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Farcău, C.; Rebeca, M. Detecting Organochlorine Pesticides by SERS. Encyclopedia. Available online: https://encyclopedia.pub/entry/7483 (accessed on 21 April 2024).
Farcău C, Rebeca M. Detecting Organochlorine Pesticides by SERS. Encyclopedia. Available at: https://encyclopedia.pub/entry/7483. Accessed April 21, 2024.
Farcău, Cosmin, Moldovan Rebeca. "Detecting Organochlorine Pesticides by SERS" Encyclopedia, https://encyclopedia.pub/entry/7483 (accessed April 21, 2024).
Farcău, C., & Rebeca, M. (2021, February 22). Detecting Organochlorine Pesticides by SERS. In Encyclopedia. https://encyclopedia.pub/entry/7483
Farcău, Cosmin and Moldovan Rebeca. "Detecting Organochlorine Pesticides by SERS." Encyclopedia. Web. 22 February, 2021.
Detecting Organochlorine Pesticides by SERS
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Organochlorine pesticides (OCPs) embody highly lipophilic hazardous chemicals that are being phased out globally. Due to their persistent nature, they are still contaminating the environment, being classified as persistent organic pollutants (POPs). They bioaccumulate through bioconcentration and biomagnification, leading to elevated concentrations at higher trophic levels. Studies show that human long-term exposure to OCPs is correlated with a large panel of common chronic diseases. Due to toxicity concerns, most OCPs are listed as persistent organic pollutants (POPs). Conventionally, separation techniques such as gas chromatography are used to analyze OCPs (e.g., gas chromatography coupled with mass spectrometry (GC/MS) or electron capture detection (GC/ECD)).

Organochlorine Pesticides Surface Enhanced Raman Spectroscopy

1. Introduction

Organochlorine pesticides (OCPs) are synthetic organic molecules that have multiple chlorine atoms in their structure. They were widely used from the 1940s [1] to the 1960s in the United States and Europe mainly to control insect pests by affecting their nervous system [2]. Most OCPs are lipophilic and hydrophobic (Figure 1), and as such, they are very resistant to environmental degradation [3], persisting in the environment for years or decades after usage. This leads to the contamination of groundwater, surface water, food products, air, and soil. An extensive literature review [4] of data from North America, Europe, and Asia on emerging pollutants in water sources identified organochlorine pesticides as one of the most common water pollutants. Due to the great health risks posed by OCPs at environmental concentrations, most of them are included in the group of contaminants known as persistent organic pollutants (POPs).

Figure 1. Structures of the listed organochlorine pesticides. DDE—dichloro-diphenyl-dichloro-ethylene; 2,4-D—2,4-dichloro-phenoxyacetic acid.

As the negative impact of pesticide pollution transcends national boundaries, POPs have been regulated by the International Agreement (United Nations Environment Program, UNEP) between 154 signatories of the Stockholm Convention on Persistent Organic Pollutants since 2001. Nine OCPs, namely aldrin, dieldrin, endrin, dichloro-diphenyl-trichloroethane (DDT), chlordane, hexachlorobenzene (HCB), mirex, toxaphene, and heptachlor, were initially listed amongst the 12 initial POPs [5] called the dirty dozen. Since then, chlordecone and hexachlorocyclohexanes (HCH) including lindane (γ-HCH), endosulfan (α and β isomers), dicofol, pentachlorophenol (PCP) and pentachlorobenzene have also been listed [6]. These chemicals are dangerously toxic pollutants, capable of long-range transport, bio-accumulation in human and animal tissues, and bio-magnification in food chains [7].

Many studies keep finding associations between human long-term exposure to OCPs and a large panel of common chronic diseases including cancer (breast, prostate, testicular, kidney, ovarian and uterine cancers), neurodegenerative diseases (Parkinson, Alzheimer, Amyotrophic Lateral Sclerosis), chronic respiratory diseases (asthma and Chronic Obstructive Pulmonary Disorder), diabetes, immune dysfunction, cardiovascular diseases, endocrine disruption, and even harmful reproduction effects [8][9][10]. There are some recent reviews that cover this topic [11][12].

Even if great efforts are made to phase POPs out globally, with obvious progress, they are still found in the environment in regions such as the Arctic [13], thousands of kilometers from any major POP source. Moreover, as novel data are gathered, the list of EPs or environmentally persistent hazardous chemicals is continuously amended. Most recently, in June 2017, the Stockholm Convention established global bans on 16 new POPs [14] including organochlorine pesticides. This translates into financial (expensive equipment) and technical challenges (time-consuming purification processes) regarding the scientific analysis of the samples given that they are conventionally analyzed by chromatographic techniques such as gas chromatography coupled with mass spectrometry (GC/MS) or electron capture detection (GC/ECD) and HPLC-MS [15][16][17]. Even if the detection and quantification of analytes at trace levels is accurate down to ng or pg/L, these techniques can only be used in well-equipped centralized laboratories, once the sample is collected and extensively pre-processed. In this context, accessible new strategies for inexpensive, fast, highly sensitive, and on-site detection of OCPs such as surface enhanced Raman spectroscopy (SERS) are of very high demand. The current review focused on strategies adopted to maximize the SERS signal of OCPs. This can be achieved by increasing the substrate’s performance by increasing the affinity of the analyte for the substrate or by preconcentration. Strategies like concentrating nanoparticles through mechanical traps [18], inducing hot-spots by aggregation or assembly of nanostructures [19] in different-sized oligomers, films, or film patches [20] are all improving the sensitivity of the substrate. Such approaches may even enable the detection of molecules with low affinities toward the metal substrate.

While maintaining their sensitivity, the selectivity of the SERS substrates may be further tuned by functionalizing them with linkers such as diamine [21][22] dithiols [23], bipyridinium dications [24], carbon, and metal-organic-frameworks [25], to name a few. The interaction of these “receptor” molecules with OCPs via covalent, electrostatic, or hydrophobic bonds will be discussed. Moreover, the potential advantages of multiplex analysis and future prospects are also presented.

2. Surface Enhanced Raman Spectroscopy (SERS) Strategies for Organochlorine Pesticide (OCP) Detection

The inelastic scattering of photons by molecules, discovered in 1928 as “a new type of secondary radiation” [26], is known as Raman scattering. This effect is very weak, specifically, there is an inelastic scattering for every ten million elastically scattered photons. As Raman spectroscopy provides the great advantage of a molecular fingerprint (in particular, the molecule’s vibrational structure), it was fortunate that in 1974, researchers developed discovered methods to amplify the weak Raman signal [27]. This is when SERS emerged. The SERS effect denotes a strong increase (several orders of magnitude) in the Raman signal of a molecule, induced with the help of a special substrate, typically represented by metallic nanostructures.

The enhancement of the Raman signal by the metal substrate is explained mainly by an electromagnetic mechanism cumulated or not with a chemical one [28][29]. At a certain resonant frequency, the interaction of electromagnetic radiation with metal nanostructures leads to collective oscillations of the conduction electrons (i.e., the excitation of surface plasmons (plasmon resonance)). One of the main consequences of this resonant plasmon excitation is the strong enhancement of the electromagnetic near fields at the metal surface. It is this resonantly enhanced near field that is the main contributor to the Raman signal enhancement in SERS. Since near fields decay exponentially away from the surface, it has been established that it is critical for the molecule to be in close proximity of the metallic surface (furthermost 10 nm) [28] to increase the Raman signal. The enhancement is maximized for molecules in direct contact with the surface, and it decreases with the increase in distance between the substrate and the analyte. By matching the surface plasmon resonances (localized or propagative) to the excitation laser, which can be done by material, size, and shape adjustments, the SERS efficiency of a substrate can be maximized at the desired wavelength. To extend SERS effects into the deep-UV and benefit at the same time from the resonance Raman effect (i.e., matching molecular electronic transitions), aluminum SERS substrates have been developed [30].

The SERS enhancement based on the electromagnetic mechanism applies to all molecules and leads to enhancement factors (EF) as high as 106–108. The chemical mechanism, on the other hand, is based on charge transfer interactions that take place between molecules and the metal surface. This requires the molecule to be chemically adsorbed on the surface of the metal substrate, making this mechanism analyte-dependent and site-specific. Molecules can be adsorbed on the surface either through physisorption (van der Waals forces) or chemisorption (chemical bonds such as covalent or electrostatic interactions).

Since it provides a broad range of advantages, SERS represents a valuable analytical tool for the detection of pesticides. Among these, the high sensitivity and the generated vibrational fingerprint of molecules are highly valued since OCPs are found in trace amounts in rather complex matrices of the environmental samples and they can have multiple stereoisomeric configurations. Moreover, the analyses are fast (given that there is no need for thorough purification processes), the equipment is not very costly, and it is easy to use. Additionally, there are portable Raman spectrometers for on-site analysis.

As every analytical technique, it has also its drawbacks, certain concerns in the SERS analysis (of pesticides) involving selectivity, sensibility, reproducibility, portability, quantification, and nonspecific binding. These challenges have been the subject of multiple comprehensive reviews [31][32][33], consequently they will not be reconsidered in detail. Bernat et al. [34] presented limitations like selectivity, reproducibility, and nonspecific binding, along with some possible solutions. Reviews regarding different spectroscopic techniques (SERS, SPR, and fluorescence) for detecting POPs [35] or pesticide residues in foods [36][37] can also be found in the literature. Some recent book chapters have addressed more general matters like usage, sensing (exploiting biofunctionalized nanomaterials), and removal of different classes of pesticides (organochlorine, organophosphate, carbamate, pyrethroids and others) [38] or development of optical (including SERS) and electrochemical sensors for pesticide detection [39].[40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]

OCPs are hardly mentioned in these works as their SERS spectrum is more troublesome to be obtained because of their highly hydrophobic structures (Figure 1) and low affinity toward the SERS substrates. Additionally, the important progress made in the last few years regarding their analysis has built the momentum of a more comprehensive discussion and review on the SERS detection strategies of OCPs, as summarized in Table 1.

Table 1. Surface enhanced Raman spectroscopy (SERS) strategies for Organochlorine pesticide (OCP) detection.

Strategy Analyte SERS Substrate/Method LOD EF Metal Laser (nm) Incubation Time Ref.
Increasing the substrate’s performance 2,4-D Au nanorods and Ag nanocubes - - Au, Ag 632.8 1 h [40]
vertically ordered arrays of Ag nanorod
bundles
61.9 nM 1.4 × 108 Ag + Au 633 hours [41]
DDT void@AuNPs@SiO2 microporous capsules - - Au 785 15 min [42]
AgNP@composite agarose gels - - Ag 785 2 h [18]
AuNP array fabricated by laser annealing of gold film - - Au 785 - [43]
AgNPs prepared by self-assembly/
in situ growing method
- 3.5 × 106 Ag 785 <1 min [44]
AgNPs sheet - - Ag 632.8 20 min [45]
Endosulfan AuNPs - - Au 785 - [46]
HCB 3D Ag F-NPs - - Ag 785 30 min [47]
Lindane chestnut-like Au nanocrystals-built film 34.38 nM > 107 Au 785 8h [19]
concave trisoctahedral and calyptriform Au nanocrystals-built films 0.1 µM 107 Au 785 8h [48]
AgNPs sheet 0.3 µM - Ag 632.8 20 min [45]
PCP MoO2nanodumbbells 0.1 µM 3.75 × 106 MoO2 532.8 20 min [49]
Fe3O4xAgNPs@pNIPAM 1 nM - Ag 785 2 h [50]
>100
pesticides
electrochemically roughened silver oxide SERS sensor - - Ag 785 - [51]
Increasing the affinity of the analyte Aldrin AgNPs@ α, ω-aliphatic diamines 13.7 nM 2 × 104 Ag 514.5 - [21]
AuNPs/AgNPs@alkyl dithiols 0.12 µM - Au, Ag 785 10 min [23]
AgNPs clusters by α, ω-aliphatic diamines 10 nM - Ag 785
514.5
- [22]
flower like AgNPs@ diquat/lucigenin - - Ag 532 5 min [52]
Dieldrin AuNPs/AgNPs@alkyl dithiols 0.82 µM - Au, Ag 785 10 min [23]
op’-DDT flower like AgNPs@ diquat/lucigenin - - Ag 532 5 min [52]
pp’-DDE flower like AgNPs@ diquat/lucigenin - - Ag 532 5 min [52]
Endosulfan (α) AuNPs/AgNPs@alkyl dithiols 0.41 µM - Au, Ag 785 10 min [23]
Endosulfan (α, β) AgNP clusters by α, ω-aliphatic diamines 10nM - Ag 785
514.5
- [22]
Endosulfan AgNP@ bis-acridinium lucigenin 49.15 nM - Ag 785 - [24]
Endosulfan (α, β) flower like AgNPs@diquat/lucigenin - - Ag 532 5 min [52]
HCH Au nanosheets built hollow sub-microcubes@4-MPBA 1.03 nM - Au 785 4h [53]
HCH (α, ϒ) urchin-like Au–Ag nanocrystals@ porous zeolite imidazole framework 15.15 nM 3 × 107 Au + Ag 785 10 h [25]
HCH (α, β) flower like AgNPs@diquat/lucigenin - - Ag 532 5 min [52]
HCH (ϒ) AuNPs/AgNPs@alkyl dithiols 3.53 µM - Au, Ag 785 10 min [23]
Heptachlor flower like AgNPs@ diquat/lucigenin - - Ag 532 5 min [52]
PCP AgNPs aggregates@cysteamine SAM 0.20 μM - Ag 785 3 h [54]
AuNPs@cysteamine 1 nM 5.7 × 105 Au 785 - [55]
nanoporous Ag coating@cysteamine 6.4 nM 3.7 × 105 Ag 785 5 h [56]
Fe3O4@carbon@AgNPs core–shell microspheres 1 pM - Ag 633 1 h [57]
Tetradifon flower like AgNPs@ diquat/lucigenin - - Ag 532 5 min [52]
Preconcentration Chlordane citrate coated AuNPs/rolling method and prediction model 1 ppm - Au 780 - [58]
PCP nanoporous Ag coating modified by cysteamine 6.4 nM 3.7 ×105 Ag 785 5 h [56]
LOD—limit of detection; EF—enhancement factor; AuNPs—gold nanoparticles; AgNPs—silver nanoparticles; F-NPs—ferro-nanoparticles; SAM—self-assembled monolayer.

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