aqueous solution of the sample, respectively. C_a and C_o are the concentrations of [Ni(C₃₈H₂₈O₂N)₂] complex in the aqueous sample solution and the organic phase, respectively. Different volumes of OPO in the range of 100–600 µL were tested. The maximum value of ER was achieved by using 300 μL of OPO. Therefore, 300 μL of OPO was used in the recommended procedure.

The impact of HNE concentration on the extraction efficiency of nickel (II) ions in the form of the [Ni(C₃₈H₂₈O₂N)₂] complex was studied at a constant concentration of Ni (II) ions (150 ngL^-1), while HNE concentrations ranged from 0.2 × 10^-7 to 1.8 × 10^-7 mol L^-1. Fig. 9 reveals that the analyte signal reached its maximum when the HNE concentration was 1.0 × 10^–7 mol L^-1 and leveled out at concentrations higher than 1.0 × 10^-7 mol L^-1. Therefore, the concentration of 1.0 × 10^-7 mol L^-1 was used in in further experiments.

The effect of HNE concentration on the efficiency of DLLME of the [Ni(C38H28O2N)2] complex using OPO as extraction solvent. DLLME conditions: sample volume, 3 mL; Ni (II) ions concentration, 150 ngL-1; pH, 6; OPO volume, 300 μL; The dispersive, N2 gas.

The influence of NaCl at concentration levels of 0, 2, 4, and 6% w/v on ER was checked under the optimized experimental conditions. The results showed that ER values were not significantly affected by the presence of salt. Therefore, no salt was added in the recommended procedure.

3.5.2.2

3.5.2.2 Instrumental parameters

The sample and buffer solutions were passed simultaneously to ensure complete mixing between the two solutions before introducing the extraction solvent (OPO) into the binary system in the separation tube. The sample size introduced into the separation tube was approximately six times larger than the volume of the buffer solution. Therefore, the flow rate of sample solution must be higher than that of buffer solution for transferring the solutions to the separation tube at the same time approximately. Thus, the flow rate of 4.0 mL min⁻¹ was chosen to transfer 3 mL of sample solution or standard Ni (II) solutions to the separation tube. While, the flow rate of 800 µL min⁻¹ was selected to transfer 0.5 mL of buffer. The flow rate of extraction solvent (OPO) containing the complexing agent (HNE) significantly affects the extraction efficiency. Thus, the flow rate of HNE dissolved in OPO was varied from 1.0 to 5.0 mL min⁻¹. The results revealed that the flow rate of 2.0 mL min⁻¹ provided good extraction efficiency with minimum value of RSD. Although flow rates higher than 2.0 mL min⁻¹ increased the efficiency of OPO dispersion through the aqueous phase, the reproducibility of method reduced significantly. Such behavior was observed previously when estimation of antipyrine in saliva using a fully automated DLLME (Medinskaia et al., 2016). Since the flow rate of 2.0 mL min⁻¹ was not enough to obtain effective dispersion of OPO, nitrogen gas was employed to disperse the OPO and form cloudy solution. Therefore, gas passing time was tested in the range 5–60 s. The time of 20 s was enough to get the cloudy solution. Thus, this time was applied in the recommended procedure.

3.5.3

3.5.3 The optimization of centrifugation

Centrifugation is the most effective method to maximize solvent recovery and achieve phase separation especially for stable emulsions. Centrifugation bears the risk of pollution during the collection of organic phases containing analyte. However, the use of spectrometer equipped with an auto-sampler for injecting the upper organic phase containing Ni complex to a graphite furnace atomizer has reduced the risk of contamination significantly. The speed and time of centrifugation were varied in the ranges of 1500–5500 rpm and 1–5 min, respectively. An effective phases separation was achieved using centrifugation at 5000 rpm for one min.

3.5.4

3.5.4 The selectivity

A number of ions of alkaline and alkaline earth elements in addition to some ions of transitional elements was tested when applying the proposed methodology for the determination of 150 ngL^-1 Ni²⁺. The tolerance limit is the largest concentration of foreign ion that give a relative error greater than ± 5%. The results shown in Table.5 reveal that most studied ions did not interfere at the tolerable level of 2000. Schiff bases form colored complexes with several metal ions. Such complexes are transferable to the organic layer. Thus, these metal ions may significantly interfere when determination of Ni (II) ion spectrophotometrically. However, the use of ETAAS as a detection technique in the proposed methodology has greatly overcome this type of interferences where the wavelength of 232.0 nm used for nickel determination by ETAAS is completely different from wavelengths of transition metal ions that can react with HNE reagent. Some anions can form stable compounds with nickel that do not dissociate easily in the atomization. Therefore, the interferences of many anions were tested. The results showed that the anions under study did not interfere at the tolerable level of 1500. Actually, the source of selectivity in our methodology is the use of ETAAS as a detection technique and the few numbers of metal ions that can react with the HNE reagent.

Table 5 The selectivity of semi-automated DLLME system coupled with ETAAS at the determination of 150.0 ngL^-1 of Ni (II).

Ions	Tolerance limits
Na⁺, Li⁺, K⁺, Sr⁺, Ca²⁺, Mg²⁺, Ag⁺, Hg²⁺, Mn²⁺, Co²⁺, Cu²⁺, Pb²⁺, Cr³⁺, Zn²⁺, Fe³⁺, Fe²⁺, Cd²⁺	2000: 1
NO₃⁻, Cl⁻, Br⁻, NO₂⁻, SO₄^{2 -}, CO₃^{2 -}, I⁻	1500: 1

3.5.5

3.5.5 The efficiency of proposed analytical method

Under the optimized conditions, the deigned DLLME system coupled to ETAAS provided a good efficiency in term of sensitivity and selectivity. The calibration curve of DLLME–ETAAS was linear in the range of 5–150 ngL^-1 with regression equation:

(2)

A = 0.00 {34 C}_{Ni} ({ngL}^{- 1}) + 0.000 8, r = 0.9995 (n = 10)

For a sample volume of 3 mL, the detection limit (LOD) and the enhancement factor (EF) calculated using the equations mentioned in (Al-Saidi and Alharthi, 2021) were 0.87 ngL^-1 and 300, respectively. The relative standard deviation was 3.6% for 10 replicate measurements at concentration level of 150 ngL^-1. Moreover, the simple semi-automated system designed in this work can protect the samples from contamination or loss during the stages of solutions injection and their dispersion.

3.5.6

3.5.6 Evaluation of proposed analytical methodology

The accuracy of analytical methodology proposed in this work was tested by the determination of nickel in the wheat sample (NCS ZC11018). The content of nickel in the sample was expressed in μg g⁻¹ using Eq.3:

(3)

C = \frac{C_{d} \times V}{W}

where, C is the nickel content in sample (μg g⁻¹), C_d the nickel concentration in diluted sample solution determined by the proposed methodology (DLLME-ETAAS) in μgmL⁻¹, V represents the volume of diluted sample solution, and W is the sample weight used in the analysis in gram. The nickel concentration determined in wheat sample by the developed method was 0.24 ± 0.02 μg g⁻¹ in a good agreement with the certified value (0.25 ± 0.03 μg g⁻¹) with relative error of 4%. The difference between determined concentration and certified value was explained by Student's t-test. The statistical test revealed that the difference between two values was due to random errors since the calculated value of t (1.67) is less than the critical t value (1.812) at t_0.95 and degrees of freedom n = 10 (Christian, 2004). Therefore, we can say that the present analytical methodology is systematic errors-free (Medinskaia et al., 2016). The precision of the proposed method was established by determining nickel in fish (Epinephelus tauvina) and black tea. The results of DLLME-ETAAS (A) were compared with the measurements of ICP-MS (B) as shown in Table. 6. The amounts of nickel in fish and black tea were expressed in μgg⁻¹ using Eq.5. Moreover, different concentrations of Ni (II) ions were added into samples solutions and nickel amounts in these solutions were then determined via DLLME-ETAAS and ICP-MS as shown in Table.6. As shown in Table.5, The percent recovery values (%RS) of our method ranged from 94 to 98% indicating that sample matrices did not influence determining nickel by the deigned DLLME system coupled to ETAAS.

Table 6 The determination of nickel in fish using the deigned DLLME system coupled to ETAAS (A) and ICP–MS (B).

Sample	Nickel added, (μg g⁻¹)	Nickel found, (μg g⁻¹)		% RS		F–value ^a
Sample	Nickel added, (μg g⁻¹)	A	B	A	B	F–value ^a
Fish	–	0.25 ± 0.14^b	0.26 ± 0.18	–	–	1.68
	0.25	0.49 ± 0.13	0.51 ± 0.16	96	100
	0.50	0.74 ± 0.15	0.77 ± 0.14	98	102
Black tea	–	0.85 ± 0.72	0.84 ± 0.65	–	–	2.28
	0.5	1.32 ± 0.95	1.33 ± 0.82	94	98
	1.00	1.80 ± 0.82	1.85 ± 0.76	97	101

a F_9,9 = 3.18, and the confidence level is 95 %, ^b Mean value ± standard deviation (n = 10).

3.5.7

3.5.7 Comparison with previous studies

Some analytical features of semi-automated DLLME system coupled to ETAAS proposed in this study were compared with those of twenty-three previously published analytical methods. Different extraction and detection techniques were used in these methods as shown in Table 7. The proposed method provides a good analytical performance in terms of linear range, EF, LOD, and LOQ. Most DLLME methodologies with flame atomic absorption spectrometry (FAAS) as a detection technique require the complete evaporation of extraction solvent prior to injection of extract into the FAAS instrument, therefore, such methods are time-consuming. However, in our method, the extract containing analyte was directly injected into ETAAS instrument. Thus, the proposed methodology in this research is characterized by speed compared to many of DLLME-FAAS methods. On the other hand, the automated system designed in the present work contains one peristaltic pump and other simple tools. Therefore, the system is convenient for routine analyses.

Table 7 Comparison between the method proposed in this work with previously published methods.

Chelating reagent ^a	Detection System ^b	Analyzed sample	LOD (ng L⁻¹)	LOQ (ng L⁻¹)	LR (ng L⁻¹)	Sample volume (mL)	EF	Optimum pH	RSD (%)	R²	Ref.
4-HyPhCHO-4-BrPhHyd	SPE-UV–VIS	Natural water	50	NR	10–100	500	82.0	4.0	0.3	0.996	(Rekha et al., 2007)
5-Br-PADMA	CPE- ETAAS	Well and river water	31	NR	100–5500	25	200	5.0	2.1	0.9942	(Han et al., 2018)
2-(5-Br-2-PA)-5-DAP	SPE-ETAAS	Water	5	NR	17–36000	900	180	7.0	4.3	NR	(Taher et al., 2014)
PMBP	CPE-ETAAS	Natural water	120	NR^C	up to 3 × 10⁵	10	27.0	5.0	4.3	NR	(Sun et al., 2006)
2-HBET	PLM-ETAAS	Seawater	12	NR	3 × 10³ –5 × 10⁵	80	20.8	9.4	4.7	NR	(Aouarram et al., 2007)
TPTS	SPME-HPLC-UV	Drinking water	6	NR	NR	5	NR	8.0	3.0	0.997	(Kaur et al., 2007)
PAN	DLPME- ETAAS	Water and rice	33	NR	NR	10	200	9.2	8.2	0.9970	(Jiang et al., 2008)
PAN	SFODME-ETAAS	Well water	0.30	1.0	5–50	10	497	7.0	4.6	0.9995	(Bidabadi et al., 2009)
Yellow Schiff's base bisazanyl derivative	DLLME-ETAAS	River water	40	NR	1 × 10³ –5 × 10⁴	5.0	138	2.0	2.1	0.9966	(Alizadeh et al., 2013)
DDTC	DLLME-SFO-ETAAS	Spring water	1.2	3.9	10–120	10	277	7.0	3.2	0.9997	(Amirkavei et al., 2013)
APDC	CPE-FAAS	Whole blood, serum	520	NR	NR	10	46.0	6.0	less than5	NR	(Arain et al., 2014)
EVA	SPE-FAAS	Tap water	3800	179,000	NR	5.0	46.0	9.0	4.4	0.9997	(Escudero et al., 2014)
Quinalizarin	CPE-FAAS	Tap and sea water	2800	9300	5 × 10³ –2 × 10⁵	50	92.0	8.0	4.6	0.9621	(Satti et al., 2016)
MDTC	FPSE-HPLC-UV	Ground water	18	59	NR	10	NR	NR	1.7	0.994	(Heena et al., 2017)
Azo-schiff base	MME-UV–VIS	Mineral water	2480	8250	2 × 10⁴ –8 × 10⁴	0.4	78.0	5.0	1.23	0.9996	(Özzeybek et al., 2018)
Formic acid	PVG-BT-UAGLS-AAS	Tap water	9500	32,000	NR	10	NR	NR	5.6	0.9992	(Büyükpınar et al., 2017)
DZ	DLLME- HPLC-DAD	Pond water	120	500	1 × 10³ –5 × 10⁴	0.04	NR	9.0	5.3	0.9935	(Chen and Liu, 2018)
APDC	PV-IS-DLLME-FAAS	Chocolate	100	300	6.7 × 10³-1 × 10⁵	0.12	17.0	2.0	4.3	NR	(Barreto et al., 2019)
S8	DASCE-SQT-FAAS	Seawater	1600	5300	NR	2	137	10.0	9.2	0.999	(Tariq and Adnan, 2019)
[TBP] [PO₄] IL	IL-DLLME-FAAS	Vegetable oil	770	2570	1 × 10³ –2 × 10⁵	2	63.0	NR	3.2	0.99	(Adhami et al., 2020)
2-[(Z)-[2-AmPh)Im]Me]-4-BrPh	DLLME-SQT-FAAS	Chamomile tea and coffee	4900	16,400	2.5 × 10³ –2 × 10⁵	0.5	66.4	8.0	8.1	0.9992	(Şaylan et al., 2020)
(E)-3,5-diBr-2-(((3HyPh)Im)Me)Ph	DLLME-SQT-FAAS	Green tea	3900	11,800	1.5 × 10³ –2 × 10⁵	8.0	81.7	9.0	5.1	0.9994	(Erulas et al., 2020)
TANP	UAE-DLLME- UV–VIS	Water, food and tobacco	300	1000	1 × 10³ –3 × 10⁵	30	45.0	7.0	2.1	NR	(Abo Taleb et al., 2021)
HNE	DLLME- ETAAS	Food samples	0.87	2.9	5–150	3	300	6	1.76	0.998	This study

(5-Br-PADMA): 2-(5-bromo-2-pyridylazo)-5-dimethylaminoaniline; APDC: Ammonium pyrrolidinedithio carbamate; 2-(5-Br-2-PA)-5-DAP2-(5-bormo-2-pyridylazo)-5-diethylaminophenol; S8: (Z)-3-Br-5 ((ptolylimino)methyl)phenol; DDTC: Sodium diethyldithiocarbamate; Yellow Schiff's base bisazanyl derivative:2-(2-(2-phenol-2-yl)Methylene-amino)phenylthio)ethylthio-N-((phenol-2-yl))methylene)benzenamine; 4-HyPhCHO-4-BrPhHyd: 4-hydroxy benzaldehyde-4-bromophenyl hydrazone; APDC: Ammonium pyrrolidine dithiocarbamate; (E)-3,5-diBr-2-(((3-HyPh)Im)Me)Ph: (E)-3,5-dibromo-2- (((3-hydroxyphenyl)imino)methyl)phenol; 2-[(Z)-[2-AmPh)Im]Me]-4-BrPh: 2-[(Z)-[2-aminophenyl)imino]methyl]-4-bromophenol; DZ: Dithizone; TANP: 6-(1,3-thiazolylazo)-2-nitrophenol; [TBP] [PO₄] IL: Tetrabutyl phosphonium phosphate ionic liquid; Azo-schiff base: 2-(2-bromophenyl)imino)methyl)-4-(5,6-dimethylpyridin-2-yl)diazenyl)phenol; HNE: 1-[4-[(2-hydroxynaphthalen-1-yl)methylideneamino] phenyl]ethanone.^b CPE: Cloud Point Extraction; SPE: Solid-phase extraction; UV–VIS: Ultraviolet–Visible spectroscopy; PLM: Permeation liquid membrane; SPME: Solid phase microextraction; HPLC: High performance liquid chromatography; DLPME: Dispersive liquid phase microextraction; SFODME: Solidified floating organic drop microextraction; DLLME: Dispersive liquid–liquid microextraction; SFO: Solidified floating organic; MME: Micelle-mediated extraction; PVG: Photochemical vapor generation; BT: Batch type; UAGLS: Ultrasonication assisted gas liquid separator; AAS: Atomic absorption spectrometry; FPSE: Fabric phase sorptive extraction; DAD: diode array detection; PV: Pressure variation; IS: In-syringe; SQT: Slotted quartz tube; UAE: Ultrasound assisted emulsification. ^C NR: Not reported.

a 2-HBET: 2-Hydroxybenzaldehyde N-ethylthiosemi-carbazone; PMBP: 1-phenyl-3-methyl-4-benzoyl-5-pyrazolone; PAN: 1-(2-Pyridylazo)-2-naphthol;

4 Conclusion

The present work describes an analytical methodology for preconcentration and determination of nickel in some food samples using DLLME-ETAAS. The effectiveness of DLLME was improved by designing a simple injection system to introduce the solutions of sample and reagents automatically into separation tube. The extraction methodology is based upon the conversion of Ni (II) ions into [Ni(C₃₈H₂₈O₂N)₂] complex by the reaction with HNE reagent. Then, the formed complex was extracted by DLLME in the presence of OPO as an extraction solvent and the nickel concentration in OPO was determined by ETAAS. The submitted method is simple, selective, and sensitive with LOD of 0.87 ngL^–1 (ppt). As a result of using ETAAS as a detection technique, OPO containing [Ni(C₃₈H₂₈O₂N)₂] complex was directly injected into ETAAS instrument without the need to evaporate the extraction solvent (OPO). Therefore, our method is simple and time saving. On the other hand, the use of auto-sampler for injecting the extract containing analyte to ETAAS instrument has reduced the risks of contamination or loss of sample significantly. The semi-automated microextraction system designed in the present work is very an appropriate for preconcentration and determination of many metal ions capable of forming complexes with some organic complexing agents. Moreover, the system can be combined with several detection techniques like spectrophotometry, spectrometry and chromatography without modifications. The composition of oil extracted from orange peel was fully characterized using GC–MS analysis, and spectral measurements. The results revealed that limonene is predominant ingredient with a percentage of 98.55 % as well as other four monoterpene hydrocarbons. The new complex of Ni (II) ion with HNE was synthesized and characterized using different characterization techniques such as ATR-FTIR, EDX, SEM, powder X-ray diffraction, and electronic spectra. The chemical structure and formula of this complex were suggested. The X-ray diffraction measurements and the analysis of SEM images revealed that the [Ni(C₃₈H₂₈O₂N)₂] complex is microcrystalline in nature and crystallizes in a monoclinic system.

Acknowledgement

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4281484DSR01).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Appendix A

Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.arabjc.2022.104094.

Appendix A

Supplementary data

The following are the Supplementary data to this article:

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[66] Temel N.K., Sertakan K., Gürkan R., . Preconcentration and Determination of Trace Nickel and Cobalt in Milk-Based Samples by Ultrasound-Assisted Cloud Point Extraction Coupled with Flame Atomic Absorption Spectrometry. Biol Trace Elem Res.. 2018;186(2):597-607.
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Designing a simple semi-automated system for preconcentration and determination of nickel in some food samples using dispersive liquid–liquid microextraction based upon orange peel oil as extraction solvent

Abstract

Keywords

Automation

Nickel

Determination

Characterization

Preconcentration

Food samples

1 Introduction

2 Experimental

2.1 Reagents and chemicals

2.2 Instrumentation

2.3 Preparation of [Ni(C38H28O2N)2]

2.4 Extraction of OPO

2.5 Recommended procedure for nickel determination

2.6 The validation of developed method using certified reference material

2.7 Analysis of nickel in muscle tissue of fish

2.8 Analysis of nickel in black tea

3 Results and discussion

3.1 Spectroscopic studies of [Ni(C38H28O2N)2] complex

3.2 SEM and EDX analysis

3.3 X-Ray diffraction studies

3.4 Characterization of orange peel oil (OPO)

3.4.1 GC–MS analysis of OPO

3.4.2 Spectral measurements of OPO

3.5 Development of a simple semi-automated microextraction system for nickel estimation

3.5.1 The optimization of graphite furnace temperature

3.5.2 The optimization of semi-automated system

3.5.2.1 Chemical parameters

3.5.2.2 Instrumental parameters

3.5.3 The optimization of centrifugation

3.5.4 The selectivity

3.5.5 The efficiency of proposed analytical method

3.5.6 Evaluation of proposed analytical methodology

3.5.7 Comparison with previous studies

4 Conclusion

Acknowledgement

Declaration of Competing Interest

References

Appendix A

Supplementary data

Appendix A

Supplementary data

Supplementary data 1

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2.3 Preparation of [Ni(C₃₈H₂₈O₂N)₂]

3.1 Spectroscopic studies of [Ni(C₃₈H₂₈O₂N)₂] complex