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Abstract
The purpose of this article is to propose an optical fiber sensor probe based on the localized surface plasma resonance (LSPR) technique for the detection of creatinine in aquaculture. The sensing probe is functionalized through the use of gold nanoparticles (AuNPs), niobium carbide (Nb2CTx) MXene, and creatinase (CA) enzyme. The intrinsic total internal reflection (TIR) mechanism is modified to increase the evanescent field intensity using a heterogeneous core mismatch and tapering probe structure (i.e., convex fiber-tapered seven core fiber-convex fiber (CTC) structure). Strong evanescent fields can stimulate AuNPs and induce the LSPR effect, thereby increasing probe sensitivity. The specific recognition is enhanced by Nb2CTx MXene adsorbing more active CA enzymes. The developed sensor probe has a sensitivity and limit of detection of 3.1 pm/µM and 86.12 µM, respectively, in the linear range of 0-2000 µM. Additionally, the sensor probe's reusability, reproducibility, stability, and selectivity were evaluated, with satisfactory results obtained with impact for areas like food protein, marine life and healthcare.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Creatinine is synthesized in the body in two distinct ways. The flesh material that is edible in the human body decomposes after consumption and generates exogenous creatinine; the flesh material that is not edible in the human body produces flesh anhydride of phosphate muscle acid and generates endogenous creatinine as a result of the human body's metabolism within the muscle organization. Creatinine testing is a primary method for determining renal function in the clinic [1]. Increases in agricultural, industrial, and household pollutants all contribute to water quality degradation. Industrial wastewater is the primary source of water contamination in the global environment. As we all know, with the exponential growth of the world's population, it is impossible to meet human nutritional needs solely through natural fish reintroduction [2,3]. Agriculture and Aquaculture sector, particularly farming and recirculating aquaculture systems (RAS), are significant sources protein and the water quality is critical for these sectors. If the water pollution if not well treated, regulated and produced cutting edge technology to have great progress in these areas. In particular, land-based fish farming needs, more than even, novel understandings and innovation on water quality measurements [4–6] since a cumulative effect on water pollution, which increases with the expansion of animal husbandry and aquaculture [3,7] can be a huge problematic. For instance, the wastewater leaving the RAS process typically comes from the mechanical filter, where faeces and other organic matters are separated into the sludge outlet of the filter [8]. Cleaning and flushing biofilters also add to the total wastewater volume from the RAS cycle. Treating the wastewater leaving the RAS can be accomplished in different ways [8] where the fish welfare depends of such quality of water. Fish that have been exposed to contaminated water for an extended period of time may develop significantly elevated cortisol, urea, and creatinine levels. As a primary source of nutrition for humans, mostly creatinine concentration through fish will be deposited in the body along with the food chain. Although the physiological concentration of creatinine in the human body ranges between 40 µM and 150 µM, the pathological value can reach 1000 µM or even 2000 µM due to muscle disorders, renal insufficiency, or excessive intake of creatinine substance meat [9]. Therefore, a simple creatinine biosensor can be developed for the purpose of non-destructively inspecting the creatinine concentration in the human body and fish in order to achieve the goal of early detection and prevent chronic diseases.
At the moment, researchers primarily use the Jaffe technique [10,11], spectroscopy technique [12,13], electrochemical technique [14,15], and Raman scattering technique [16–18] to determine the concentration of creatinine. These are typically sophisticated operations that are susceptible to electromagnetic interference and have low sensitivity. Unlike the above biosensor types, fiber-based biosensors are cost-effective, small in size, flexible and light in weight, and their anti-electromagnetic interference properties make them medically applicable in the fields of endoscopy and laser surgery. Studies have shown that biological binding processes occurring around the fibers can also be detected by modifying the fiber structure (grating, nanoparticle deposition, etching, tapering). In addition, fiber-based biosensors are biocompatible (according to the ISO 10993 standard) and can work in hazardous environments and complex biological media [19,20]. Localized surface plasmon resonance (LSPR) has a higher sensitivity and selectivity than conventional methods and is relatively simple to operate [21,22]. Singh et al. proposed a fiber optic LSPR-based cell sensing technique for the efficient detection of various types of cancer cells [21]. Kumar et al. successfully developed a LSPR based fiber optic sensor using AuNPs and a two-dimensional (2D) material functionalization for the detection of Shigella bacteria [22].
The LSPR effect is a significant mode of energy conversion that takes advantage of the interaction of light and matter to control the electromagnetic field and carrier properties of nanostructures. When light strikes precious metal nanoparticles (MNPs), the LSPR effect occurs. When incident light's photon frequency matches the vibration frequency of noble MNPs, they absorb a significant amount of photon energy [21–23]. The plasma biosensor based on LSPR technology is extremely sensitive and operates on a simple principle. The resonant electromagnetic field is extremely sensitive to variations in the refractive index (RI) of the medium surrounding MNPs, which is how LSPR is generated [24,25]. Colloids and other non-metallic NPs exhibit greater light scattering and absorption. The development of these sensors requires the fabrication of metallic nanomaterials (NMs) such as nanoballs, nanorods, nanowires, and NPs in order to enhance the plasma phenomena of the devices [23,24].
MXene is a new class of 2D functional materials based on 2D transition metal carbide/nitride. It has a continuously adjustable electronic structure ranging from gold to semiconductor and a rich surface chemical composition, offering a broad range of application prospects in energy storage [26], catalysis [27], biology [28], electronics [29], sensing [30,31], and a variety of other fields. MXene has been extensively used in biosensing due to its unusual physical and chemical properties. MXene's layered structure results in a greater specific surface area, that increases the surface area of MXene when it comes into contact with external substances. MXene's surface contains an abundance of hydrophilic functional groups such as = O, -OH, and –F, that significantly enhances the adsorption of aqueous biomolecules to MXene. MXene's large surface area and excellent hydrophilicity enable the adsorption of more biomolecules, increasing detection sensitivity [30]. Due to the diversity of its chemical composition and crystal structure, it enables a high degree of control over its physicochemical properties and causative roles. In comparison to noble metal, Nb2CTx MXene exhibits superior light absorption properties, and its surface is easily coated with diverse functional groups while retaining its metal conductivity [30,32,33]. Additionally, Nb2CTx MXene exhibits a significant LSPR effect and a high degree of hydrophilicity in the UV-visible-infrared range due to the abundance of functional groups on its surface [28,30]. As a result of its ease of chemical bonding to other ions and macromolecules, Nb2CTx MXene may be used to attach NMs to the component under test.
The purpose of this work is to develop a biosensor that utilizes Nb2CTx MXene to enhance the LSPR effect of AuNPs for the measurement of creatinine levels found in aquaculture industry. This article is divided into four sections, beginning with the introduction. Section 2 is devoted to experiment preparation, that includes an overview of the experimental instruments and materials, fabrication of the sensor probe, and synthesis and immobilization of functional materials. Section 3 discusses the experimental results and analysis, which include the sensor probe selection, characterization of functional materials and functionalized probe, and measurement of analytical fluid, as well as the sensor probe's reusability, reproducibility, stability, and selectivity. Section 4 of the work is focused on its conclusion.
2. Materials and methods
2.1 Materials
Sensing probes were fabricated using standard single-mode fiber (SMF, 9/125 µm) and seven-core fiber (SCF, 6.1/125 µm) that were purchased from Shenzhen EB-link Technologies Co., Ltd. in China, and Fibercore Ltd. in the United Kingdom, respectively. AuNPs solution was synthesized using tetrachloroauric acid (HAuCl4), trisodium citrate, and deionized (DI) water. The AuNPs were immobilized to fiber structure using (3-mercaptopropyl) trimethoxysilane (MPTMS). Nb2CTx MXene was purchased from Jiangsu Xianfeng Nano Material Technology Co. Ltd. Creatinase (CA) enzyme, N-hydroxysuccinimide (NHS), ethyl (Dimethylaminopropyl) carbodiimide (EDC), and 11-mercaptoundecanoic acid (MUA) were purchased from Sigma-Aldrich (Shanghai) Trading Co., Ltd.
2.2 Instruments and measurements
The tungsten-halogen lamp (HL-2000) and spectrometer (USB2000+) used to test the light transmission characteristics of the sensor probe were purchased from Ocean Optics, USA. Sensing probes were fabricated using a special fiber optic fusion splicer machine (FSM, Japan) and a 3SAE combiner manufacturer system (CMS, USA). Quantitative fiber length cutting will be performed using the ultra-large core-diameter fiber cutting machine (CT106, Japan). The absorption spectrum of AuNPs was determined using a UV-Vis spectrophotometer (Hitachi-3310, Japan) in order to determine the peak LSPR wavelength of synthesized NPs. The geometry of NMs in an aqueous solution was investigated using high-resolution transmission electron microscopy (HR-TEM, Japan). The field emission scanning electron microscopy (FE-SEM, Japan) was used to characterize the surface of functional sensing probes, and SEM-EDS was used to determine the chemical element on the surface of functional sensing probes.
2.3 Fabrication of the sensor probe
SCF has the advantages of low loss when combined with SMF and sensitivity to changes in the RI of the surrounding media, that enables the fabrication of compact and highly sensitive sensing probes. Additionally, SCF's cladding contains seven fiber cores, that also increases the degree of freedom of fiber parameters, enabling the development of SCF-based sensor probes [21,22]. The FSM splicer machine is used to splice the SMF-SCF together to develop a convex structure. The splicing process is set to automatic mode, the discharge time is 3000 ms, the discharge power is 357 bits, the overlap distance is 160 µm, and the re-discharge power is 512 bits. During the electrode discharge process, the optical fiber softens under high temperatures and gradually expands to form a convex structure under the thrust of the splicing machine's motor. The raised area measures approximately 160 µm in diameter. The splicing process is extremely stable and allows for the precise fabrication of multiple probe structures. The same process was used to fuse the convex SMF-SCF structure with the SMF structure to form Convex SMF-SCF-Convex SMF (CSC) structure. Figure 1(a) illustrates a schematic diagram of the CSC structure-fabrication process. CMS machine was used to taper the CSC sensor probe, resulting into the Convex Fiber-Tapered SCF Fiber-Convex Fiber (CTC) novel fiber structure, that allows for increased light penetration into the cladding. The structure-development process for the CTC structure is depicted schematically in Fig. 1(b).
2.4 Sensing principle of the probe
Tapered optical fiber directly lead to the change of the fiber diameter, thereby allowing higher order cladding mode and the initial incentive model of induction for power exchange, optical power exchange depends on the pattern shape and pattern, the phase difference between the optical fiber taper can change the original transmission path, the RI of cladding medium is less than the core medium, this means that the cladding cannot form total internal reflection. Therefore, more light is allowed to leak into the surrounding media through the cladding, increasing the intensity of the evanescent field on the fiber surface. The optical properties of the fiber are affected by the taper region, resulting in interactions between the surrounding medium and the evanescent waves (EWs). Evanescent field can improve the performance of sensing system.
When incident light enters the tapered region for the first-time following transmission through SMF, it excites a new conduction mode in the six fiber cores and cladding surrounding SCF due to the mode field diameter mismatch. Without the convex cone structure, incident light was not coupled to the six fiber cores of SCF; however, with the convex cone structure, incident light was coupled to a portion of the six fiber cores, ensuring that each fiber core has an adequate light field to effectively excite the higher-order cladding mode. The incident light is split into two components: the core and the cladding mode. Light waves enter the SCF after passing through the convex cone region. Due to the large distance between fiber cores, light can be transmitted independently from each fiber core. When these two upward tapers collide, they recouple and are then transmitted into the SMF's core. The introduced downward taper was used to stimulate the generation of more EWs. The generation of EWs is critical for the development of LSPR sensors. On the one hand, EWs can induce and excite LSPR phenomena after interactions with AuNPs, thereby generating absorption peaks associated with RI. Additionally, EWs can generate sufficient spatial overlap to improve perceived biological response behavior and sensing performance. The transmission spectrum of the structure can be expressed using the interference between the core and cladding modes [34]:
where, I is the total interference strength, ${I_{core}}$ and ${I_{clad}}$ represent the mode field strength of fiber core and cladding, respectively, and $\delta $ represents the phase difference: here, $\lambda $ is the central wavelength of transmission spectrum, $\Delta {n_{eff}}$ is the effective RI variation, and $\; L$ is the length of the sensing region. It should be pointed out that the sensor proposed in this experiment is based on wavelength modulation sensor. According to Mill′s theory, the resonance absorption peak will also change when the RI of the medium around AuNPs changes. Therefore, the sensitivity of the sensor will mainly depend on $\Delta {n_{eff}}$. The RI changes when biomaterials attach to the AuNPs/Nb2CTx MXene surface, and the LSPR spectrum's absorption peak shifts. The absorption peak wavelength shift can be represented as [35]: where, m represents the sensitivity of NPs to the electromagnetic field, d represents the effective thickness of the adsorption layer, and ${d_p}\; $ represents the penetration depth of the evanescent field. When the incident light passes through the taper region of the fiber structure, the EWs attenuates exponentially in the cladding layer, and the distance transmitted by the evanescent field at the fiber core-cladding interface attenuates to 1/e being defined as the penetration depth, that can be expressed as [36,37]: here, ${n_{core}}$ and ${n_{clad}}$ represent the RI for fiber core and cladding, respectively. ${\theta _i}$ denotes the angle of ray incidence between the fiber core and cladding. Modulated light waves propagating through the sensing area are gathered in the bulge and sent through the SMF. Due to the bigger numerical aperture of SMF, it has a good signal-to-noise ratio (SNR). The large core diameter enables the SMF core to collect transmission light in the SCF, converting multi-mode signals into single-mode signals that are sent via the SMF and demodulated to provide real-time LSPR sensing spectra.2.5 Synthesis of nanomaterials
For the LSPR effect, AuNPs with a diameter of about 10 nm were synthesized using the Turkevich method [38]. The solution of AuNPs synthesized using this method can be stored at room temperature for two months. To obtain the MXene dispersion, 2 mg of Nb2CTx MXene was dissolved in 10 mL of organic solvent N,Ndimethylformamide (DMF). Further, the dispersion of Nb2CTx MXene was attained using an ultrasonic machine for one hour [32,39].
2.6 Enzyme functionalization over sensing probe
Clean and polish the probe sensing region with acetone to remove the majority of the organic dust. After washing, the sensing area was immersed in piranha solution (H2O2:H2SO4 = 3:7) for 30 minutes to establish a layer of hydroxyl on the probe surface. The probe was then cleaned with deionized water and dried at a temperature of 70°C. For 12 hours, the dried sensor probe was immersed in an ethanol containing MPTMS to modify the sulfhydryl group in the sensing region. The unbound MPTMS monomers were then rinsed with ethanol. The sensing region was then immersed in AuNPs solution to form a stable covalent bond with the MPTMS mercaptan group, and AuNPs were attached to the probe surface [22,36].
The sensor probe was immersed in a DMF dispersion of Nb2CTx MXene for 12 hours, and Nb2CTx MXene was deposited on the surface of AuNPs [30,33]. Figure 2 depicts a model diagram of the sensor probe that has been immobilized by nanomaterials.
The amino-conjugated fixation method was used to immobilize CA enzyme on the surface of NMs. Because amine groups are ubiquitous, immobilized ligands via amine coupling are compatible with the majority of biological molecules. This method randomly immobilizes ligands and typically produces results of high quality. The most frequently used method involves activating the carboxyl group with a mixture of NHS and EDC to form an amine active lipid. DI water was used to clean the fiber probe's surface. The immobilized NMs sensor probe was then immersed in MUA ethanol solution (5 mL, 0.5 mM) for 5 hours to permanently fix a carboxyl group layer on the probe surface. The sensor probe was then immersed for 30 minutes in a 5 mL mixture solution of EDC (200 mM) and NHS (50 mM) to activate the carboxyl group and form the amine active lipid. Finally, the sensor probe was functionalized in a CA enzyme solution (20 mL, 0.1 mM). To achieve enzyme functionalization of the sensor probe, the NHS ester forms a stable covalent bond with the CA primary amine [1,36]. Figure 3 illustrates the chemical bonding that occurs as a result of the functionalization process.
2.7 Preparation of the creatinine solution
To prepare a 2 mM creatinine stock solution, 114.26 mg (99%) of pure creatinine powder was dissolved in 50 mL of 1×PBS solution. Different concentration of creatinine solutions was prepared by diluting the stock solution with DI water. Ten different samples with creatinine concentrations ranging from 0 to 2000 µM were used to evaluate the performance of the proposed CTC fiber-based novel sensing probe.
2.8 Experimental setup
The functional sensor probe was used to conduct the experiments. Figure 4 illustrates the devices used in this study. The sensor probe was immersed in a reaction dish containing a solution of creatinine sample. The optical signal from a light source (HL-1000) was launched into the sensor probe. The spectrometer (USB2000+) collects the output spectrum of the functional sensor probe and sends it to the computer for further data processing in order to acquire the LSPR spectra.
Fig. 4. Experimental setup for detection of creatinine solution using proposed CTC-based sensor probe.
3. Results and discussion
3.1 Sensor probe selection and fabrication
In the preparation stage of CSC structure, the length of SCF was uniformly changed to prepare CSC structure. Three CSC structures were made for each length. The transmission intensity of three probes with different SCF lengths was measured by the experimental setup shown in Fig. 4. The transmission intensity of the three probes with different SCF lengths was averaged to obtain the spectrum in Fig. 5(a). The maximum transmission intensity of the fiber probe with different SCF lengths was drawn in Fig. 5(b). It can be seen from Fig. 5(a) and 5(b) that when the length of SCF is 30 mm, the transmission intensity is the lowest, which also means that has a strong evanescent field on sensing region at this length. A strong evanescent field will be helpful to stimulate LSPR and enhance the sensing performance. The transmission intensity of three sensing probes with a length of 30 mm was measured as shown in Fig. 5(c). It can be seen that different sensing probes with the same length have excellent repeatability. Then, the CSC structure with an SCF of 30 mm was tapered.
The diameter scanning of the tapered area of the CTC structure is shown in Fig. 5(d), where the tapered diameter is 40 µm and its structure also has excellent repeatability.
Fig. 5. Optimization results of proposed CSC optical fiber sensor probe. (a) transmission intensity spectrum vs. SCF length variation, (b) peak value of transmission intensity at different SCF lengths, (c) repeatability of fiber structure with length of 30 mm, (d) diameter scan results of CTC probe.
3.2 Characterization of the nanoparticles
The absorption spectrum of AuNPs was determined using a UV-vis spectrophotometer, obtaining the spectral results shown in Fig. 6(a). The results indicate that the synthesized AuNPs have an absorbance peak at 519 nm, indicating a diameter of about 10 nm. Additionally, HR-TEM was used to characterize the NPs solution as shown in Fig. 6(b). The synthesized AuNPs have spherical shape with an uniform diameter. The diameter of the majority of AuNPs is uniform at 10 nm, as shown in Fig. 6(c). HR-TEM was also used to characterize Nb2CTx MXene and found that the geometric form of Nb2CTx MXene is a layered structure as illustrated in Fig. 6(d).
Fig. 6. (a) Absorbance spectrum, (b) HR-TEM image, (c) histogram of AuNPs, and (d) HR-TEM image of MXene.
3.3 Characterization of the nanomaterial-immobilized structure
The overall structure and surface material morphology of the fiber probe was examined using advanced SEM. Figures 7(a) and 7(b) depict the probe's entire image at lower magnifications. Figure 7(c) also clearly shows the SEM results for AuNPs and Nb2CTx MXene immobilized to the structure's surface. Figure 7(d) depicts the energy spectrum obtained from the functional region's energy spectrum analysis. As can be seen, the functional region contains an abundance of Au and Nb elements. This also demonstrated the effective immobilization of AuNPs and Nb2CTx MXene to the probe surface.
Fig. 7. (a, b) SEM images of CTC fiber sensor structure, (c) AuNPs/Nb2CTx MXene-immobilized sensor structure, (d) SEM-EDX of AuNPs/Nb2CTx MXene-immobilized sensor structure.
3.4 Measurement of the analytes
LSPR spectra of various concentrations of creatinine solution were acquired using the experimental setup described in Fig. 4. With increasing time after adding creatinine solution, the resonance wavelength gradually undergoes a redshift. With increasing creatinine concentration, the wavelength shift increased. To minimise measurement error caused by the testing process, three probes were used to evaluate sensing performance throughout the experiment, and the results of the three groups of experiments were averaged to obtain the LSPR sensing spectrum shown in Fig. 8(a). After analysing the aforementioned detection data, Fig. 8(b) illustrates the relationship between the concentration of the sample solution and the resonance wavelength. The abscissa represents the concentration of creatinine in the sample solution, while the ordinate represents the resonance wavelength associated with that concentration. The linear regression equation describing the relationship between the concentration of the sample solution and the resonance wavelength is as follows:
here, c is the concentration of creatinine solution sample in µM. The designed sensor has sufficient linearity based on the correlation fitting factor, ${\textrm{R}^2} = 0.9774$. Creatinine combines with oxygen and is broken down into creatine at an accelerated rate catalyzed by CA enzyme, this chemical reaction equation is as follows:Changes in the RI of surrounding media as a result of creatinine decomposition into creatine and other compounds catalysed by the CA enzyme. Changes in RI will result in a significant change in the LSPR sensor's excitation resonance peak. As illustrated in the inset of Fig. 8(a), the peak wavelength of the LSPR spectrum is redshifted as creatinine concentration increases. The wavelength modulation sensing method was used in the experiment, and the sensitivity (S) was calculated to be 3.1 pm/µM using the following formula:
where, $\Delta s$ is the wavelength shift, and $\Delta c$ represents the concentration range from maximal to minimal.3.5 Reproducibility and reusability test
When evaluating the performance of a sensor, reproducibility and reusability are critical considerations. When the test solution is identical in concentration, reproducibility determines whether a sensor can reproduce the same results. Three probes were randomly chosen to test the same concentration in order to establish the probe's reproducibility. To minimise the effect of errors on the experimental results, the three measurement experiments used the same external variables. Additionally, reproducibility refers to the probes’ interoperability. The test procedure is to use three probes to determine the concentration of 1000 µM (300 µL) and to record the resulting LSPR spectrogram. As illustrated in Fig. 9(a), different probes exhibit excellent reproducibility when used with the same solution.
The reusability test entails testing the same concentration consecutively with a sensor probe and comparing the two test results for similarity. In this experiment, 1000 µM and 2000 µM creatinine solutions were measured twice with a probe, and the experimental results were compared as shown in Fig. 9(b). The sensor spectrum is similar for the same concentration tested, as are the peak wavelength positions. This indicates that the sensor probe developed can perform repeated measurements. It should be noted that prior to each concentration test, the sensor probe must be rinsed with 1×PBS solution to remove any by-products on the sensor probe surface.
3.6 Stability and pH test
Stability testing is used to determine the stability and reliability of the sensor probe over a series of measurements. Stability refers to a sensor's ability to maintain its performance parameters over time. The stability of this test is defined as the difference between the sensor's peak wavelength at this time and the sensor's initial peak wavelength after a specified time interval (10 minutes) at room temperature. This difference is referred to as the stability error. The standard deviation is frequently used to express the stability error (SD). Detection of a probe using fifteen consecutive measurements of PBS solution under the same sensing conditions. As illustrated in Fig. 10(a), the corresponding peak wavelength of these spectra aids in ensuring the stability of the test patterns. The SD of this measurement is too small at 0.089, indicating a high level of stability. According to Eq. (8), the test's limit of detection (LoD) is 86.12 µM [15,16].
The pH test is used to determine the pH of solution that is most appropriate solvent for target analyte. Acetic acid, ethanol, and potassium hydroxide (KOH) are used to adjust the pH of acidic and alkaline solvents, respectively. Five different solvents were used: acetic acid solution (pH-4), ethanol (pH-6), PBS (pH-7.4), and KOH (pH-10,14). In the preceding test, creatinine solutions with concentrations of 0 µM and 2000 µM were prepared. Before the new measurement, prior solution is removed then rinsed with PBS and dried the sensor probe.
As illustrated in Fig. 10(b), when PBS was used as the solvent, the creatinine solution with a 2000 µM concentration difference showed the maximum wavelength shift. As a result, PBS was chosen as the most appropriate solvent for the proposed experiment, as well as being compatible with the pH of potential aquaculture water. Additionally, PBS solution, as a phosphate buffer and pH buffer, not only maintains the salt concentrations of biological substances in balance, but also protects the structure and biological characteristics of such biological proteins, ensuring that active substances participate in biological reactions under optimal conditions.
3.7 Selectivity test
To assess the probe's capability for testing in complex biological conditions, interferers such as creatine, sarcosine, ascorbic acid, pyruvate acid, and uric acid were selected for selective testing. The maximum concentration (2000 µM) and minimum concentration (0 µM) were chosen for testing within the linear range. When creatinine is identified, the probe exhibits the largest wavelength shift, in comparison to the wavelength shift of other biomolecules, as shown in Fig. 11. This is related to the fact that the enzyme catalyzes the formation of creatinine, which results in a significant change in the RI surrounding the probe. Additionally, it is demonstrated that the sensor probe has a high degree of specificity and reliability.
3.8 Evaluation of the sensing performance
In order to compare the newly developed probe's performance with other established sensors for the detection of creatinine, a series of performance factors, such as detection range, LoD, and sensitivity, were measured and compared with those of other established sensors. Table 1 illustrates this point by comparing the various characteristics.
Table 1. Performance comparison of the proposed sensor with other established sensors
4. Conclusion
In this study, the concentration of creatinine was determined using a novel heterogeneous core fiber-based CTC probe integrated with LSPR technology. By combining heterogeneous core fusion with a tapering probe structure, it was possible to enhance the evanescent field intensity while simultaneously modifying the intrinsic TIR mechanism. With the help of AuNPs, Nb2CTx MXene, and CA enzyme, able to achieve excellent sensitivity and specific recognition for the sensing probe. The sensitivity and LoD of the probe were 3.1 pm /µM and 86.12 µM in the linear range of 0–2000 µM, where R2 was a linear fitting coefficient of 0.9974. This means that in the linear range, the resonant peak wavelength is uniformly redshifted as the concentration increases. The sensor probe's reusability, reproducibility, stability, pH test, and selectivity were all tested, and the results were satisfactory. These encouraging experimental results suggest that the LSPR-based creatinine biosensor has a broad range of potential applications in the aquaculture industry.
Funding
Double-Hundred Talent Plan of Shandong Province, China; Special Construction Project Fund for Shandong Province Taishan Mountain Scholars; Liaocheng University (31805180301, 31805180326, 318051901); Natural Science Foundation of Shandong Province (ZR2020QC061); Fundação para a Ciência e a Tecnologia (CEECIND/00034/2018, UI/BD/153066/2022); Fundação para a Ciência e a Tecnologia/Ministério da Educação e Ciência (LA/P/0037/202, PTDC/EEI-EEE/0415/2021, UIDB/50025/2020, UIDP/50025/2020).
Acknowledgments
This work was supported by Special Construction Project Fund for Shandong Province Taishan Mountain Scholars, China. C. Marques and Maria Simone Soares acknowledge Fundação para a Ciência e a Tecnologia (FCT) through the CEECIND/00034/2018 (iFish project) and UI/BD/153066/2022, respectively. This work was also developed within the scope of the projects i3N, LA/P/0037/202, UIDB/50025/2020 & UIDP/50025/2020, and DigiAqua, PTDC/EEI-EEE/0415/2021, financed by national funds through the FCT/MEC.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
References
1. S. Yadav, R. Devi, P. Bhar, S. Singhla, and C. S. Pundir, “Immobilization of creatininase, creatinase and sarcosine oxidase on iron oxide nanoparticles/chitosan-g-polyaniline modified Pt electrode for detection of creatinine,” Enzyme Microb. Technol. 50(4-5), 247–254 (2012). [CrossRef]
2. H. K. Mahmoud, F. M. Reda, M. Alagawany, and M. R. Farag, “The stress of abamectin toxicity reduced water quality, growth performance, immunity and antioxidant capacity of Oreochromis niloticus fish: Modulatory role of Simmondsia chinensis extract as a dietary supplement,” Aquaculture 534, 736247 (2021). [CrossRef]
3. H. Xu, Q. Gao, and B. Yuan, “Analysis and identification of pollution sources of comprehensive river water quality: Evidence from two river basins in China,” Ecol. Indic. 135, 108561 (2022). [CrossRef]
4. C. Leitão, A. Leal-Junior, A. R. Almeida, S. O. Pereira, F. M. Costa, J. L. Pinto, and C. Marques, “Cortisol AuPd plasmonic unclad POF biosensor,” Biotechnology Reports 29, e00587 (2021). [CrossRef]
5. A. G. Leal-Junior, A. Frizera, and C. Marques, “Low-cost Fiberoptic Probe for Ammonia Early Detection in Fish Farms,” Remote Sens. 12(9), 1439 (2020). [CrossRef]
6. L. C. B. Silva, B. Lopes, M. J. Pontes, I. Blanquet, M. E. V. Segatto, and C. Marques, “Fast decision-making tool for monitoring recirculation aquaculture systems based on a multivariate statistical analysis,” Aquaculture 530, 735931 (2021). [CrossRef]
7. H. H. Mahboub, H. M. Elsheshtawy, N. I. Sheraiba, E. M. Fahmy, S. R. masoud, E. A. A. Mohamed, N. S. Abdelnaeim, D. I. Mohamed, T. A. Ismail, and S. A. A. Ahmed, “Dietary black cumin (Nigella sativa) improved hemato-biochemical, oxidative stress, gene expression, and immunological response of Nile tilapia (Oreochromis niloticus) infected by Burkholderia cepacia,” Aquacult Rep. 22, 100943 (2022). [CrossRef]
8. J. Bregnballe, “A guide to recirculation aquaculture: an introduction to the new environmentally friendly and highly productive closed fish farming systems,” Copenhagen, Eurofish International Organisation, Fao, Budapest (2015).
9. K. K. Reddy and K. V. Gobi, “Artificial molecular recognition material based biosensor for creatinine by electrochemical impedance analysis,” Sens. Actuators, B. 183, 356–363 (2013). [CrossRef]
10. M. V. Guterres, P. O. L. Volpe, and M. M. C. Ferreira, “Multiway Calibration for Creatinine Determination in Human Serum Using the Jaffé Reaction,” Appl. Spectrosc. 58(1), 54–60 (2004). [CrossRef]
11. A. Blel, Y. Orven, N. Pallet, J. F. Chasse, B. Vedie, M. A. Loriot, J. L. Paul, and C. Narjoz, “Pegylated liposomal doxorubicin (Caelyx®) interference with the spectrophotometric Jaffe method for quantitative determination of creatinine in human plasma,” Clin. Biochem. 50(7-8), 455–457 (2017). [CrossRef]
12. K. V. Oliver, A. Maréchal, and P. R. Rich, “Effects of the Hydration State on the Mid-Infrared Spectra of Urea and Creatinine in Relation to Urine Analyses,” Appl. Spectrosc. 70(6), 983–994 (2016). [CrossRef]
13. F. Yang, P. Wen, G. Li, Z. Zhang, C. Ge, and L. Chen, “High-performance surface-enhanced Raman spectroscopy chip integrated with a micro-optical system for the rapid detection of creatinine in serum,” Biomed. Opt. Express 12(8), 4795–4806 (2021). [CrossRef]
14. A. M. Fekry, S. A. Abdel-Gawad, R. H. Tammam, and M. A. Zayed, “An electrochemical sensor for creatinine based on carbon nanotubes/folic acid /silver nanoparticles modified electrode,” Measurement 163, 107958 (2020). [CrossRef]
15. S. K. Ponnaiah and P. Prakash, “Carbon dots doped tungstic anhydride on graphene oxide nanopanels: A new picomolar-range creatinine selective enzymeless electrochemical sensor,” Mater. Sci. Eng., C 113, 111010 (2020). [CrossRef]
16. M. Li, Y. Du, F. Zhao, J. Zeng, C. Mohan, and W.-C. Shih, “Reagent- and separation-free measurements of urine creatinine concentration using stamping surface enhanced Raman scattering (S-SERS),” Biomed. Opt. Express 6(3), 849–858 (2015). [CrossRef]
17. H. Zhang, G. Li, S. Li, L. Xu, Y. Tian, A. Jiao, X. Liu, F. Chen, and M. Chen, “Boron nitride/gold nanocomposites for crystal violet and creatinine detection by surface-enhanced Raman spectroscopy,” Appl. Surf. Sci. 457, 684–694 (2018). [CrossRef]
18. K. Karn-orachai and A. Ngamaroonchote, “Role of polyelectrolyte multilayers over gold film for selective creatinine detection using Raman spectroscopy,” Appl. Surf. Sci. 546, 149092 (2021). [CrossRef]
19. M. Sypabekova, A. Amantayeva, L. Vangelista, Á. González-Vila, C. Caucheteur, and D. Tosi, “Ultralow Limit Detection of Soluble HER2 Biomarker in Serum with a Fiber-Optic Ball-Tip Resonator Assisted by a Tilted FBG,” ACS Meas. Sci. Au (2022), early access. [CrossRef]
20. C. Ribaut, M. Loyez, J.-C. Larrieu, S. Chevineau, P. Lambert, M. Remmelink, R. Wattiez, and C. Caucheteur, “Cancer biomarker sensing using packaged plasmonic optical fiber gratings: Towards in vivo diagnosis,” Biosens. Bioelectron. 92, 449–456 (2017). [CrossRef]
21. R. Singh, S. Kumar, F.-Z. Liu, C. Shuang, B. Zhang, R. Jha, and B. K. Kaushik, “Etched multicore fiber sensor using copper oxide and gold nanoparticles decorated graphene oxide structure for cancer cells detection,” Biosens. Bioelectron. 168, 112557 (2020). [CrossRef]
22. S. Kumar, Z. Guo, R. Singh, Q. Wang, B. Zhang, S. Cheng, F. Liu, C. Marques, B. K. Kaushik, and R. Jha, “MoS2 Functionalized Multicore Fiber Probes for Selective Detection of Shigella Bacteria based on Localized Plasmon,” J. Lightwave Technol. 39(12), 4069–4081 (2021). [CrossRef]
23. G. Gauglitz, “Critical assessment of relevant methods in the field of biosensors with direct optical detection based on fibers and waveguides using plasmonic, resonance, and interference effects,” Anal. Bioanal. Chem. 412(14), 3317–3349 (2020). [CrossRef]
24. M. Chauhan and V. Kumar Singh, “Review on recent experimental SPR/LSPR based fiber optic analyte sensors,” Opt. Fiber Technol. 64, 102580 (2021). [CrossRef]
25. H. Altug, S.-H. Oh, S. A. Maier, and J. Homola, “Advances and applications of nanophotonic biosensors,” Nat. Nanotechnol. 17(1), 5–16 (2022). [CrossRef]
26. H. Aghamohammadi, R. Eslami-Farsani, and E. Castillo-Martinez, “Recent trends in the devolvement of MXenes and MXene-based composites as anode materials for Li-ion batteries,” J. Energy Storage 47, 103572 (2022). [CrossRef]
27. S. K. Sharma, A. Kumar, G. Sharma, D.-V. N. Vo, A. García-Peñas, O. Moradi, and M. Sillanpää, “MXenes based nano-heterojunctions and composites for advanced photocatalytic environmental detoxification and energy conversion: A review,” Chemosphere 291, 132923 (2022). [CrossRef]
28. C. Liu, R. Wang, Y. Shao, C. Chen, P. Wu, Y. Wei, and Y. Gao, “Detection of GDF11 by using a Ti3C2-MXene-based fiber SPR biosensor,” Opt. Express 29(22), 36598–36607 (2021). [CrossRef]
29. D. Tan, C. Jiang, N. Sun, J. Huang, Z. Zhang, Q. Zhang, J. Bu, S. Bi, Q. Guo, and J. Song, “Piezoelectricity in monolayer MXene for nanogenerators and piezotronics,” Nano Energy 90, 106528 (2021). [CrossRef]
30. W. Li, Y. Miao, T. Guo, K. Zhang, and J. Yao, “Nb2CTx MXene-tilted fiber Bragg grating optofluidic system based on photothermal spectroscopy for pesticide detection,” Biomed. Opt. Express 12(11), 7051–7063 (2021). [CrossRef]
31. K. Rizwan, A. Rahdar, M. Bilal, and H. M. N. Iqbal, “MXene-based electrochemical and biosensing platforms to detect toxic elements and pesticides pollutants from environmental matrices,” Chemosphere 291, 132820 (2022). [CrossRef]
32. J. Yin, S. Pan, X. Guo, Y. Gao, D. Zhu, Q. Yang, J. Gao, C. Zhang, and Y. Chen, “Nb2C MXene-Functionalized Scaffolds Enables Osteosarcoma Phototherapy and Angiogenesis/Osteogenesis of Bone Defects,” Nano-Micro Lett. 13(1), 30 (2021). [CrossRef]
33. N. Arif, S. Gul, M. Sohail, S. Rizwan, and M. Iqbal, “Synthesis and characterization of layered Nb2C MXene/ZnS nanocomposites for highly selective electrochemical sensing of dopamine,” Ceram. Int. 47(2), 2388–2396 (2021). [CrossRef]
34. R. Jha, J. Villatoro, G. Badenes, and V. Pruneri, “Refractometry based on a photonic crystal fiber interferometer,” Opt. Lett. 34(5), 617–619 (2009). [CrossRef]
35. A. J. Haes and R. P. Van Duyne, “A Nanoscale Optical Biosensor: Sensitivity and Selectivity of an Approach Based on the Localized Surface Plasmon Resonance Spectroscopy of Triangular Silver Nanoparticles,” J. Am. Chem. Soc. 124(35), 10596–10604 (2002). [CrossRef]
36. G. Zhu, Y. Wang, Z. Wang, R. Singh, C. Marques, Q. Wu, B. K. Kaushik, R. Jha, B. Zhang, and S. Kumar, “Localized Plasmon-Based Multicore Fiber Biosensor for Acetylcholine Detection,” IEEE Trans. Instrum. Meas. 71, 1–9 (2022). [CrossRef]
37. N. Agrawal, C. Saha, C. Kumar, R. Singh, and S. Kumar, “Detection of L-Cysteine Using Silver Nanoparticles and Graphene Oxide Immobilized Tapered SMS Optical Fiber Structure,” IEEE Sens. J. 20(19), 11372–11379 (2020). [CrossRef]
38. J. Turkevich, P. C. Stevenson, and J. Hillier, “A study of the nucleation and growth processes in the synthesis of colloidal gold,” Discuss. Faraday Soc. 11, 55–75 (1951). [CrossRef]
39. R. P. Pandey, P. A. Rasheed, T. Gomez, K. Rasool, J. Ponraj, K. Prenger, M. Naguib, and K. A. Mahmoud, “Effect of Sheet Size and Atomic Structure on the Antibacterial Activity of Nb-MXene Nanosheets,” ACS Appl. Nano Mater. 3(11), 11372–11382 (2020). [CrossRef]
40. V. Kumar, S. Hebbar, R. Kalam, S. Panwar, S. Prasad, S. S. Srikanta, P. R. Krishnaswamy, and N. Bhat, “Creatinine-Iron Complex and Its Use in Electrochemical Measurement of Urine Creatinine,” IEEE Sens. J. 18(2), 830–836 (2018). [CrossRef]
41. Y. He, X. Zhang, and H. Yu, “Gold nanoparticles-based colorimetric and visual creatinine assay,” Microchim. Acta 182(11-12), 2037–2043 (2015). [CrossRef]
42. J. Sittiwong and F. Unob, “Detection of urinary creatinine using gold nanoparticles after solid phase extraction,” Spectrochim. Acta, Part A 138, 381–386 (2015). [CrossRef]
43. S. Sharma, A. M. Shrivastav, and B. D. Gupta, “Lossy Mode Resonance Based Fiber Optic Creatinine Sensor Fabricated Using Molecular Imprinting Over Nanocomposite of MoS2/SnO2,” IEEE Sens. J. 20(8), 4251–4259 (2020). [CrossRef]
