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Silver Nanoparticles-Based Substrate for Blood Serum Analysis under 785 nm Laser Excitation

Sahar Z. Al-Sammarraie1*, Lyudmila A. Bratchenko1, Elena N. Typikova1, Peter A. Lebedev2, Valery P. Zakharov1, and Ivan A. Bratchenko1

1 Samara University, 34 Moskovskoe shosse, Samara 443066, Russian Federation

2 Samara State Medical University, 39 Chapaevskaya str., Samara 443099, Russian Federation

* e-mail: saharzead@rocketmail.com

Abstract. Individuals who have different diseases need a routine assessment of their body metabolism, and the methods that used are practically difficult, inconvenient or expensive. The objective of this study was to develop a technique of human blood serum analysis that is simple, reliable and fast, and based on a surface-enhanced Raman spectroscopy (SERS). In this study, serum samples were examined using conventional Raman (CR) and SERS. The observed CR and SERS bands were analyzed. Several of these bands (724, 813, 890, 961, and 1132 cm-1) clearly stand out by the impact of the SERS technique, as the intensities of these bands in CR measurements are weaker than the intensity of the autofluorescence and noise. The Enhancement Factor (EF) was up to 4 x 105. Stability of the proposed SERS technique was confirmed by the measurements of signal standard deviation. The observed standard deviation does not exceed 19% for different SERS substrates and does not exceed 8% in case of a single SERS substrate measurements. The obtained results demonstrate that the proposed SERS technique is stable and has significant potential in clinical diagnosis applications. © 2022 Journal of Biomedical Photonics & Engineering.

Keywords: surface-enhanced Raman spectroscopy; Enhancement Factor; blood serum; silver nanoparticles; Raman band shift.

Paper #3461 received 1 Nov 2021; revised manuscript received 24 Jan 2022; accepted for publication 24 Jan 2022; published online 3 Feb 2022. doi: 10.18287/JBPE22.08.010301.

1 Introduction

The biggest threat to human life and health is a disease, according to death causes statistics provided by the World Health Organization [1]. Numerous diseases are caused by lifestyle behaviors, environmental pollution, occupational hazards, biological and natural factors, chronic intoxication and trauma, iatrogenic factors, and other external factors; and certain diseases are fatal [2, 3]. As a result, the most effective strategies to increase patient survival are early diagnosis and therapy [4]. That is why it is important for individuals who have different diseases to perform a routine assessment of their body functions. Health screening programs are central to the public health system. In order to analyze the functional and pathological processes of the human body, laboratory analyzes of body biofluids are widely used. Many studies on the human body and the investigation of disease processes are done at the level of single cells, proteins, and genes [5-7]. As a result, there is a growing demand for disease diagnostic

and analysis technologies. It is necessary to establish a variety of measuring methods and standards for the early detection of various illnesses. Recently, biomarkers have been increasingly utilized in illness diagnosis to signal definite or possible changes in the structure or function of systems, organs, tissues, and cells. It should be noted that laboratory methods used today in clinical practice have a number of limitations.

Recently, Raman spectroscopy has been used to detect viruses, identify bacteria, analyze cells, and make clinical diagnoses [8-14]. Because of its multiple detection capabilities, single-molecule level sensitivity, and quantitative ability, Raman spectroscopy's unique spectral feature makes it an excellent label-free, reliable, and fast diagnostic tool since it can analyze chemical components such as proteins, nucleic acids [6-10], and carbohydrates in biological materials. Surface-enhanced Raman spectroscopy (SERS) has been extensively investigated for biological applications [2-5]. SERS is

recognized as an ultrasensitive optical detection technique with wide application potential in a variety of fields due to its great sensitivity at the single-molecule level.

However, the limitations of the SERS technique are (1) the method requires intimate contact between the enhancing surface and the analyte; (2) the substrates degrade with time resulting in a decrease in the signal; (3) limited selectivity of the substrates for a given analyte; (4) limited re-usability of the substrates; and (5) problems with homogeneity and reproducibility of the SERS signal within a substrate [15]. In this regard, novel approaches of SERS signal registration from the biological fluids are in a high demand. Optimization of methodology of SERS substrates preparation and SERS spectra acquisition requires new approaches to create fast, low-cost and reliable techniques of biofluids chemical sensing.

Thus, in the current study, a simple approach of SERS substrate implementation for blood serum analysis is considered. Blood serum or blood components is a biofluid that is widely used as a diagnostic sample since it is rich in biochemical and biological information, is easily accessible, and can be obtained noninvasively. For the same reasons, it is often saved in biobanks for research purposes. In general, it is a metabolite pool generated from almost all of the human body's organs and tissues. As a result, Raman spectroscopy of human blood samples can be used to monitor metabolic changes caused by illnesses [16-20]. Serum has regularly been investigated using the SERS approach. For instance, Bonifacio et al. [21] employed aqueous Au and Ag colloids in combination with three lasers ranging from visible to near-infrared to perform a systematic study of SERS spectra of blood serum and plasma. However, the SERS spectra analysis indicated that strong and reproducible spectra can be generated rapidly only if proteins are eliminated from samples and a near-infrared excitation is employed in combination with Ag colloids. While Cao et al. [22] investigated the spectral properties of human blood for the purpose of detecting lung adenocarcinoma, lung squamous carcinoma, and large cell lung cancer using SERS spectroscopy on a highly branched gold nanoparticle substrate. They showed that SERS spectroscopy was a sensitive analytical tool for cancer classification and discriminating. Scientific group of Pérez et al. [23] was the first to employ SERS spectroscopy to identify Chagas disease in blood serum using concentrated silver nanoparticles and chemometric techniques. They achieved great accuracy when classifying samples into three categories (healthy patients and asymptomatic patients). However, even when the appropriate experimental parameters are established, significant care must be given while planning research for the development of diagnostic tests. Blood serum, like other biological objects, contains a number of fluorophores and is characterized by the presence of fluorescence [24, 25]. It should be noted that fluorescence is background and noise for the Raman signal. In order to reduce the contribution of fluorescence

to the signal in the study of biological objects, the excitation of spectra in the near infrared range is widely used. Therefore, excitation of the spectral characteristics is realized at a wavelength of 785 nm. It is advisable to use particles of silver, copper or gold to achieve surface enhancement of the Raman signal in the near infrared region [26-28]. Colloidal silver solution is characterized by simplicity of preparation.

In the current work to implement a simple analysis of human serum using SERS, a silver SERS substrate was prepared. The goal of this work was to develop a SERS technique based on sliver nanoparticles (Ag NPs) application for simple, reliable and rapid analysis of human serum. To assess the prospects of the proposed SERS technique, the stability of SERS substrates and a comparative analysis of the conventional Raman (CR) spectra and SERS characteristics of serum samples with the calculation of the enhancement factor (EF) were carried out.

2 Materials and methods

2.1 Colloidal silver nanoparticles solution

Silver nitrate and trisodium citrate were used as starting materials for the preparation of (AgNPs). The silver colloid was prepared by using chemical reduction method. All solutions of reacting materials were prepared in distilled water. In typical experiment 20 ml of distilled water heated to boil. To this solution 3 ml of 1.8% AgNO3 of and 6 ml of 1% trisodium citrate (Na3C6№O7) were added. The resulting solution was heated at 95 °C for 20 min until a yellow-green solution is formed. Then the solution was removed from the heating device and stirred until cooled to room temperature. The UV-Vis spectrum of the AgNPs solution was obtained by utilizing the Spectrophotometer (UNICO 1201, United Products & Instruments, USA). The obtained absorption spectrum of AgNPs solution is shown in Fig. 1 and demonstrates an absorption maximum at 410 nm with a full width at halfmaximum of 40 nm, which corresponds to spherical nanoparticles with 20 nm in diameter.





O 1

-Q <

0.5 0

300 400 500 600 Wavelength, nm

Fig. 1 UV-Vis absorbance spectrum of the colloidal AgNPs.

View field: 30.96 Mm Det: SE 5 nm m' View field: 10.83 pm Det: SE 2 |jm

(a) (b)

Fig. 2 The electron-microscopic images of the substrate with silver particles at magnification factors of 7 000 x and 20 000 x.

The resulting colloidal solution with a volume of 20 ml was poured onto an aluminum foil with an area of 75 mm x 25 mm and dried at room temperature until completely dry. Fig. 2 presents the electron-microscopic images of the prepared substrate after drying. The electron-microscopic images of the substrate with silver structures were obtained on a Vega SB scanning electron microscope (Tescan, Czech Republic) with an INCA X-act energy dispersive X-ray analyzer (Oxford Instruments, the United Kingdom) at an accelerating voltage of 30 kV. The images contained agglomerates of spherical particles about 200 nm in size.

2.2 Serum samples preparation

A standardized sampling was carried out from patients of the Samara Regional Clinical Hospital named after V. D. Seredavin. The study included patients with stages 1-3a of chronic kidney disease. The study protocols were approved by the ethical committee of Samara State Medical University. All the subjects who participated in this study gave their written informed consent at the beginning of the study. The blood serum samples were collected from patients in fasting condition and placed in sealed containers, followed by freezing at a temperature of -16 °C. Immediately before the start of the analysis, the blood serum samples were defrosted at room temperature.

Each blood serum samples were dropped in a volume of 1.5 ^l and dried for 30 min:

• on aluminum foil for CR analysis;

• on aluminum foil with the layer of dried silver colloid for SERS analysis.

2.3 Experimental setup and spectra collection

The experimental setup for Raman analysis of human serum includes a spectrometric system (EnSpectr R785,

Spektr-M, Chernogolovka, Moscow Region, Russia) and a microscope (ADF U300, ADF, China). Focusing the exciting radiation and collecting the scattered radiation were implemented using 50x Objective LMPlan. The stimulation of collected spectra was performed by the laser module with central wavelength 785 nm. The diameter of the laser spot at the focus on the sample surface was 5 ^m. The laser power was 10 mW for the SERS technique and 60 mW for the CR technique. Exposure time was 4 s for SERS and 20 s for CR. The resulting raw spectrum for each sample is an automatic sequential recording of four spectra with subsequent averaging. Thus, the recording time of the CR raw spectrum is 80 s, and 16 s for SERS.

The analyzed serum sample was examined 15 times in the same position and condition and then examined for 5 different SERS substrates at 5 different spots to indicate the accuracy and stability of the proposed SERS substrates in comparison to CR.

2.4 Spectra processing and calculation of enhancement factor (EF)

The analysis of the stability of the SERS substrate and the reproducibility of the result was carried out on raw spectra without preprocessing. The raw spectral data were subjected to calculating the mean spectrum and standard deviation. Calculation of standard deviation was performed in a standard manner [15]. The mean CR spectra and SERS spectra and shift peaks were assigned to the molecular structures and biochemical components based on the analysis of available studies.

The estimation of the EF is realized in a comparative analysis of the preprocessed CR and SERS spectra. Raw spectra were processed by carrying baseline correction and smoothing to remove noise and fluorescence background. Prior to analysis, the raw spectral data were smoothed by the Savitzky-Golay filter. Savitzky-Golay

filter was applied with filter window width of 15, 1 order of polynomial used for smoothing and 0 order of derivative to take (no derivative). Smoothing aims to remove noise and outliers from a given set of experimental data. Proper smoothing will act to increase the signal-to-noise ratio and enhance the primary features embedded in a given data-set. Numerical means whereby Raman spectral data may be smoothed have been widely employed in the field for many years [18, 29]. Baseline correction is based on polynomial approximation method (tenth-order polynomial function) to separate the autofluorescence and the Raman signal [28]. After spectral processing EF is estimated by the following equation:

_ ¡SERSX#VRaman X%Raman X'#aman (1)


where Isers and /Raman are the intensities of the same band for the SERS and CR spectra, respectively; EVsers and EVRaman are the exposure times for the SERS and CR spectra, respectively; Csers and CRaman are the sample concentration for the SERS and CR spectra, respectively; Psers and PRaman are the exciting laser power for the SERS and CR spectra, respectively. Since the CR and SERS spectra of the same sample were recorded to calculate the

EF, the concentration ratio CRaman / Csers is equal to 1

(CRaman = CsERs).

3 Results and discussion

At the first stage, the characteristic spectral features of a human serum sample using the SERS technology, using the CR technology, and the spectral characteristics of the prepared silver SERS substrate were considered. Fig. 3 shows the recorded raw SERS and CR spectra of serum. Fig. 4 demonstrates the preprocessed SERS and CR spectra of human serum corresponding to the Raman contribution. As can be seen from Fig. 3, the spectral contribution of the silver substrate to the SERS spectrum of the serum sample is insignificant. Consequently, when analyzing the SERS spectrum of serum obtained using the proposed approach, the spectral features characterize the tested serum sample and there is no need to take into account in the analysis the foreign contribution of the substrate. Only a few Raman peaks could be observed in the regular Raman spectra of the serum sample. The SERS spectra of the serum sample, obtained with the proposed technique with AgNPs shows multiple distinctive peaks, and the intensity of these peaks is enhanced compared with that of CR spectra.

Wavenumber, cm-1

Fig. 3 Comparison of the raw spectrum of a blood serum sample for CR, SERS, and the spectral characteristics of the silver substrate.

Fig. 4 Comparison of the preprocessed CR and SERS spectra of the same blood serum sample.

Table 1 Peak positions and their characteristic assignments. Raman shift (cm-1) Peak assignment Ref.

Conventional Raman

525 (S-S) disulfide stretching in proteins Phosphatidylserine [37, 38]

641 (C-S) L-tyrosine, lactose [39]

770 (C-H) Hypoxanthine, Phosphatidylserine [40-43]

1100 u(C-C)-lipids, fatty acids [44]

1543 un(Cp Cp), tryptophan [45-46]

1661 (C-O) Amide I, a-helix [40-43]


498 (S-S) disulfide stretching in proteins Phosphatidylserine [37, 38]

641 (C-S) L- tyrosine, lactose [39]

728 (C-H) Hypoxanthine, Phosphatidylserine [37, 38]

813 (C-C-O) L-Serine, glutathione [37, 38]

890 (C-O-H) Glutathione, D-(C)-galactosamine [37, 38]

961 u (C-C), Cholesterol [37, 38]

1003 (C-C) Phenylalanine [39]

1086 (C-N) Stretching, Phosphodiester groups in nucleic acids [37, 38]

1094 (C-N) Stretching mode in D-mannose [47]

1132 (C-N) Ascorbic acid, L-serine [37, 38]

1210 C-C6H5 stretching, L-tryptophan, phenylalanine [48]

1291 CH2 twisting in phospholipids [37, 38]

1326-1329 CH vibration in DNA/RNA, CH2 twisting in lipids [41]

1331 (C-H) Nucleic acid base [40]

1400 (C-H), Collagen, phospholipids [41]

1450 (C-H2) deformation, Acetoacetate, tryptophan [37, 38]

1584 (C-C) bending mode of phenylalanine [40]

1655 (C-O) Amide I, a-helix [37, 38]

Laser-induced fluorescence is the most common source of interfering baseline signal encountered in Raman measurements. The fluorescence interference in Raman spectroscopy may result from the compound analyzed or from fluorescent impurities in the sample. Autofluorescence background can be produced by tryptophan and tyrosine side chains in folded proteins, by carotenoids and photosynthetic pigments or other chromophores within microbial, animal and plant cells. In addition, it may due to manufacturing additives.

The observed CR and SERS bands are described in Table 1. The major CR peaks are observed at 525, 641, 770, 1100, 1543, 1661 and 1849 cm-1 and may be attributed to biochemical components of nucleic acids, fatty acids and Amide I group. For SERS measurements we observe strongly enhanced bands which may be attributed to such biochemical components as nucleic acids (641, 724, 813, 1003, 1210, 1132 and 1450 cm-1), carbohydrates (641, 890, and 1094 cm-1), lipids (1278 and 1327 cm-1), etc. The observed SERS peaks are indicators of the corresponding serum components [2932].

Several of the observed bands clearly stand out by the impact of SERS technique at 724, 813, 890, 961, and 1132 cm-1. These bands were undetectable by CR spectroscopy, as the intensities of these bands are weaker than the intensity of the autofluorescence and noise signals. The SERS spectrum of serum with AgNPs showed many dominant vibration bands, indicating a strong interaction between the silver colloids and the serum substances. This interaction also indicated that biochemical ingredients in the serum were closely adsorbed onto the surfaces of the AgNPs, and Raman scattering took place in the highly localized optical fields of these structures, which resulted in a strong enhancement in the intensity [29-32].

In SERS spectrum, bands of several molecules could be shifted compared with the bands of these molecules in

the CR spectrum. Such shift may be observed from 525, 770, and 1100 cm-1 bands to 498, 728, and 1003 cm-1 bands respectively. This might be due to the strong interaction between prepared substrate and analyte [33]. It is also found in Fig. 4, which demonstrates the redshift of the SERS spectrum relative to the CR spectrum that observed shift is related to the change of the chemical bond's energy. Some studies have also found the influence of atomic species on the Raman activities. The redshift in SERS is mainly due to the change of the localized plasmons depending on the dielectric functions of metallic nanostructures that, in turn, depend on different parameters, such as the medium, size of NPs, and intrinsic damping of metal, which are known to display this interesting common phenomenon [31]. Moreover, the reason for the blueshift or the redshift of the Raman peak is the change of the corresponding chemical bonds, leading to the migration of electron clouds. Specifically, the change involves the transformation of interatomic bond force and distance [34].

Thus, the spectral bands and SERS characteristics of the serum obtained in the current study are similar to those reported in the studies of other authors. However, Huang et al. and Feng et al. [35, 36] reported SERS bands at 702 cm-1 and 1383 cm-1 which were not observed in our study; these bands were assigned to the cytosine and CH3 domains respectively.

The next stage of our research was the analysis of the results repeatability and the stability of the proposed silver substrates. To clarify the central location of the spectral data distribution, a calculation of a mean spectrum was carried out, and the standard deviation of the collected raw spectra was also performed to assess the spread of the values above and below the mean. Such data is presented in Fig. 5 and Fig. 6.



±standard deviation

Wavenumber, cm 1

Fig. 5 Mean and standard deviation for human serum sample examined 15 times in the same conditions using single SERS substrate.

Fig. 6 Mean and standard deviation for serum sample examined for 5 different SERS substrates at 5 different spots.

Table 2 EF values for different spectral bands.

Raman shift (cm-1) iraman Isers EF


±standard deviation


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321 521 721 921 1121 1321 1521 1721

Wavenumber, cm"1

727 0.240 641 131

815 13.4

1132 10.5

The low values of standard deviation show that the data are clustered closely around the mean, thus, the proposed SERS technique is reliable and may be applied in quantitative measurements. In case of a single SERS substrate application maximum standard deviation was up to 8%. In case of different SERS substrates utilization, the standard deviation was up to 19%.

The value of EF depends on the morphology of the NPs. However, the physic-chemical properties of the interfaces, such as the pH, the presence of impurity species, and the available surface on the metal, are also important and may affect the EF value. The interfacial properties of metal surfaces should be considered to understand why certain molecules do not work well in SERS, or why the spectral profile changes from preparation to preparation. The prepared AgNPs were effective and stable, as they are attached to a substrate standing at an appropriate interparticle distance to have a giant electromagnetic enhancement with the formation of hot spots [49]. Lin et al. [50] reported an EF of (5.6 x 109) using gold "pearl necklace" nanomaterials substrate. While Wang et al. [51] found that highly-branched gold nanoparticles provided an EF of (3.6 x 105). However, H. Wang et al. [52] reported an EF of (105) for the Ag metalized substrates. The highest EFs observed in the

313x10 3.91x105

479x10 1.10x103

136x10 3.04x103

231x10 6.60x103

study are collected in Table 2. We found that the largest EF that was calculated from Eq. (1) is detected at 727 cm-1 peak and was 3.91 x 105. Note here, that in order to get a higher EFs, it is possible to consider CR bands (IRaman) close to 0, and if newly appeared SERS bands are detected, than the calculated EF value may be extremely high.

The storage stability of the substrates was tested during 1-month period. In this period, we estimated standard deviation of the SERS signal and tracked EF changes. During the tracked period the standard deviation was not higher than the standard deviation observed for single substrate measurements presented above (up to 19%), and EF did not decrease by less than 8%. However, for future possible clinical applications the stability of the substrate should be estimated for a longer period. Important to note, that no blinking phenomenon [53] of SERS spectra from Ag nanoparticles was detected. The term "blinking" is used to describe temporal fluctuations in the Raman spectral signal. It is typically accepted that the observation of blinking behavior in the SERS spectrum is indicative of a single molecule undergoing adsorption/desorption cycles at a SERS "active" site. Blinking in SERS may be attributed to thermal diffusion of a single molecule in and out of hot spots, where

alterations in position of a few nanometers can create a significant change in SERS signal intensity [54, 55]. The intensity of SERS spectra for 5 different SERS substrates at 5 different points and for 15 times in the same condition for one SERS substrate did not change significantly and is within the standard deviation of 19%.

In addition, we showed non-invasiveness of the proposed approach as we examined the serum sample multiple times at the same spot and observed no changes in the intensity for each performed test. It is worth mentioning that the proposed method is quite promising since, unlike previous studies, we employed a non-cooling camera for spectra collection. For instance, Zhou et al. [55] studied SERS effect collecting spectra using a cooling system camera set to -70 °C.

SERS is recognized as an ultrasensitive optical detection technique with wide application potential in a variety of fields due to its great sensitivity at the single molecule level. However, the complex manufacturing and expensive price of SERS substrate remain a barrier to its wide application in industry [56]. Aside from the advantages and limitations of SERS technology in comparison to other diagnostic methods, it is also important to notice that SERS-active substrates are most often fabricated with transition metals (Rh, Pd, Pt, Fe, Co, etc.) and plasmonic metals such as Au and Ag, which have the ability to induce localized surface plasmon resonance, which is a result of the coupling of resonant oscillations of electrons and electromagnetic fields near the nanostructures. However, transition metals have also explored found to impart weaker SERS effects compared to plasmonic metals [57]. In the material selection process, it is also important to consider the suitability of the substrate material in terms of its stability in an environment or condition where it is to be applied. Each plasmonic metal exhibit their own characteristic properties that are not found in other metals; for example, gold is a biocompatible material but provides a lower signal enhancement than silver, which on the other hand is less stable due to it being prone to oxidation [58]. Studies have thus explored the use of both of these metals to take advantage of combining these properties, giving synergistic effects.

Homogeneity is another key point in the evaluation of the SERS substrate, especially when it is used for quantitative detection and imaging. The detection result is reliable only when the results from the substrate are reproducible because SERS is a localized phenomenon and it is highly sensitive to the local structure of the substrate and the surrounding environment. For the NP substrates, the homogeneity in size, shape, and aggregation state will increase the reproducibility. Besides the higher enhancement or better homogeneity, the cost of substrate fabrication should also be considered in SERS applications.

Our proposed approach was found to be considerably easier, faster and more convenient than the methods stated in earlier studies. The colloidal substrates are prepared by either the chemical reduction process or by laser ablation procedures. For instance, Chu et al. [59]

proposed a laser ablation-assisted method to fabricate the SERS substrate on the teflon. For the reduction method, Chi et al. [60] created a Silver NP colloidal solution by reducing silver nitrite with sodium citrate in the presence of ultraviolet (UV) light by dropping sodium citrate solution to silver nitrite solution and placing the mixture in a UV chamber. Silicon substrates were immersed in an Ag NP colloidal solution to allow Ag NPs to adhere to the surface. Huang et al. and Zong et al. [35, 61] prepared colloids by adding NaOH solution to hydroxylamine hydrochloride solution and mixing the solution with AgNO3 aqueous solution at room temperature, until the formation of a milky-grey color. The Ag colloids were pelleted by centrifugation. The pelleted Ag NP was then mixed with urine or serum samples at a 1:1 ratio in a rectangle aluminum plate. In another work, Jing et al. [62] used a seed growth technique with AuNPs as seeds to create spherical AgNPs by adding HAuCl4 to deionized water, the solution was heated to boiling. Then, trisodium citrate was immediately added. Serum samples were mixed with the prepared AgNPs solution after cooling. The mixture was transferred to a silicon wafer for SERS analysis.

In comparison to the observed techniques, we have successfully developed a new simple and low-cost AgNPs SERS substrate with excellent sensitivity and promising stability. We obtained excellent SERS spectra using a digital camera without cooling (working at ambient temperature). The proposed technique of blood serum analysis has great potential to significantly increase the application of SERS methods, particularly in clinical applications. Nevertheless, further research in a larger group of collected blood specimens is required to confirm the applicability of the proposed techniques in clinical settings.

4 Conclusion

In this study human blood serum samples were examined using CR and newly proposed SERS technique. To demonstrate the applicability of the proposed technique the serum sample was examined 15 times in the same condition for one SERS substrate and then examined for 5 different SERS substrates at 5 different spots, calculated standard deviation for these cases was up to 8% and up to 19% correspondingly. The obtained results indicate accuracy and stability of the proposed SERS substrates. Demonstrated results shows that the obtained SERS effect helps to achieve up to 4 x 105 EF with silver NPs application for 785 nm laser excitation. Moreover, the proposed SERS technique provides a capability to detect Raman bands 724, 813, 890, 961, and 1132 cm-1 which may be attributed to such biochemical components as nucleic acids, carbohydrates, lipids, etc. These bands were not presented in the CR spectra of human serum; thus SERS analysis increases the possibility to detect disease biomarkers during blood samples analysis. The obtained results demonstrate that the proposed SERS technique is stable, no-invasiveness with no blinking phenomenon and has significant potential in clinical diagnosis applications.



All authors declare that there is no conflict of interests in this paper.

The study was supported by a grant from the Russian Science Foundation No. 21-75-10097 https://rscf.ru/project/21-75-10097/.


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