S. typhimurium is among the agents responsible for causing gastrointestinal infections such as salmonellosis, with clinical manifestations including diarrhea (watery or greenish stools), nausea, vomiting, abdominal pain, headache, and mild fever, appearing within 6–48 h after exposure [1]. Infections caused by S. typhimurium bacteria have been responsible for more than 95 million cases and more than 50 000 deaths annually, resulting in costs of up to US$110 billion in lost productivity and medical expenses due to food safety deficiencies in low- and middle-income countries [2]. Pathogen outbreaks primarily result from the ingestion of contaminated food and beverages due to poor hygienic practices, and developing countries tend to have a higher incidence of these infections, according to epidemiological data from the World Health Organization (WHO) [2]. In México, a high prevalence of this pathogen has been documented, with diverse environmental persistence and more than 2600 serotypes, resulting in millions of infections annually [3].

Besides, antimicrobial resistance (AMR) is a global problem, and S. typhimurium is among the listed microorganisms of international concern due to its high adaptability to diverse environments and acquired resistance to fluoroquinolones and third- and fourth-generation antimicrobial agents [4]. Therefore, S. typhimurium has the potential to cause severe outbreaks, making infections difficult to treat; hence, rapid identification and appropriate treatment against the pathogen are vitally important. In this regard, fast and specific detection of bacteria is of great importance for ensuring food safety. Detection methods can be broadly categorized into four principal groups: phenotypic, molecular, immunological, and proteomic. Each category relies primarily on a specific analytical technique—bacterial culture, polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and high-performance liquid chromatography (HPLC), respectively. These methods have proven highly reliable for detecting and identifying various pathogens, thanks to their high specificity and sensitivity; however, they have certain limitations for widespread implementation, including long processing times, specialized equipment, and trained personnel. Likewise, conventional microbiological, microscopic, and immunological methods continue to play a crucial role in clinical practice for diagnosing microbial agents [5].

Recently, the development of emerging biosensor devices that can quickly detect diverse pathogens has provided several advantages, including miniaturization, point-of-care testing, and user-friendly management, without compromising selectivity and reproducibility. Recently, a new generation of biosensors has incorporated nanotechnology [6].

The most common nanomaterials used in biosensor development are those that provide consistent responses and high stability of immobilized bioreceptors, enabling detection of target analytes. Such materials are mainly based on gold [7–13], carbon [14–16], ZnO [17], TiO2 [18], silver, cadmium, platinum, and some other nanocomposites [19], as depicted in figure 1(a) and table 1. For ZnO-based materials, one-dimensional (1D) platforms can provide a higher surface-to-volume ratio, thereby minimizing nonspecific binding. Moreover, quantum confinement, which characterizes their dimensionality, can improve their optical signaling when integrated into biosensors [20, 21]. These advantages are further strengthened by the fact that ZnO is a highly compatible semiconductor due to the presence of surface hydroxyl (-OH) groups, which increase the number of binding sites for biomolecules through electrostatic interactions or covalent bonding [20, 22].

Figure 1 (a) General panorama of the most used nanomaterials for biosensor fabrication and (b) bioreceptors commonly employed for S. typhimurium biosensing. (c) Schematic representation of the proposed interaction between ZnO NWs and Salmonella cells.

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Table 1. Comparison of biosensor works aimed at detecting Salmonella bacteria.

SerotypeTransduction responseBiological recognition elementImmobilization MatrixLOD (CFU/mL)ReferenceS. typhimuriumElectrochemical CVAptamerCNTs1 × 101[14]Salmonella spp.Electrochemical DPVRBP 41 ProteinAuNPs0.029 × 101[7]S. typhimuriumFRETAptamerCDs3.5 × 101[15]S. typhimuriumMicrofluidic-Based electrochemical systemAntibodyAuNPs–HRP0.1 × 101[8]S. typhimuriumMicrofluidic Device ColorimetricAntibodyAu@PtNCs1.7 × 101[9]Salmonella spp.Photon Transduction SERSAntibodySilicon Nitride Surfaces1 × 101[26]Salmonella spp.SPRSingle-stranded DNASA chip1 × 102[27]S. enteritidisSPRAntibodyAu thin-film1 × 10⁵[10]S. typhimuriumSMSAntibodyOptical Fiber24.7 × 101[28]Salmonella spp.Optical PLAntibodyTiO2 NPs1 × 103[18]S. typhimuriumColorimetricAptamerAuNPs1.6 × 101[11]Salmonella spp.Colorimetric/AbsorbanceAntibodyAuNPs1 × 101[12]Salmonella spp.Colorimetric and FluorescenceAntibodySandwich (MNPs and ZnO capped mesoporous SiNPs loaded with curcumin)6.3 × 101[17]Salmonella spp.Optical FTIRAntibodyAmorphous silicon thin films1 × 102[29]S. typhimuriumOptical Photometric/ FluorometricAptamerQDs-COOH (carboxyl-modified CdSe/ZnS QDs)1 × 101[30]Salmonella spp.Optical Colorimetry detected by smartphoneAntibodyGold@platinum nanozymes5.6 × 101[13]S. typhimuriumOptical ColorimetricAntibodyAg NPs1.68 × 101[31]S. typhimuriumOptical FluorescenceAptamerCDs5 × 101[16]S. typhimuriumOptical FluorescenceAptamerAgNCs0.8 × 101[32]S. typhimuriumOptical FTIRAntibodyZnO NWs1 × 101This work

Abbreviations appear as follows: DPV: Differential Pulse Voltammetry, CNTs: Carbon Nanotubes, CV: Cyclic Voltammetry, NPs: Nanoparticles, FRET: Fluorescence Resonance Energy Transfer, SPR: Surface Plasmon Resonance, SMS: Single-mode/multimode/single-mode, PL: Photoluminescence, FTIR: Fourier Transform Infrared Spectroscopy, QDs: Quantum Dots, CDs: Carbon Dots, HRP: Horseradish Peroxidase, NCs: Nanocatalysts.

Additionally, ZnO exhibits intrinsic photoluminescent properties associated with its wurtzite crystal structure and the presence of surface defects (e.g., oxygen vacancies and zinc interstitials). These features enhance optical emission processes or spectral changes produced upon interaction with analytes [23, 24]. Finally, its synthetic versatility enables the fabrication of nanostructures with precise morphologies via accessible, controllable methods. Another significant advantage is its synthetic versatility. ZnO can be obtained via accessible, cost-effective methods such as hydrothermal, solvothermal, or vapor–liquid–solid (VLS) synthesis, the latter of which was employed in this work [25]. These synthesis methods enable precise control of nanostructure morphology—including nanowires, nanorods, and nanoplates—which modulates quantum confinement and can improve the device’s optical sensitivity by increasing surface area and enhancing light–matter interactions.

From a search in the Web of Science database, it is notorious an increasing interest in developing biosensing devices based on these transducer nanomaterials that can reach detection limits (LODs) as low as 101CFU ml−1 (table 1), where most of them have used antibodies as selective bioreceptors, followed by aptamers, DNA probes and enzymes, as shown in figure 1(b). As observed, biosensing devices using ZnO nanomaterials as an immobilization matrix are scarce for S. typhimurium detection. Therefore, this work proposes characterizing the interaction between ZnO nanowires (ZnO NWs) and S. typhimurium using polyclonal antibodies as bioreceptors to evaluate their biosensing performance over a 60 min timespan. Our study provides new insights into the interaction between ZnO nanowires and bacterial membrane components, emphasizing that detection was performed using bacterial lysates rather than viable cells, thereby ensuring biosafety and controlled surface interactions. The analysis of immobilization processes and the correlation between the optical signals obtained from bacterial membrane components and their specific interactions with functionalized ZnO nanostructures are key points of this work. Figure 1(c) illustrates a schematic of the stages involved in the development of the interaction proposed in this study. It begins with the synthesis of ZnO nanowires, followed by the functionalization and immobilization of the anti-Salmonella polyclonal antibody, and concludes with the interaction with the bacterial target.

1D ZnO Nanowire’s synthesis. The synthesis of 1D nanostructures was carried out using the VLS technique, based on the thermal evaporation of precursors to promote vapor-phase growth catalyzed by a metal over silicon substrates, as previously reported [33]. An aluminum-doped zinc oxide (AZO) thin film was employed as a seed layer and deposited by RF magnetron sputtering (Intercovamex H2 model). The Cressington 6002–8 system deposited a gold seed layer. For the growth process, a 1:1 mixture of ZnO and graphite was used as a precursor to produce ZnO via a carbothermal reaction. The gas flow rates (Ar, O₂) were controlled, and the growth was performed in a tubular furnace at 900 °C under a controlled pressure of 30 mbar for 30 min. Optical characterization of ZnO nanowires was performed using Fourier transform infrared spectroscopy (FTIR) on a Vertex 70 Bruker instrument and photoluminescence (PL) with a He-Cd laser at 325 nm as the excitation source. The structural analysis was performed using x-ray diffraction (XRD) and scanning electron microscopy (SEM).

S. typhimurium was used as a testing model (ATCC 14 028). The Sigma-Aldrich anti-S. typhimurium polyclonal antibody (4 mg/ml) was used as the principal stock.

Functionalization of ZnO NWs. Surface activation was performed by immersing the nanowire substrates in a 0.1 M KOH solution in methanol for 10 min, followed by a wash. Then, surface silanization was performed using a 1% (v/v) APTMS solution in acetone for 5 min. The substrates were rinsed and then subjected to a thermal treatment at 110 ± 5 °C for 80–100 min, generating free amino groups to promote antibody binding.

Antibody immobilization: A polyclonal anti-Salmonella antibody (0.1 mg ml−1, Sigma-Aldrich) was used. The substrates were incubated in the antibody solution at 4 °C for 1 h, followed by two washes with phosphate-buffered saline (PBS) to minimize nonspecific adsorption.

Interaction experiments for the detection of S. typhimurium. For bacterial activation and growth, 1 µl of bacterial stock was inoculated into 5 ml of LB broth and incubated at 37 °C for 24 h. After incubation, the culture was adjusted to an OD600 of 1 × 10⁸ CFU ml−1, then diluted to 1 × 101 CFU ml−1 and verified by plate counting. For interaction assays, the ZnO NW substrates were immersed in each prepared bacterial dilution for 60 min, followed by three washes with PBS containing 0.05% Tween 20 for 10 min each, and then stored in PBS buffer.

Characterization of the interaction between ZnO NWs and S. typhimurium. The chemical surface modification of ZnO NWs and the optical response after contact with bacteria were evaluated using FTIR Spectroscopy (ATR-FTIR) and electron microscopy (SEM), and the optimal conditions were determined for further preservation in PBS buffer. Moreover, the stability of ZnO NWs in PBS under selected conditions was evaluated after 1 year of storage and confirmed by microscopy.

3.1. Optical and structural characterization of ZnO NWs substrates

The VLS method was employed to synthesize ZnO nanowires, enabling the growth of unidimensional nanostructures with a vertically aligned morphology and a high surface-to-volume ratio, thereby enhancing interaction with the target analyte. According to the optical characterization, the FTIR spectrum shown in figure 2(a) corresponds to the ZnO NWs without surface modification. The principal signals were observed at 424 and 527 cm−1, corresponding to Zn–O E1(TO) and A1(LO) stretching vibrations, respectively. As reported by Salinas et al [34], these vibrations can appear within the 350–600 cm−1 range, depending on the crystal phase, material size, and morphology, as well as on the presence of defects in the crystal lattice [35]. Due to the characteristics of nanoplatforms and the sensitivity of this technique, a signal peak at 693 cm−1 is observed, attributable to the silicon substrate used to grow the nanostructures. SEM morphological characterization (figure 2(b)) revealed elongated, well-defined nanowires with homogeneous growth and vertical orientation. The average nanowire length was determined using Image J, yielding 4.4 µm. A higher growth temperature favored the diffusion and solubility of precursor species, thereby increasing the feed rate and droplet supersaturation, thereby promoting nanowire elongation. Combined with the 30 min deposition time, these conditions enabled the observed growth. Moreover, the low working pressure (30 mbar) contributed to the alignment of the nanostructures by facilitating directional vapor transport and reducing molecular collisions that could interfere with the process. The structural characterization of ZnO NWs was carried out by XRD (figure 2(c)), which showed the crystallographic planes of the ZnO wurtzite structure, in accordance with JCPDS card No. 36–1451 [36].

Figure 2 Characterization of the synthesized ZnO NWs. (a) Absorption peaks of ZnO obtained by FTIR spectroscopy, (b) microscopic visualization by SEM, (c) x-ray diffractograms showing the crystallographic planes of ZnO, and (d) normalized PL spectra measured at room temperature for ZnO nanoplatforms.

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Among these peaks, the (002) reflection exhibited the highest intensity, indicating a preferential orientation [37, 38], perpendicular to the substrate. This finding confirms that the synthesized nanostructures display vertical alignment, which is promoted by the anisotropic growth inherent to the VLS process and further enhanced by the sputter-deposited seed layer and the metallic catalyst, both of which contribute to alignment during nucleation [39]. Finally, a further evaluation of the ZnO NWs’ optical properties, as shown by PL spectroscopy (figure 2(d)), revealed a prominent emission at 529 nm, within the green region of the visible spectrum, corresponding to the deep level emission band (DLE). This signal is primarily associated with structural defects, which strongly modulate band intensity, thereby altering emission strength in this region [40–42].

3.2. Surface biofunctionalization of ZnO NWs for S. typhimurium detection

Biofunctionalization of ZnO NWs was undertaken to provide a compatible surface for bacterial cells, enabling detection under covalent attachment (figure 3). The green spectrum corresponds to the functionalization of ZnO NWs with—NH₂ groups via APTMS, which provide affinity for attaching the biorecognition element (antibodies). This functionalization treatment produced characteristic bands at 1030–1039 cm−1, corresponding to Si–O–Si vibrations arising from the condensation of hydroxyl groups present in siloxane. In addition, intense bands at 1604 cm−1 were observed, attributed to amino (–NH₂) groups. Free amino groups exposed to the surrounding medium interact with and bind the antibody’s terminal carboxyl groups [43]. The signal associated with OH- is attributed to surface hydroxylation, confirming the generation of these groups on the NW surface and allowing binding and the siloxane conformation.

Figure 3 FTIR spectroscopic analysis of bare ZnO NWs (dashed point black line), functionalization (green line), antibody immobilization (blue line), and bacterial detection (red line) using ZnO nanoplatforms. Peaks associated with functional groups detected from each spectrum are indicated with black arrows.

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Then, antibody immobilization was achieved after interaction with functionalized ZnO NW platforms (figure 3, blue line). The resulting spectrum exhibited characteristic vibrations associated with amide I and amide II bands at 1647 and 1528 cm−1, respectively, which indicate the presence of the peptide backbone. Amide I is attributed to C = O stretching, whereas amide II corresponds to N–H bending and C–H stretching within the peptide bond [44].

Additionally, the band at 563 cm−1 corresponds to disulphide bridges present in lysine residues within the hinge region of antibodies, confirming the appropriate interaction of the antibody with the nanostructured surface of ZnO NWs. It is important to note that the contributions of OH groups from immobilized proteins are high and consistent, and this signal distribution differs from that observed in the hydroxylation process, providing proper confirmation of antibody viability for further bacterial detection. Regarding bacterial detection (red line), the most intense band appeared at 1022 cm−1, attributed to phosphate-group vibrations from polysaccharide rings and phospholipids in the bacterial membrane [45].

These results demonstrate that the assembled nanoplatform directly interacts with Salmonella’s outer membrane. On the other hand, the amide I and II regions (1635–1650 cm−1 and 1530–1560 cm−1, respectively) showed decreased band intensities due to bacterial interaction, reflecting antigen–antibody binding to S. typhimurium cells [46, 47]. The following section examines the optical response by FTIR spectroscopy after detecting bacteria at increasing concentrations.

3.3. Optical response of the interaction between ZnO NWs and S. typhimurium at different concentrations

To corroborate the interaction of ZnO nanoplatforms for S. typhimurium detection, we performed contact tests at different bacterial concentrations (1 × 101, 1 × 10⁴, 1 × 106, and 1 × 10⁸ CFU ml−1) to assess specific differences in FTIR signals and to verify the device’s sensitivity. The FTIR spectra obtained at different concentrations are shown in figure 4(a), which provides a detailed analysis and comparison of the most relevant spectral peaks.

Figure 4 FTIR spectroscopic analysis for the detection of S. typhimurium at concentrations between 1 × 101 and 1 × 108 CFU ml−1. (a) Full spectra (4000–400 cm−1), (b) zoom view between 800 and 1300 cm−1, (c) zoom view between 1100 and 2200 cm−1, and (d) zoom view between 2700 and 3700 cm−1.

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Bands corresponding to the bacterial membrane were identified, as mentioned in the previous section, such as the peaks related to the presence of P=O and P–O–C bonds between 1020 and 1040 cm−1 range, as well as carbonyl groups associated with phospholipids and lipopolysaccharides [48], as shown in figure 4(b).

Indeed, a progressive increase in peak intensity of P–O absorption bands was observed with increasing bacterial concentrations (1 × 101, 1 × 10⁴, 1 × 106 CFU ml−1), which was corroborated by statistical analysis by one-way analysis of variance (ANOVA) (figure S1, supplementary material). This is consistent with studies reporting spectral variations in ATR-FTIR spectra in response to changes in bacterial concentration [49, 50]. However, at a high titer (1 × 10⁸ CFU ml−1), a lower peak signal was observed, attributed to a change in the distribution of FTIR spectral signals. Since the detection experiments were followed by PBS/Tween 20 washes to inactivate bacteria, this implies that the interaction primarily involves the cellular membranes of lysed bacteria. This accounts for spectral variations, with diminished peak intensity in some regions (e.g., 1031 cm−1) and increased contributions in others. Another characteristic signal observed in the spectra compared is at 1541 cm−1 and 1649 cm−1, corresponding to the Amide II and Amide I peaks, respectively (figure 4(c)). These are attributed to peptide bonds in the bacterial membrane, including porins, integral membrane proteins, and other structural proteins that confer rigidity to the bacterial envelope. The presence of these signals is consistent with contributions from antibodies and bacterial membranes, suggesting constructive interference.

Finally, peaks attributed to bacterial membrane components, such as polysaccharides and glycoproteins, are observed in the 3200–3700 cm−1 range. Figure 4(d) shows a clear differentiation between spectral curves corresponding to each bacterial concentration. This behavior is particularly relevant because it allows a direct correlation between band intensity and bacterial concentration to be established. A similar trend was observed in the band corresponding to C-H stretching vibrations, attributed to lipids and fatty acids in the bacterial plasma membrane [51]. Additionally, to verify the selectivity of the biofunctionalized nanoplatform, we evaluated interactions between antibodies against Salmonella spp. (Gram-negative) and Enterococcus faecalis (Gram-positive), used as positive and negative controls, respectively. The aim was to determine whether any signal associated with nonspecific binding was present. The spectra obtained are shown in figure S2. In the spectrum corresponding to Salmonella spp. (green line), characteristic bands of bacterial membrane components are observed, mainly associated with phosphate groups (P=O, C=O, P–O–C), which are typical of lipopolysaccharides (LPS), key constituents of the lipid bilayer of gram-negative bacteria. This confirms a specific interaction between the employed antibody and epitopes on the bacterial surface of Salmonella spp., a finding also verified by ELISA against other pathogens, including Shigella flexneri, S. aureus, and Bacillus thuringiensis (figure S2, supplementary material).

It is noteworthy that the changes observed in this region include the breakage of bacterial membranes, which exposes the hydrophobic tails of phospholipids, thereby increasing the signal of the C–H stretching vibrations at higher concentrations. This contribution shifts the overall spectrum toward a decrease in the P–O signal from the hydrophilic head of phospholipids. In the case of the signal corresponding to O–H bonding, the conformation and increment of the peak are directly related to the sum of the signal contributions. Constructive interference among functional groups, membrane proteins, and cholesterol at high concentrations affects the definition and breadth of the peaks. From these results, we can establish a direct correlation between the signals and the bacterial concentration. These findings confirm bacterial cell adhesion to the immune-functionalized nanowire surface. Nevertheless, the variations observed in the phosphate and polysaccharide regions demonstrate the potential of the FTIR-based biosensor to distinguish between different bacterial concentrations, thereby consolidating its applicability for the detection and characterization of microorganisms.

3.4. Microscopic identification of S. typhimurium detection

Various strategies are used for the identification and validation of biosensor devices; in this work, microscopic verification was employed to assess target detection. Figure 5 shows a series of SEM micrographs (a–f) that demonstrate the detection of S. typhimurium at 1 × 104 CFU ml−1 and the presence of a bacterial membrane over biofunctionalized ZnO NWs. These micrographs illustrate the antigen–antibody interaction after 60 min of exposure, highlighting how the bioreceptors retain affinity for bacterial membranes despite bacterial lysis. The polyclonal antibodies used can recognize multiple membrane epitopes, enabling the device to detect both viable and non-viable bacteria.

Figure 5 Visualization of S. typhimurium membranes attachment after 60 min of interaction with biofunctionalized 1D ZnO NWs by SEM microscopy, secondary electrons. (a) 250x scale bar 100 μm, (b) 1000x scale bar 10 μm, (c) 2500x scale bar 10 μm, (d-e) 5000x scale bar 5 μm, and (f) 10000x scale bar 1 μm.

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As presented in figure 2(b), the ZnO nanoplatforms were densely distributed, forming a three-dimensional network. This morphology provides biosensing platforms with a high specific surface area and a rough surface that promotes bacterial membrane adhesion.

Figure 5 shows the interaction of lysed bacteria with the bioselective layer of ZnO NWs. Because the contact time was prolonged to 60 min, membranes can cover a considerable surface area of nanoplatforms (figure 5(a)), which contrasts with the sharp morphology of nanowires and reveals an irregular array of elongated, amorphous structures. Close-up images at 1000x and 2500x (figures 5(b) and (c)) show the specific distribution of bacterial membranes, despite the low cell concentration and the use of lysis buffer. At this point, antibodies immobilized on the ZnO nanowires selectively capture S. typhimurium via antigen–antibody interactions. In addition, nonspecific interactions—such as hydrogen bonding, electrostatic forces, and van der Waals forces—are established between the charged ZnO surface and bacterial membrane components [52, 53]. The same observation is observed at 5000x and 10000x (figures 5(d)–(f)), providing a detailed view of these membranes and confirming that the management protocol for Salmonella typhi is adequate and that our nanoplatforms can detect non-viable bacteria. Moreover, it is essential to recognize that biosensor devices must detect intact bacterial cells as well as their components, given the heterogeneity of complex matrices in food, clinical, and ecological environments. Overall, these results confirm that the interaction between Salmonella typhimurium and ZnO nanowires leads to effective membrane attachment. This not only confirms the suitability of biosensing platforms for rapid bacterial detection but also underscores the stability of ZnO nanowires synthesized through physical methods such as VLS. The high isoelectric point of ZnO nanostructures has been documented as a desirable property for achieving stability and suitable interactions with different biomolecules [54, 55].

3.5. Evaluation of the storage conditions and preservation of ZnO biosensing nanoplatforms

From a biosensor perspective, the storage stability of biosensing platforms under normal conditions is of great importance, as it must ensure that activity is maintained over time [56, 57]. Therefore, the present work also included an experimental evaluation of different concentrations of PBS buffer for preserving bioreceptors, specifically the immobilization of antibodies on ZnO NWs, for future reference. In this context, three concentrations of bacterial detection were evaluated after the platforms were conserved in PBS buffer 0.01 M (100 mM), prepared from the commercial stock, corresponding to 10,000 times the previously used concentration (>10 μM). Figures 6(a) and (b) show a detection of 1 × 102 CFU/mL, where the micrographs were colored on purpose for easy differentiation of buffer remnants.

Figure 6 Visualization of bacterial detection after contact with ZnO nanoplatforms preserved at 100 mM PBS buffer. (a) and (b) 1 × 102 CFU ml−1, (c) and (d) 1 × 104 CFU ml−1, and (e) and (f) 1 × 106 CFU ml−1. SEM images were edited and colorized using GIMP software to enhance contrast and highlight assembly components and PBS buffer storage conditions of ZnO biosensing nanoplatforms.

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These micrographs illustrate how the buffer salts in PBS can promote the formation and spread of salt crystals over the ZnO nanowires, completely covering the nanostructured samples.

The same behavior is presented in figures 6(c) and (d) for the detection of 1 × 104 CFU ml−1, where a total coverage of NWs by the remnants of PBS buffer and cellular debris (marked in green) was observed, where the high concentration of phosphate produces salt nucleation over nanowires, resulting in visible salt crystals (purple).

Similarly, after detection of 1 × 106 CFU ml−1 by ZnO NWs preserved at 0.01 M PBS figures 6(e) and (f), it was observed that the coverage of salts over the surface of NWs (blue), but after contact with high bacterial concentrations, a detection of bacterial cells attached over the surface (green). This coverage reduces both the optical response and the capacity for bacterial component attachment. Such interferences compromise the integrity of the nanostructures, indicating that at the nanoscale, the concentration of preservation solutions is critically important. In particular, the salt concentration can induce nucleation on the nanostructures, thereby influencing subsequent detection processes. Furthermore, although a buffer is necessary for preserving the bioreceptor, it is essential to establish the operating conditions for nanostructured devices. Micrographs 6 (e) and (f) confirm the presence of bacterial material despite the interference of nucleating salts in the samples.

This shows that the biofunctionalization strategy is effective for detecting S. typhimurium. However, the preservation solution for nanobiosensors must be evaluated, as prolonged storage can interfere with the underlying nanostructure. Finally, based on the experiments conducted, it was concluded that the required buffer concentration should be less than 10 μM to preserve multiple devices that can be produced on an industrial scale. As shown in figures 7(a)–(d), we evaluated the stability of biofunctionalized ZnO NWs after contact with 1 × 104 CFU ml−1, and after one year of preservation in 10 μM PBS buffer, where it can be distinguished that the bacterial membranes are attached to the nanowires, which are preserved and rightly observed without visible interferences.

Figure 7 Visualization of bacterial detection after contacts between 1 × 104 CFU ml−1 and ZnO biofunctionalized nanoplatforms preserved at 10 μM PBS buffer after one year. (a) 2500x scale bar 10 μm backscattered electrons, (b) 5000x scale bar 1 μm secondary electrons, (c) 5000x scale bar 1 μm backscattered electrons, and (d) 2500x scale bar 10 μm backscattered electrons.

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An elemental mapping by energy dispersive x-ray spectroscopy (EDS) was performed for the biosensing nanoplatform shown in figure 7(d), as well as a replicate sample that had been stored for one year (figure S3, supplementary material). This analysis confirmed the presence of the characteristic elements of bacterial cells (carbon and phosphate), nitrogen from biofunctionalized nanowires (from antibodies), silicon (from APTMS), and Zn and O from the nanoplatforms, indicating bacterial attachment and the intact morphologies of ZnO nanowires preserved in buffer solution.

This comparison demonstrates the importance of selecting appropriate buffer concentration and preservation conditions, highlighting that nanowires remain stable after 1 year and that the absence of salt nucleation preserves their detection performance. This would provide stable optical responses for targeted biosensing and enhance interactions with biomolecules or analytes, enabling specific detection over time. Therefore, a comprehensive study should address critical aspects, including device preservation and long-term storage. Although this topic has received limited attention in the literature, the present work contributes valuable insights by examining the interaction between PBS buffer solutions and ZnO nanostructures. The findings highlight the importance of carefully optimizing buffer concentration to minimize unwanted nucleation processes and maintain both detection efficiency and structural stability over extended storage periods. Such considerations are essential for developing next-generation nanobiosensors with reliable, long-term performance.

Moreover, nanoscale stability is a key consideration for biosensor development, and the analysis of bioreceptor coupling to ZnO nanostructures remains incompletely understood. As shown in table 1, various authors have focused on different parameters for achieving accurate biodetection, including nanomaterial configurations, bioreceptors, detection times, and transduction systems. For instance, different nanomaterials/nanoplatforms have been developed for the electrochemical and optical detection of Salmonella. Although most of them can offer sophisticated systems for accurate detection, they are commonly based on carbon nanotubes, carbon dots, gold nanoparticles (Au NPs), platinum and s