Surface plasmon resonance biosensors: Advancements, applications, and future directions in molecular detection

Abstract

Surface Plasmon Resonance (SPR) biosensors have emerged as one of the most potent and adaptable methods for detecting molecular interactions in real-time and without labeling. SPR enables the accurate monitoring of biomolecular interactions in various contexts by detecting changes in refractive index near the sensor's surface. Over the last few decades, SPR technology has advanced dramatically, with improvements in sensor sensitivity, resolution, and throughput.

This study provides a comprehensive overview of SPR biosensors, highlighting recent advances in sensor technology, materials, and detection methodologies. We cover the fundamentals of SPR sensing and the factors that influence sensor performance, including metal selection, surface functionalization, and immobilization techniques. The report also looks at the wide range of applications for SPR biosensors, including drug development and illness diagnostics, as well as environmental monitoring and food safety.

The promise of SPR technology is further investigated by studying new advancements such as the incorporation of nanomaterials, microfluidics, and multi-analyte detection systems. We also explore the SPR biosensor's future directions, including existing limits and potential applications in customized medicine, point-of-care diagnostics, and quick environmental monitoring.

This review seeks to give a comprehensive overview of SPR biosensors, highlighting their potential to revolutionize molecular detection in a variety of disciplines, as well as outlining the obstacles and possibilities that lie ahead for their future development.

1. Introduction

One of the most developed and extensively used systems for label-free, real-time molecular interaction research is Surface Plasmon Resonance (SPR) biosensors. SPR allows biomolecular binding events to be directly transduced into detectable optical signals through local refractive index changes by taking advantage of the stimulation of surface plasmons at a metal–dielectric interface. SPR biosensors have become essential instruments in biotechnology, pharmaceutical research, and clinical diagnostics because of their high sensitivity, kinetic resolution, and compatibility with a wide range of analytes, from tiny molecules to huge biomolecular assemblies.^1,2

The field has advanced significantly from its initial prism-based configurations since the first commercial SPR instruments were introduced in the early 1980s. Advanced SPR sensor architectures that address sensitivity, miniaturization, and system integration constraints have emerged in recent years. Significantly, waveguide-coupled platforms and photonic crystal fiber-based SPR sensors have shown greater electromagnetic field confinement and longer interaction lengths, allowing for better detection limits and adaptability for small and portable sensing devices. The design space has been further broadened by hybrid metal–dielectric multilayer designs and improved plasmonic interfaces, which enable precise control over performance metrics and resonance characteristics. Blood component analysis, cancer biomarker identification, viral sensing, and environmental pollution monitoring are just a few of the increasingly complex applications that have resulted from these advancements, demonstrating a move away from laboratory-based measurements and toward SPR systems that are application-oriented (such as recent models described in Biosensors, Micromachines, Photonics, and Results in Optics).^30,31

Despite these noteworthy developments, a careful analysis of recent reviews from 2024 to 2025 reveals that a considerable portion of the current body of work remains primarily descriptive or compartmentalized. Without offering a comprehensive analysis of how sensor architecture, metal film properties, surface immobilization chemistry, and microfluidic integration collectively affect sensitivity, stability, and reproducibility, many reviews concentrate on discrete elements such as materials, optical configurations, or particular application domains. Furthermore, whereas microfluidics and nanomaterials are typically touted as enabling technologies for next-generation SPR sensors, the difficulties they present, such as biofouling, regeneration constraints, flow-induced instability, and manufacturing reproducibility, are generally overlooked. Consequently, there is a lack of systematic attention to the trade-offs that ultimately determine real-world deployability.^32,33

Simultaneously, the limitations of traditional model-based analysis have been revealed by the growing complexity of SPR datasets, especially in low-signal or data-limited circumstances characteristic of clinical and point-of-care applications. Despite their increasing significance for obtaining kinetic and diagnostic information from noisy or sparse measurements, artificial intelligence and data-driven approaches for SPR signal interpretation have not yet been adequately incorporated into the majority of current reviews. A fundamental synthesis that connects physical sensing concepts, biochemical interaction analysis, system-level design considerations, and new computational techniques is therefore obviously needed.

Inspired by these gaps, the current study offers a thorough and integrated examination of SPR biosensor technologies, focusing on new sensor models, cutting-edge designs, and developing applications. This paper critically analyzes the theoretical underpinnings of SPR sensing, quantitative performance measures, kinetic modeling capabilities, and the practical constraints related to nanomaterials, microfluidics, and multiplexed detection, in addition to reviewing technological advancements. This review aims to illuminate the current state of the field and offer practical pathways toward developing reliable, repeatable, and application-ready SPR biosensing platforms by systematically integrating optical principles, surface chemistry, device architecture, and data processing techniques.

2. Literature search strategy

In order to find current and pertinent research on surface plasmon resonance (SPR) biosensors, this review was carried out using an organized literature search. Web of Science, Scopus, PubMed, and IEEE were the main databases consulted. Keywords like “surface plasmon resonance,” “SPR biosensor,” “plasmonic sensing,” “SPR nanomaterials,” “microfluidic SPR,” and “SPR applications” were incorporated in search queries. In order to catch recent technological advancements, the search was limited to peer-reviewed journal publications published mostly between 2019 and 2025. Research that dealt with SPR sensor design, materials, detecting processes, performance measurements, or real-world applications was included. Articles with insufficient methodological detail, conference abstracts, and non-peer-reviewed sources were not included. Citation tracking of important review and research articles was used to find more pertinent references.

3. Principles of SPR sensing

Surface Plasmon Resonance (SPR) is an optical phenomenon that happens when polarized light strikes a metal-dielectric contact at a precise angle, causing free electrons on the metal surface to oscillate in time with the light. These oscillations, known as plasmons, travel over the metal's surface and are extremely sensitive to changes in refractive index near the surface. This feature serves as the foundation for SPR-based biosensors, which monitor molecular interactions in real-time by detecting changes in the resonance angle or the intensity of reflected light.^34,35

3.1. Strategies for improving sensitivity, resolution, and analytical performance of SPR sensors

Numerous strategies have been proposed to enhance the resolution and analytical performance of surface plasmon resonance (SPR) sensors, most notably through material engineering, optical configuration optimization, and multiplexed detection schemes. While these approaches consistently report improvements in sensitivity and limit of detection compared to conventional prism-coupled gold film SPR (Kretschmann configuration), a critical analysis reveals that performance gains are often accompanied by non-negligible trade-offs in terms of stability, reproducibility, and system complexity.^3,36

Material-based enhancement strategies, particularly those involving nanomaterials and two-dimensional (2D) materials, primarily aim to amplify the local electromagnetic field at the sensing interface. For instance, graphene oxide decorated with silver nanoparticles has been shown to significantly enhance sensitivity (up to 890 nm/RIU) and achieve detection limits as low as 10⁻⁹ M, representing an improvement of approximately two orders of magnitude compared to conventional SPR sensors. Similarly, MoS₂-based SPR configurations exploit strong light–matter interactions at the metal–2D material interface, yielding sensitivities on the order of 431 nm/RIU with comparable detection limits. Metal–insulator–metal (MIM) waveguide-coupled SPR sensors further push sensitivity beyond 1000 nm/RIU by confining the evanescent field within the waveguide layer.^37,38

However, these sensitivity enhancements do not necessarily translate into proportional improvements in overall sensing performance. Nanoparticle- and 2D-material-based platforms often suffer from resonance broadening, which can degrade the Figure of Merit (FOM) despite high sensitivity values. In addition, silver-based nanostructures are prone to oxidation and chemical instability, while multilayer and waveguide-coupled designs increase fabrication complexity and may compromise reproducibility across devices. In contrast, conventional prism-based SPR systems, although less sensitive, offer superior long-term stability, narrow resonance linewidths, and well-established surface chemistry, making them more reliable for quantitative kinetic measurements and routine applications.^39,40

Beyond material selection, optimization of the optical interrogation conditions—particularly the angle of incidence—has been shown to significantly improve resolution and detection limits for specific biomolecular targets. Angle-optimized SPR sensors have achieved picogram-per-milliliter detection limits for clinically relevant biomarkers such as interleukin-6, prostate-specific antigen, and streptavidin. While this approach preserves the simplicity of prism-based configurations, its effectiveness is often target-specific and requires precise optical alignment, limiting its robustness in portable or high-throughput settings.^41,42

Multichannel and array-based SPR architectures address a different performance dimension by enhancing throughput and enabling simultaneous multi-analyte detection. Reported systems achieve detection limits in the low picogram-per-milliliter to picomolar range for cancer biomarkers and nucleic acid hybridization assays. Nevertheless, multiplexing introduces additional challenges related to cross-talk, fluidic complexity, and data interpretation, which can affect reproducibility and quantitative accuracy.^43,44

Overall, these studies highlight that no single SPR configuration universally outperforms others across all performance metrics. Instead, SPR sensor design inherently involves trade-offs between sensitivity, resolution, stability, fabrication complexity, and operational robustness. High-sensitivity nanomaterial-enhanced platforms are well-suited for ultra-trace detection, whereas conventional prism-based systems remain preferable for applications requiring high reproducibility and reliable kinetic analysis. Multichannel configurations further extend SPR capabilities toward high-throughput diagnostics but demand careful system-level optimization to mitigate interference and complexity4, 45 (Table 1).

Table 1.

Comparative performance metrics, advantages, and limitations of representative SPR sensor architectures.^3,4

SPR architecture	Typical sensitivity	Limit of detection	Key advantages	Main limitations
Prism-based SPR (Au film, Kretschmann)	∼100–300 nm/RIU	∼10−6 RIU	High stability, narrow resonance, reproducible kinetic analysis	Bulky optical setup, moderate sensitivity
Ag NP/GO-enhanced SPR	Up to ∼890 nm/RIU	∼10−9 M	Strong electromagnetic field enhancement, ultra-low LOD	Nanoparticle oxidation, resonance broadening, reduced long-term stability
MoS2-based SPR	∼431 nm/RIU	∼10−9 M	Strong light–matter interaction, compact sensor designs	Fabrication complexity, moderate FOM
MIM waveguide-coupled SPR	>1000 nm/RIU	∼10−7 RIU	Extreme sensitivity, strong optical confinement	Complex fabrication, high alignment sensitivity
Angle-optimized prism SPR	Target-dependent	Typically pg/mL range	Simple architecture, improved detection limits	Limited robustness, critical angular alignment
Multichannel SPR arrays	Moderate–high	pg/mL–pM	High throughput, multiplexed detection	Cross-talk, increased fluidic and data-processing complexity

Sensitivity and limit of detection values are presented as indicated in the cited research and may thus be expressed in different units depending on the analyte and sensor configuration.

3.2. Fundamental principle

At the heart of SPR sensing is the interplay of light and surface plasmons. When light strikes a metal surface, such as gold or silver, at a specific angle (the resonance angle), the energy is connected to surface plasmons, causing a decrease in reflected light intensity. The position of this dip, known as the SPR angle, is dependent on the refractive index of the medium near the surface. When molecules, such as biomolecules, connect to the sensor's surface, the local refractive index changes, causing the SPR angle to move. This shift may be observed in real time and provides useful information about the analyte's binding kinetics, concentration, and affinity.^46,47

The performance characteristics of an SPR device depend on several factors, including the design of the sensor chip, the instrumentation used, and the interrogation. Here are some common performance characteristics that are evaluated for SPR devices48, 49, 50:

3.2.1. Penetration depth (δ)

The penetration depth of the electromagnetic field into the metal is given by:

$$\delta =\frac{1}{Im\left(K_{SPP}\right)}$$ (1)

where $K_{SPP}$ is the complex wave number of surface plasmons.

3.2.2. Surface plasmon wave number (${\mathit{K}}_{\mathit{S}\mathit{P}\mathit{P}}$)

The surface plasmon wave number is given by:

$$k_{SPP}=k_0\sqrt{\frac{{\epsilon }_m{\epsilon }_d}{\left({\epsilon }_m+{\epsilon }_d\right)}}$$ (2)

• -$k_0$ Wave number in vacuum, defined as $k_0$ = $\frac{2\pi }{\lambda }$
• -${\epsilon }_m$: Complex permitivity of the metal
• -${\epsilon }_d$: Permittivity of the dielectric

3.2.3. Sensitivity

The sensitivity of an SPR device refers to its ability to detect changes in the refractive index of the sample. The sensitivity is affected by factors such as the thickness and material of the metal film, the quality of the surface chemistry used to functionalize the chip, and the instrumentation. It's an important performance criterion in biosensing applications because it determines the sensor's ability to detect small changes in the concentration of an analyte. For instance, in breast cancer diagnosis, high sensitivity is required because the concentrations of the cancer markers in the blood are generally very low.

The ratio of the change in sensor output (wavelength, intensity, phase, etc) to the change in the quantity being measured (analyte concentration, refractive index, etc) is defined as the sensitivity of sensors. For instance, the sensitivity of a refractive index-based SPR sensor is given by:

$$S=\frac{\Delta \theta }{\Delta n}$$ (3)

Where Δθ is the change in resonant angle, and Δn is the change in refractive index.

3.2.4. Resolution (minimum detectable RI change)

3.2.4.1. Angular sensitivity

$$S_{\theta }=\frac{\Delta {\theta }_{\text{res}}}{\Delta n}$$ (4)

where Δn is the index variation of the medium near the surface.

3.2.4.2. Spectral sensitivity (if working in wavelength mode)

$$S_{\lambda }=\frac{\Delta {\lambda }_{\text{res}}}{\Delta n}$$ (5)

3.2.4.3. Index resolution (limit related to instrumental resolution)

In general, the smallest detectable index variation is:

$$\Delta n_{\min }=\frac{\Delta {\theta }_{\min }}{S_{\theta }}$$ (6)

or in spectral mode:

$$\Delta n_{\min }=\frac{\Delta {\lambda }_{\min }}{S_{\lambda }}$$ (7)

where:

• •Δθmin: smallest measurable angle variation (instrument),
• •Δλmin: smallest resolvable wavelength variation (spectrometer).

3.2.5. Figure of merit

The Figure of Merit (FoM) is a key metric for evaluating the performance of SPR devices, particularly their ability to detect small variations in refractive index.

$$\text{FoM}=\frac{S}{\text{FWHM}}$$ (8)

where S is the sensitivity of the sensor to the refractive index change, and $\text{FWHM}$ is the width of the resonance curve at half maximum. A high

FoM means that the SPR sensor has a good resolution and can detect very subtle variations in refractive index, which is crucial for high-precision biosensing applications.

3.2.6. Quality factor (QF):V

The Quality Factor (QF) is another performance indicator for SPR devices, linked to the width of the plasmon resonance.

$$\text{QF}=\frac{{\theta }_{RES}}{FWHM}$$ (9)

${\theta }_{RES}$ is the resonance angle. FWHM(θ) is the Full Width at Half Maximum of the resonance curve. A high-quality factor indicates sharper resonance.

3.2.7. Limit of detection

The limit of detection (LOD) is the smallest amount of measurement agent that a sensor is capable of detecting. This value is influenced by both the sensor and the system noise.

$$\text{LOD}=\frac{3\mathrm{\sigma }}{\mathrm{S}}$$ (10)

A lower LOD is desired for biosensors because it increases their ability to detect low concentrations of analytes.

This value is influenced by both the sensor and the system noise.

3.3. SPR sensor design

A typical SPR sensor consists of the following components51, 52, 53, 54:

• •Metal Layer: The most commonly used metals are gold and silver, which are chosen for their ability to support surface plasmon waves effectively. The metal layer is usually deposited on a glass prism or sensor chip.
• •Light Source: A monochromatic light source, often a laser, is directed at the sensor surface at various angles.
• •Prism: The light is passed through a prism, which helps direct it onto the sensor surface. The prism is crucial for achieving the proper angle of incidence to induce SPR.
• •Detector: A photodetector or a charge-coupled device (CCD) is used to measure the intensity of the reflected light. The change in reflection intensity at the SPR angle provides the data for detecting molecular interactions.

3.4. Measurement and detection

The interaction of light with surface plasmons causes a shift in the SPR angle, which may be observed by measuring the intensity of reflected light at different incidence angles. The shift in resonance angle and the concentration of the binding molecules have a strongly linear relationship, making SPR a quantitative approach. Biomolecules and other analytes bond to the surface, increasing the local refractive index and shifting the SPR angle. This shift enables real-time observations of biomolecular interactions, such as association and dissociation rates55, 56, 57, 58 (see Fig. 1).

Fig. 1.

Principle of surface plasmon resonance (SPR).²⁷

3.5. Principles of kinetic modeling in SPR biosensors

Beyond equilibrium affinity measurements, surface plasmon resonance biosensors enable real-time kinetic characterization of biomolecular interactions by monitoring the temporal evolution of the binding response. In SPR kinetic assays, the interaction between an immobilized ligand $\left(L\right)$ and a soluble analyte $\left(A\right)$ is most commonly described using a reversible 1:1 Langmuir binding model28, 29, 59, 60, 61, 62, 63, 64:

$$L+A\underset{k_d}{\overset{k_a}{\rightleftharpoons }}LA$$ (11)

where $k_a$ ($M^{-1}$· $s^{-1}$) represents the association rate constant and $k_d$ ($s^{-1}$) the dissociation rate constant. The change in SPR response over time reflects the formation and dissociation of the ligand–analyte complex at the sensor surface and can be modeled by the differential rate equation:

$$\frac{d\left[LA\right]}{dt}=k_a\left[L\right]\left[A\right]-k_d\left[LA\right]$$ (12)

During the association phase, when analyte is continuously injected at a constant concentration $C$, the SPR response follows an exponential rise toward equilibrium:

$$R\left(t\right)=R_{\max }\left(1-e^{-\left(k_{a}C+k_d\right)t}\right)$$ (13)

where $R_{\max }$ corresponds to the maximum binding capacity of the immobilized ligand layer. Upon replacement of the analyte solution with running buffer, the dissociation phase is governed by a simple exponential decay:

$$R\left(t\right)=R_0{e}^{-k_{d}t}$$ (14)

allowing direct extraction of the dissociation rate constant $k_d$. The equilibrium dissociation constant $K_D$, which quantifies binding affinity, is obtained from the ratio $K_D=k_d/k_a$.

The time evolution of the binding response is represented by a typical SPR sensorgram, which can be separated into discrete phases that accurately depict the underlying biochemical and physical processes at the sensor surface. In the baseline phase, equilibrium conditions at the metal-dielectric interface are reflected by the establishment of a stable signal under continuous buffer flow. Analyte molecules bind to immobilized ligands during the association phase, which is started by the introduction of the analyte and results in a rise in response proportionate to the rate of complex formation controlled by the association constant $k_a$. When the rates of binding and unbinding are equal, the equilibrium area represents a dynamic balance between association and dissociation processes. The dissociation phase is seen as an exponential signal decrease controlled by the dissociation rate constant $k_d$. When the analyte solution is substituted with buffer. In order to recover the surface for later measurement cycles, a regeneration procedure eliminates any remaining bound analyte. This sensorgram demonstrates how association ($k_a$), dissociation ($k_d$), and equilibrium ($K_D$) kinetic parameters can be extracted from binding curves using real-time SPR measurements (see Fig. 2).

Fig. 2.

(a) Biosensor chip architecture; (b) SPR sensorgram at different phases.²⁸

This SPR sensorgram serves as an example of real-time kinetic analysis of a biomolecular interaction. The baseline stabilization phase, analyte injection, and association phase, which is marked by an exponential increase in response, are all visible in the sensorgram. A balance between the processes of attachment and dissociation is shown in the equilibrium area. On the other hand, the dissociation phase shows an exponential decrease controlled by the dissociation rate constant $k_d$ after being replaced with an analyte-free buffer. Adapted from Chiu and Nurrohman (2024) (Fig. 3).

Fig. 3.

SPR sensorgram showing kinetic binding for ACE2 with (a) 2019-nCoV S, (b) SARS-CoV RBD-SD1.²⁹

Fig. 4: Real-time SPR sensorgrams and affinity profiles illustrating the binding interactions of multiple synthetic cannabinoid analytes. Panels (a–d) show the responses for JWH-018, AMB-4ENPICA, MAM-2201, and FDU-PB-22; panels (e–h) correspond to STS-135, 5F-MDMB-PINACA, 5F-AKB-48, and AB-CHMINACA; and panels (i–j) depict MDMB-4en-PINACA and FUB-AKB-48. Sensorgrams were captured at increasing sample concentrations for each analyte, represented by various colors, and fitted using kinetic binding models. The vertical dashed line in each affinity plot indicates the equilibrium dissociation constant ($K_D$), defined as the analyte concentration at which 50 % of the available binding sites are occupied, providing a quantitative measure of binding affinity.⁶¹

Fig. 4.

SPR-based kinetic and affinity characterization of synthetic cannabinoids.⁶¹

SPR kinetic analysis obtained from multiple sensorgrams recorded at increasing analyte concentrations. The association rate constant $k_a$, dissociation rate constant $k_d$, and equilibrium dissociation constant $K_D$. are extracted by globally fitting the experimental data (symbols) using a 1:1 Langmuir binding model (solid lines). This method makes it possible to distinguish between ligands with different binding kinetics but comparable equilibrium affinities. Adapted from Shi et al. (2025).

The practical application of this kinetic paradigm is amply demonstrated by recent experimental investigations. By capturing sensorgrams that clearly resolve baseline stabilization, concentration-dependent association, equilibrium saturation, and dissociation phases, Nurrohman and Chiu (2024) demonstrated a SPR-based kinetic study of biomolecular interactions. In the context of viral protein–receptor interactions in particular, their study demonstrates how changes in association and dissociation slopes reflect differences in interaction kinetics and binding strength. Similar to this, Shi et al. (2025) used SPR to globally fit many sensorgrams taken at various analyte concentrations to determine $k_a$, $k_d$, and $K_D$ values for a range of synthetic cannabinoids interacting with the CB1 receptor. The obtained kinetic profiles highlighted the significance of kinetic modeling beyond steady-state studies by allowing discrimination between ligands with comparable equilibrium affinities but different binding kinetics.

When taken as a whole, these findings show how SPR kinetic modeling supports applications in drug development, biomolecular engineering, and clinical diagnostics by offering quantitative insight into the strength and kinetics of molecular interactions.

3.6. Factors affecting SPR performance

Several factors influence the performance of SPR sensors, including68, 69, 70, 71:

• •Metal Selection: The choice of metal is crucial for the excitation of surface plasmons. Gold is the most widely used metal because of its biocompatibility and ease of functionalization. Silver, on the other hand, provides higher sensitivity but is more prone to oxidation.
• •Surface Functionalization: The sensor surface needs to be functionalized to selectively capture target molecules. Common strategies include using self-assembled monolayers (SAMs) or biotin-streptavidin coupling to immobilize biomolecules such as proteins, nucleic acids, or small molecules.
• •Immobilization Strategies: The method by which biomolecules are immobilized on the sensor surface plays a critical role in the sensitivity and reproducibility of the SPR sensor. Common immobilization techniques include covalent bonding, physical adsorption, and affinity capture.

3.7. The effect of metal layer thickness and refractive index on SPR signal strength

The optical performance of a Surface Plasmon Resonance (SPR) biosensor is primarily determined by the electromagnetic coupling between incident photons and surface plasmons at the metal-dielectric interface. The thickness of the metal layer (q) and its refractive index (n) are two physical characteristics that have a significant impact on the effectiveness of this coupling and, as a result, the sensor's sensitivity and stability 5, 6, 7, 8, 9, 10, 11:

3.7.1. The impact of metal layer thickness on SPR signal strength

3.7.1.1. Coupling efficiency and penetration depth

For a typical Kretschmann prism design, the metal layer (usually Au, occasionally Ag) must be thin enough (about 40–55 nm for Au) to allow the evanescent field $\overrightarrow{k}$ to tunnel through the metal and generate surface plasmons at the outer interface (Fig. 5).

• •
  If the metal film is too thin (q < 30–35 nm):

  • o
    Incomplete damping of the evanescent field occurs.
  • o
    Increased optical transmission leads to broad and shallow SPR dips.
  • o
    Signal becomes noisy and unstable due to increased roughness, grain boundaries, and oxidation (especially for Ag).
  • o
    Higher sensitivity but very poor baseline stability.
• •
  If the metal film is too thick (q > 55–60 nm):

  • o
    The evanescent field cannot effectively penetrate to the metal–analyte interface.
  • o
    Plasmon excitation becomes inefficient, producing:

    • ⁃
      a weak SPR dip,
    • ⁃
      reduced sensitivity to RI variations,
    • ⁃
      high reflectivity and low signal contrast.

Fig. 5.

Excitation of SP in the Kretschmann configuration.

Metal thickness, therefore, directly modulates the full width at half maximum (FWHM: which is the width of the resonance curve at half maximum, and minimum reflectance depth, which determines the precision of the resonance angle measurement.

3.7.1.2. Optimal thickness and Sensitivity–Stability balance

The appropriate thickness involves a trade-off:

• •Thinner films have greater plasmon coupling, sharper resonance, and better sensitivity, but are susceptible to morphological instability and oxidation.
• •Thicker coatings enhance chemical resilience and long-term stability, resulting in decreased sensitivity but greater reliability for repeated measurements and regeneration cycles.

In biosensing, 50 ± 5 nm Au is the optimum compromise. Ag has better sensitivity at 40 ± 5 nm but needs protective coatings (such as Al₂O₃, graphene) due to its chemical instability.

3.7.2. Effect of metal refractive index (RI) on SPR performance

The metal's complex refractive index (n + ik) controls the propagation constant of surface plasmons, which form resonance conditions. Two components are crucial12, 13, 14:

• ❖The real part (n): Optical Confinement

A lower real portion of the RI extends the evanescent field outside the metal, increasing sensitivity to changes at the analyte contact.

• •Gold: n = 0.18 at 633 nm • Silver: n = 0.14 at 633 nm (lower n = more sensitivity) This explains why silver produces sharper resonances and more sensitivity, although at the expense of faster oxidation and surface deterioration.
• ❖The Imaginary Part (k): Metal Losses

The imaginary component reflects optical absorption losses.

High k values (Au) result in larger resonance curves, higher FWHM, lower signal-to-noise ratio, and greater chemical endurance.

Lower k values (Ag) result in a narrow resonance dip, greater figure-of-merit (FOM), but low chemical and mechanical resilience.

Thus, the RI regulates both plasmon lifespan and resonance linewidth, which has a direct influence on sensing resolution.

3.7.3. Sensitivity–stability trade-offs in metal film design

High sensitivity is frequently associated with lower long-term operational stability, particularly under recurrent regeneration, biofouling, or temperature variations, therefore the strongest SPR signal is not always the most dependable (Table 2).

Table 2.

Impact of metal film properties on sensitivity and stability in SPR biosensors.5, 6, 7, 8, 9, 10, 11, 12, 13, 14

Parameter	Increase → Effect on Sensitivity	Increase → Effect on Stability	Overall Trade-off
Metal thickness	↓ sensitivity when thickness > optimal range	↑ mechanical and chemical stability	Thinner films = high sensitivity but unstable
Real part of metal RI (n)	Lower → ↑ sensitivity	Lower → ↓ confinement of energy	Optimizing n necessary for balanced field penetration
Imaginary part (k)	Higher → ↓ sensitivity (broader FWHM)	Higher → ↑ robustness (Au), ↓ oxidation	Ag vs. Au compromise
Surface roughness	↓ sensitivity due to scattering	↑ nonspecific adsorption, ↓ reproducibility	Requires controlled deposition

3.7.4. Practical guidelines for optimizing metal layer properties

• 1.Use 45–55 nm Au films for reliable biosensing applications.
• 2.Use Ag films for maximum sensitivity and protect them with ultra-thin dielectric coatings to prevent oxidation.
• 3.
  Adjust thickness for specific platforms.

  • o
    Fiber-optic SPR: significantly thicker metal (50–60 nm) to compensate for lower coupling.
  • o
    LSPR nanoparticles: size, shape, and material prevail over thickness.
• 4.To improve repeatability and decrease roughness, employ controlled deposition methods (e-beam, sputtering, template stripping).

4. Technological advancements in SPR biosensors

Surface Plasmon Resonance (SPR) biosensors have advanced significantly in terms of sensitivity, resolution, and application during the last several decades. These advancements have created new opportunities for study and practical applications in a variety of disciplines. The following are some of the important technological developments72, 73, 74, 75, 76, 77, 78, 79:

4.1. Enhancement of sensitivity and resolution

Recent advancements have led to significant increases in the sensitivity of SPR sensors. Nanomaterials (such as gold nanoparticles, carbon nanotubes, and graphene) have been used to improve the local electromagnetic field at the sensor surface, enhancing the sensor's sensitivity to minor changes in the refractive index. This enables the detection of analytes at low concentrations and weak molecular interactions.80, 81, 82, 83, 84, 85, 86, 87

• •Nanostructuring of Metal Surfaces: The development of nanostructured metal surfaces has been crucial for improving the sensitivity of SPR biosensors. For instance, nanoparticle-enhanced SPR (nSPR) exploits the plasmonic effects of metal nanoparticles to concentrate light at the sensor surface, enhancing detection capabilities (Fig. 6).^88,89

Fig. 6.

(A) Gold nanoparticles mediated SPR signal amplification strategies; (B) reflectivity of a gold film measured as a function of the incident angle in air for gold (circles), for a 1,6-hexanedithiol modified Au film (triangles), and for a layer of Au NPs deposited onto Au film-HDT (squares).⁶⁵ The solid line area calculated fits the exp. data [reprinted with permission from Hutter et al. (2001); copyright © 2001, American Chemical Society], (C) Au NP-amplified immunoassays and the corresponding SPR signals.⁶⁵

4.2. Miniaturization and portable SPR systems

The desire for on-site and point-of-care diagnostic tools has accelerated the downsizing of SPR sensors. Compact, portable SPR sensors are now being developed for field usage, enabling for real-time detection without the requirement for a laboratory environment. These handheld devices often combine SPR technology with microfluidics to provide portable, high-throughput detection of a wide range of analytes.90, 91, 92

• •Lab-on-a-chip devices: By combining microfluidics and SPR, researchers may conduct experiments on tiny chips with small sample volumes. This connection is particularly important in customized treatment and quick diagnostics (Fig. 7).

• ❖High-Throughput SPR Systems

Fig. 7.

Configuration of portable SPR biosensor instrument using tunable color OLED integrated by brightness enhancement and reflective polarizer microstructure.⁶⁶

With the increasing demand for high-throughput screening in drug development and other research disciplines, multi-channel SPR devices have been created to enable the simultaneous monitoring of many interactions. These methods allow for the simultaneous testing of a large number of compounds, dramatically accelerating drug discovery and biomarker validation.^93,94

4.3. Advanced detection methods

SPR biosensors are expanding to include sophisticated detection techniques, including SPR imaging (SPRi) and dual-wavelength SPR. SPR imaging delivers spatially resolved data over the sensor surface, enabling for the simultaneous monitoring of several binding events in various locations of the chip. Dual-wavelength SPR combines the benefits of classic SPR with a second wavelength of light, which improves detection of multi-analyte interactions and measurement precision.^95,96

4.3.1. Localized surface plasmon resonance (LSPR): from nanostructured interfaces to portable architectures

Localized Surface Plasmon Resonance (LSPR) has emerged as a major plasmonic sensing paradigm complementing conventional propagating SPR. While classical SPR relies on surface plasmon polaritons supported by extended metal films and typically requires prism- or waveguide-based coupling, LSPR is generated by the resonant oscillation of conduction electrons confined within metallic nanostructures (e.g., nanoparticles, nanorods, or periodic nanoarrays). This nanoscale confinement produces intense localized electromagnetic near-fields and enables high surface sensitivity to refractive-index perturbations occurring within a short sensing depth around the nanostructure. As emphasized in recent fiber-integrated implementations, LSPR platforms can be engineered by controlling the size, shape, composition, and spatial arrangement of nanostructures, allowing for resonance tunability and application-driven optimization of sensitivity and spectral response (Fig. 8).^97,98

Fig. 8.

Configurations of (a) straight transmissive and (b) flat-headed reflective FOLSPR sensors.⁶⁷(a) Straight transmissive FOLSPR architecture, where broadband light propagates through the fiber core and interacts with a nanoparticle-functionalized sensing region along the fiber sidewall via the evanescent field; the LSPR-induced spectral modulation is collected at the distal end. (b) Flat-headed reflective FOLSPR architecture, where illumination and signal collection are performed through the same fiber end using a coupler; LSPR-active nanostructures can be immobilized at the fiber end-face and/or near-end sidewall, enabling compact probe geometries for in situ or remote measurements. These layouts illustrate how LSPR can be implemented without prism coupling, facilitating miniaturization and field-deployable biosensing (Adapted from Lu et al., 2024, Photonic Sensors, https://doi.org/10.1007/s13320-024-0709-1).

From a systems perspective, LSPR is particularly attractive for the development of compact and portable biosensors because it can be implemented in simplified optical layouts and integrated into fiber-optic probes. In fiber-optic LSPR (FOLSPR) architectures, metallic nanoparticles or nanoarrays are immobilized on the fiber surface (sidewall and/or end-face), and the resonance is monitored through transmission or reflection modalities. These configurations enable miniaturization, remote sensing, and compatibility with multiplexed measurements, which are increasingly relevant for point-of-care and field-deployable platforms.⁹⁹

However, LSPR integration also introduces non-trivial engineering constraints that must be critically acknowledged in comparison to conventional SPR. Because the plasmonic response depends strongly on nanostructure morphology and distribution, device-to-device reproducibility can be limited by batch variations in nanoparticle synthesis or surface assembly, and resonance linewidth broadening may reduce the Figure of Merit despite strong local sensitivity. Consequently, LSPR is best viewed as a complementary approach, particularly advantageous where portability, surface-confined detection, and simplified coupling are prioritized. In contrast, classical SPR remains highly competitive for robust kinetic profiling and standardized, high-precision measurements.¹⁰⁰

4.3.2. Advanced chip and waveguide architectures in SPR biosensing

Recent developments in surface plasmon resonance (SPR) biosensing have progressively shifted from traditional bulk optical configurations toward integrated chip-based and guided-wave architectures that enable compact, scalable, and multifunctional sensing platforms. These architectures leverage planar and waveguide structures to confine light and plasmons within on-chip geometries, thereby enhancing device integration, system stability, and potential for mass production.

Fig. 9: Schematic representation of planar optical waveguide SPR sensor architectures. (a) Preparation process of an integrated waveguide-based SPR sensor with planar optical components and plasmonic layer. (b) Schematic experimental setup illustrating light coupling into and out of the waveguide-based SPR system, with microfluidic integration. These configurations exemplify advanced chip and waveguide architectures that move SPR beyond conventional prism-based designs toward compact, integrated platforms (Adapted from MDPI Biosensors, 2025, 15(1), 35).

Fig. 9.

(a) Preparation process of G/HMM/D-POF, (b) schematic of an experimental setup based on G/HMM/D–POF sensor.¹⁰¹

One major class of integrated SPR devices is based on planar optical waveguides. In these implementations, the SPR active region is incorporated into planar waveguides fabricated on substrates such as polymers, silica, or silicon. Walter and colleagues demonstrated a planar multimode polymer waveguide SPR sensor that couples light through simple optical elements and integrates the plasmonic layer within the planar structure. This configuration exhibits sensitivity on the order of 608.6 nm/RIU with a measurement resolution of 4.3 × 10⁻³ RIU and is particularly suitable for low-cost, disposable lab-on-chip applications with potential for multiplexed detection of biomarkers, as shown in their schematic of the planar waveguide sensor system.^107,108

Beyond planar guides, waveguide-coupled plasmonic architectures exploit hybrid mode interactions between guided optical modes and surface plasmon polaritons (SPPs) to achieve strong field confinement and sensitivity enhancement. Fiber-optic waveguide SPR sensors, which integrate plasmonic layers on optical fiber surfaces or microstructured fibers, not only reduce device footprint but also facilitate remote and distributed sensing in challenging environments. An optical fiber waveguide-coupled SPR sensor employing a zirconium disulfide dielectric layer and poly-dopamine cross-linker for C-reactive protein detection exemplifies this approach, combining transfer-matrix-optimized design with practical biochemical functionalization.^109,110

A particularly promising subclass of guided architectures is found in metal–insulator–metal (MIM) and hybrid plasmonic waveguides, where SPR modes are tightly confined within nanoscale gaps between metal and dielectric components. Recent reviews of MIM waveguide designs highlight their ability to support highly sensitive plasmonic modes with enhanced field intensities and strong interaction with analytes, potentially enabling ultra-small sensor footprints and compatibility with photonic integrated circuits. In such designs, geometrical parameters and resonator features can be engineered to balance field confinement and propagation losses, addressing a perennial trade-off in integrated plasmonic devices.^111,112

Despite their advantages, integrated waveguide and chip architectures also introduce challenges. Planar and fiber waveguide SPR systems must contend with coupling losses, fabrication complexity, and stringent alignment requirements, which may impact reproducibility and device yield. MIM and other sub-wavelength guided structures frequently exhibit increased propagation losses compared with conventional SPR, necessitating careful optimization of materials and geometric design to preserve both sensitivity and figure of merit. Moreover, integration of fluidic channels and surface chemistry within chip architectures remains a critical engineering task for reliable biosensing in real samples.^113,114

In summary, advanced chip and waveguide architectures represent a key frontier in SPR biosensor development, offering paths toward miniaturized, integrated, and application-ready platforms. These systems balance optical confinement, sensitivity, and manufacturability, enabling practical deployment beyond benchtop setups and advancing SPR toward real-world diagnostics and environmental monitoring applications.

Beyond hardware-level innovations, recent advances increasingly rely on artificial intelligence and machine learning techniques to overcome intrinsic limitations of SPR systems, particularly in terms of signal interpretation, noise suppression, and operation under data-scarce conditions. AI-based models, including neural networks and Bayesian regularized architectures, enable robust extraction of kinetic and affinity parameters from limited or noisy datasets and facilitate real-time decision-making in compact and portable SPR platforms. These digital approaches play a central role in enabling miniaturized, embedded, and point-of-care SPR systems.¹⁰⁶

4.3.3. Innovations in biorecognition elements for SPR biosensors

Recent advances in SPR biosensing increasingly rely on alternative biorecognition elements designed to overcome the limitations of conventional antibodies. Aptamers and engineered protein binders such as affirmers have emerged as promising recognition molecules due to their small size, high chemical and thermal stability, and excellent regeneration capability. In SPR platforms, these properties enable higher surface packing density, reduced mass transport limitations, and improved assay reproducibility, particularly for small-molecule detection and measurements in complex biological matrices. Moreover, their compatibility with harsh regeneration conditions and miniaturized sensor architectures makes them well-suited for portable and multiplexed SPR systems, where long-term stability and repeated use are critical performance requirements.6, 7, 8

5. Applications of SPR biosensors

Surface Plasmon Resonance (SPR) biosensors have found several uses due to their sensitivity, real-time detection capabilities, and label-free nature. The following are some of the primary uses of SPR biosensors 115, 116, 117, 118:

5.1. Drug discovery and development

SPR biosensors serve an important role in drug development because they allow for real-time monitoring of molecular interactions between drug candidates and target biomolecules (see Fig. 10). This label-free method enables direct study of binding kinetics, such as association and dissociation rates, which is critical for evaluating treatment effectiveness and improving chemical selection.^119,120

• •High-Throughput Screening: SPR technique enables high-throughput screening (HTS) of huge chemical libraries, allowing scientists to quickly find promising therapeutic candidates. The capacity to analyze several interactions concurrently using multi-channel SPR equipment has considerably expedited drug development (see Fig. 11).
• •Binding Affinity Determination: SPR is often used to determine the affinity of small compounds, antibodies, and peptides for their respective targets. This is critical for medication development, as maximizing binding strength and specificity is critical to therapeutic effectiveness.

Fig. 10.

Different pharmaceuticals and biomedical applications of SPR.¹⁰²

Fig. 11.

Overview schematic showing typical biomarkers, the various shapes-based sensor designs, and plasmonic detection that are explored within point-of-care diagnostic technologies.¹⁰³

5.2. Disease diagnostics

SPR biosensors are rapidly being employed in clinical diagnostics to identify illness biomarkers in blood, saliva, urine, and other biological fluids. The ability to monitor molecular interactions in real time without the use of labels or complicated reagents makes SPR an appealing technique for diagnostic applications.^121,122

• •Biomarker Detection: SPR is used to identify biomarkers related to cancer, cardiovascular disease, autoimmune disorders, and infectious illnesses. SPR allows disease signs to be identified quickly by monitoring the binding of certain antibodies or antigens to the sensor surface.
• •Point-of-Care Diagnostics: Portable SPR devices have been created for point-of-care diagnostics, allowing illness diagnosis to occur quickly and on-site. These gadgets may be used in hospitals, clinics, or at home and produce faster findings than standard diagnostic procedures.

5.3. Environmental monitoring

SPR biosensors are commonly used to detect environmental pollutants, contaminants, and poisons in air, water, and soil. These sensors provide a sensitive, real-time, and cost-effective approach to monitoring environmental health and safety.123, 124, 125

• •Water Quality Monitoring: SPR is used to identify heavy metals, pesticides, and industrial pollutants in water sources. This application is critical for maintaining clean drinking water and identifying environmental contamination early on.
• •Air Quality Monitoring: SPR's capacity to detect gaseous contaminants and airborne poisons has made it an invaluable instrument for air quality monitoring. SPR biosensors can be used to detect pollutants such as nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) that are hazardous to human health.

5.4. Food safety and quality control

In the food business, SPR biosensors detect infections, allergies, and spoilage factors in food items. This application protects the safety and quality of food items, including raw ingredients and processed meals.^126,127

• •Pathogen Detection: SPR can identify pathogens, including E. coli, Salmonella, and Listeria in food samples, hence preventing foodborne diseases. The capacity to conduct on-site, real-time testing enhances food safety measures while reducing the time and expense of existing testing techniques.
• •Allergen Detection: SPR is also used to identify allergens in food items, which is necessary for food labeling and the safety of those who have food allergies.

5.5. Biosensors for personalized medicine

Personalized medicine is a growing area that tailors medical therapy to individual patients based on their genetic and molecular profiles. SPR biosensors play a crucial role in this field by enabling the identification of molecular biomarkers unique to each patient.128, 129, 130

• •Genetic Testing: SPR biosensors may be used to study DNA or RNA interactions, which can aid in the detection of genetic mutations or variations linked to illnesses like cancer or genetic disorders. This allows for more accurate and tailored treatment strategies for patients.
• •Pharmacogenomics: SPR may also be used in pharmacogenomics to better understand how a patient's genetic profile influences their reaction to medications, allowing for more focused therapy with fewer adverse effects.

6. Applications of SPR biosensors: cutting-edge use-cases, benchmarking against ELISA/LFA/electrochemical sensors, and validation metrics

When compared to established assays like ELISA, lateral flow assays (LFAs), and electrochemical biosensors, surface plasmon resonance (SPR) biosensors are increasingly positioned not only as interaction-analysis tools but also as clinically and industrially relevant analytical platforms. Drug discovery and interaction profiling, near-patient and point-of-care (POC) biomarker testing, and field-deployable monitoring in food safety and environmental surveillance are now the most significant uses. Miniaturized photonic readout designs and data-driven analysis pipelines that may produce quick, quantitative judgments outside of centralized labs are strongly related to this translational expansion.¹⁰¹

6.1. Clinical and near-patient diagnostics

Compact SPR formats, such as fiber-optic SPR, are emphasized in recent work for therapeutically actionable biomarkers where quick quantitative reading is essential. A fiber-optic SPR assay, for instance, demonstrated the kind of repeatability reporting anticipated for translation with an inter-assay variance of 14.4 % and low detection/quantification limits in a clinically focused workflow. Journal of JTH¹²⁵ As proof-of-concept, physics gives way to clinically framed performance reporting; new nanomaterial-assisted SPR biosensors are being validated with claims of high specificity and clinical utility (e.g., pregnancy-associated biomarker detection with stability/specificity emphasis). ScienceDirect¹²⁶ Crucially, performance in real matrices (serum, plasma, or urine), cross-validation against reference techniques, and quantitative reproducibility parameters (CV, regeneration repeatability, chip-to-chip variance) are now typically included in the most publishable application studies.¹²⁷

6.2. Comparative perspective: SPR vs ELISA, LFAs, and electrochemical biosensors

Label-free real-time detection and the capacity to extract kinetic/affinity information instead of endpoint signals set SPR apart from ELISA. In contrast, ELISA is still easier to use and frequently dominates routine laboratories because of standardized workflows and inexpensive instrumentation, and there is constant effort to increase ELISA sensitivity through workflow and signal-amplification innovations. LFAs are still superior to SPR in terms of ease of use, speed, and decentralization. Recent reviews show how enhanced assay design, enrichment techniques, and sophisticated signal transducers are increasing LFA sensitivity and specificity. However, LFAs are still often limited by semi-quantitative readouts and limited multiplexed kinetic insight. Although electrochemical sensors are excellent for low-power portability and simple integration into small electronics, they may be susceptible to fouling and drift in complicated matrices. In cases where optical interrogation, surface-chemistry control, and kinetic analysis provide benefits, current comparative evaluations clearly place optical biosensing (including SPR) as complementary.128, 129, 130

6.3. Validation metrics that determine real-world value

Recent work in biosensing emphasizes that “high sensitivity” must be contextualized through analytical validity, diagnostic validity, and reproducibility to elevate application claims above descriptive reporting. Limit of detection (LOD), limit of quantification (LOQ), linear range, recovery in spiked matrices, and resistance to matrix effects are examples of analytical validity. Sensitivity, specificity, ROC/AUC, and clinically significant cutoffs are all components of diagnostic validity. Intra-assay and inter-assay CV, chip-to-chip variability, regeneration cycle performance, and stability under storage/continuous flow are examples of reproducibility that should be specifically stated. The literature on POC and near-patient biosensing increasingly demands these standards (Table 3, Table 4).¹³¹

Table 3.

Comparative analysis of SPR versus established biosensing technologies (ELISA, LFA, electrochemical sensors) for applied molecular detection.15, 16, 17, 18

Technology	Readout type	Label-free	Real-time kinetics	Typical strengths	Typical limitations	Best-fit use cases
SPR	Optical (RI change at surface)	Yes	Yes	Quantitative, kinetic/affinity extraction; multiplexing (SPRi); strong analytical control	Higher instrumentation complexity; surface chemistry & fouling must be controlled	Drug discovery, interaction profiling, quantitative biomarker panels
ELISA	Enzymatic color/fluoro/chemi readout	No	No	Mature, standardized, inexpensive per test; broad adoption	Multistep workflow; endpoint only; labels required	Routine clinical labs, high-throughput screening
LFA	Colorimetric/fluorescent test strip	No	No	Low cost, rapid, minimal training; field deployable	Often semi-quantitative; limited kinetics; sensitivity constrained (improving with new transducers)	POC screening, outbreak surveillance, home testing
Electrochemical	Current/voltage/impedance	Often yes	Limited (depends on design)	Portable, low power, electronics integration	Surface drift/fouling; matrix effects; calibration challenges	Wearables/portable analyzers, rapid field testing

Table 4.

Recommended validation metrics for SPR applications in complex matrices and near-patient settings.19, 20, 21, 22, 23, 24

Validation dimension	What to report (minimum)	Why it matters
Analytical sensitivity	LOD, LOQ, linear range, calibration model	Prevents over-claiming; enables cross-study comparability
Specificity/selectivity	Interferent panel; non-specific binding controls; matrix blanks	Essential for serum/plasma/food matrices
Reproducibility	Intra-/inter-assay CV; chip-to-chip variability; operator variability	Central for translation and multicenter use
Regeneration & stability	Number of regeneration cycles; baseline drift; storage stability	Distinguishes “demo” from robust platform
Method comparison	Bland–Altman/correlation vs reference method (sush as ELISA)	Demonstrates clinical/field relevance

7. Challenges and limitations of SPR biosensors

While Surface Plasmon Resonance (SPR) biosensors have numerous advantages, such as label-free detection, high sensitivity, and real-time monitoring, researchers and developers still confront a number of obstacles and constraints. These problems must be overcome in order to increase the broad use and commercial application of SPR technology.^132,133

7.1. Surface functionalization and reproducibility

SPR biosensor performance is strongly dependent on the quality and stability of surface functionalization (Fig. 12). Surface functionalization is the process of attaching biomolecules to the sensor surface, which is crucial for achieving selective binding and enhanced sensitivity. However, attaining consistent, repeatable, and well-defined surface functionalization is still a considerable difficulty.^134,135

• •Issue: Variability in surface chemistry and biomolecule orientation can lead to inconsistent results, reducing the reliability of measurements.
• •Solution: Ongoing research attempts to increase the consistency and robustness of SPR sensors by developing new surface functionalization technologies such as self-assembled monolayers (SAMs), polymer coatings, and nanomaterials.

Fig. 12.

SPR sensorgram of non-specific binding interference of negative serum, immobilized onto Neutravidin-covered gold surface. Constituents of serum bind to Neutravidin.¹⁰⁴

7.2. Criteria for selecting immobilization chemistry in SPR biosensing

Efficient immobilization of biorecognition components on the SPR sensor surface is a critical factor in the specificity, repeatability, and robustness of biosensing results. Because SPR detects changes in refractive index at the metal-analyte interface, surface chemistry has a direct impact on nonspecific adsorption, binding orientation, mass transfer, and regeneration capacity. The biochemical nature of the analyte, the surface qualities of the gold film, and the complexity of the biological matrix all have to be taken into account when choosing an acceptable immobilization approach.^25,26

• ➢Specificity:

The specificity of an SPR device refers to its ability to selectively detect a particular analyte in the presence of other substances. This is determined by the specificity of the surface chemistry used to functionalize the chip and the selectivity of the analyte-binding reaction.

• ➢Reproducibility:

The reproducibility of an SPR device refers to its ability to produce consistent results over multiple measurements. This is affected by factors such as the stability of the sensor chip, the precision of the instrumentation, and the variability of the sample preparation.

• ➢Robustness:

Robustness refers to the ability of a biosensor to maintain its performance under adverse conditions, such as high temperatures, extreme pH, or harsh chemicals. For instance, in an electrochemical biosensor, the electrode material should be chosen carefully to ensure that it is stable and resistant to corrosion.

7.2.1. Controlling orientation and bioactivity of ligands

• •Covalent amine coupling (EDC/NHS) is widely used for proteins with exposed lysine residues, but may lead to heterogeneous orientation and loss of biological activity.
• •Oriented immobilization strategies, such as His-tag/Ni–NTA interactions, biotin–streptavidin bridging, or site-specific thiol–maleimide coupling, enhance analyte accessibility and produce more reproducible kinetic profiles.
• •For antibodies, Protein A/G–mediated orientation maintains antigen-binding sites outward, improving both affinity and signal stability.

7.2.2. Minimizing non-specific adsorption in complex matrices

When working with serum, plasma, saliva, food extracts, or environmental materials, fouling becomes the most common cause of artifacts.^25,26

To prevent this, the immobilization layer should include hydrophilic antifouling coatings as PEG (polyethylene glycol), zwitterionic polymer brushes, or carboxymethyl dextran.

• ➢hydrophilic antifouling coatings as PEG (polyethylene glycol), zwitterionic polymer brushes, or carboxymethyl dextran.
• ➢Use BSA, casein, or fish gelatin to saturate unreacted surface areas.
• ➢Self-assembled monolayers (SAMs) with functional and inert molecules (such as, HS–(CH₂)₁₁–EG₆-OH/HS–(CH₂)₁₁–EG₆-COOH) balance ligand density and steric stability.

7.2.3. Ensure reproducibility across sensor chips

Reproducibility is impacted by immobilization density, homogeneity, and chemical stability.

Best approaches include controlling ligand density to prevent steric hindrance and rebinding artifacts in kinetic experiments.

• •Use carboxymethyl dextran or 3D hydrogels to create a hydrated scaffold for uniform ligand distribution.
• •Maintaining stable covalent bonds throughout several regeneration cycles (e.g., thiol-gold for SAMs or amide bonds for EDC/NHS surfaces).

7.2.4. regeneration compatibility

The immobilization approach must withstand the chemical conditions employed to renew the surface.

• •Strong covalent coupling is ideal for severe acidic or basic regeneration.
• •Non-covalent systems (streptavidin/biotin) have great sensitivity but are less regenerable, making them ideal for single-use or low-cycle sensors.

7.2.5. Selecting immobilization according to analyte type

Efficient immobilization chemistry must balance specificity (orientation, selectivity, antifouling behavior), reproducibility (uniform density, stable coupling), matrix compatibility (fouling resistance), and kinetic fidelity (minimizing mass-transport artifacts and rebinding) (Table 5).

Table 5.

Analyte-specific immobilization strategies for SPR biosensor applications.^25,26

Analyte	Recommended Immobilization Chemistry	Rationale
Proteins	EDC/NHS covalent coupling; Protein A/G orientation	High stability, oriented binding domains
DNA/RNA	Thiol-modified oligonucleotides; avidin–biotin	Stable, high-density immobilization
Small molecules	SAM-based surfaces with controlled ligand density	Minimizes steric effects and rebinding
Cells, exosomes	3D hydrogels, PEGylated interfaces	Reduces non-specific adsorption and deformation artifacts
Complex matrices (serum, plasma)	PEG, zwitterionic SAMs, blocking proteins	Antifouling properties essential

These criteria are critical for enabling accurate SPR analysis in real-world biological samples.

7.3. Sensitivity and detection limits

Although SPR biosensors are extremely sensitive, they have inherent limits for detecting analytes at low concentrations or in complicated biological samples (example: Fig. 13). The sensitivity of SPR is often in the nanomolar to picomolar range; identifying sub-picomolar concentrations or weaker interactions remains difficult.^136,137

• •Issue: The sensitivity of SPR sensors can be limited by factors such as low signal-to-noise ratios, interference from non-specific binding, and the quality of the sensor surface.
• •Solution: Recent advances in nanomaterials, such as gold nanoparticles and carbon-based nanomaterials, have been demonstrated to improve sensitivity by magnifying the SPR signal. Furthermore, using dual-wavelength SPR and SPR imaging approaches can boost sensitivity.

Fig. 13.

Performance comparison of different COVID-19 serological assays.¹⁰⁵

SPR biosensor assays, LFA tests, and ELISA tests are shown for positive and negative serum samples. LFA tests are considered positive when a colored band appears with regular (2) or strong (3) intensity in the IgG and/or IgM line, and negative when the bands are very weak (1) or non-colored (0). ELISA tests are considered positive when the IgG and/or IgA cutoff index (COI) exceeds 1.1. SPR biosensor assays are considered positive for samples above the set threshold (red dotted line), calculated as described in the Experimental section. Detection result rows display the number of positive (+) and negative (−) samples for each serological method.¹⁰⁵

7.4. Complexity and cost of equipment

While SPR technology has grown more widely available, it still requires somewhat expensive equipment, especially for high-resolution measurements and multiplexing applications. This can be a hurdle to its broad use, particularly in smaller laboratories or point-of-care diagnostics.^138,139

• •Issue: Traditional SPR systems are bulky, require complex optical components, and are costly to maintain, limiting their use in resource-limited settings.
• •Solution: The development of portable SPR devices and lab-on-a-chip systems helps to lower the cost and size of SPR sensors, making them more accessible for on-site testing and commercial applications.

7.5. Non-specific binding and interference

One of the primary issues with SPR biosensors is reducing non-specific binding (NSB), which occurs when molecules other than the intended target contact with the sensor surface. This can lead to false positives and impair the measurement's accuracy.^140,141

• •Issue: Non-specific binding can be caused by the sensor surface's physical properties or by interfering substances present in complex samples (e.g., blood or environmental samples).
• •Solution: Researchers are working on innovative tactics to prevent or reduce non-specific binding, such as utilizing blocking agents, modifying sensor surface chemistry, or applying selective immobilization techniques.

7.6. Data interpretation and quantification

Although SPR gives real-time data on molecular interactions, understanding and measuring this information can be difficult. Several variables impact the shift in SPR angle or intensity, including analyte concentration, interaction kinetics, and sensor surface quality.^142,143

• •Issue: Accurate interpretation requires sophisticated models to account for these variables, and real-time analysis can be challenging for multi-analyte systems.
• •Solution: Advanced data analysis approaches, such as machine learning and mathematical modeling, are being investigated to increase the accuracy and reliability of SPR data interpretation.

7.6.1. Kinetic parameters

SPR produces a sensorgram that consists of three primary phases: baseline, association, and dissociation, which indicate the temporal development of molecule binding. The kinetic parameters are obtained by fitting the experimental data to suitable binding models144, 145, 146:

7.6.1.1. Association phase

Analyte molecules attach to immobilized ligands on the sensor surface as they are injected. The rate of association is defined as follows:

$$\frac{dR}{dt}=k_{a}C\left(R_{\mathit{max}}-R\right)-k_{d}R$$ (15)

where:

• •R is the SPR response (proportional to bound analyte),
• •C is analyte concentration,
• •Rmax is maximum binding capacity,
• •ka is the association rate constant.

Solving this equation gives an exponential rise curve characteristic of bimolecular interactions.

7.6.1.2. Dissociation phase

When analyte-free buffer flows across the surface, molecules begin to dissociate:

$$\frac{dR}{dt}=-k_{d}R$$ (16)

This yields an exponential decay curve, from which kd is extracted.

7.6.1.3. Determination of equilibrium constant

The equilibrium dissociation constant is calculated as:

$$K_D=\frac{k_d}{k_a}$$ (17)

A lower KD indicates a stronger affinity.

7.6.2. Advanced binding models

SPR may suit complicated interactions beyond the basic 1:1 Langmuir model, including heterogeneous ligand interactions, bivalent binding or avidity effects, conformational change models, mass-transport constrained systems, and multivalent analytes.

Model selection is crucial for getting relevant kinetic parameters.

7.6.3. Major sources of error affecting kinetic measurements

Despite SPR's great temporal resolution, various physicochemical and instrumental variables can cause considerable errors.

7.6.3.1. Mass transport limitations

When analyte diffusion to the surface is slower than the binding reaction, the sensorgram reflects transport kinetics rather than intrinsic molecular rates.

Consequences:

• •Overestimation of ka
• •Underestimation of kd
• •Non-exponential association curves

This is frequent for high-affinity ligands, large ligand densities, and viscous/crowded samples.

7.6.3.2. Heterogeneous ligand orientation and surface density

Non-uniform immobilization creates diversity in binding site accessibility:

• •leads to multi-phasic sensorgrams,
• •complicates model fitting,
• •increases uncertainty in Rmax and KD.

Improper ligand density can cause:

• •steric hindrance (too high),
• •low signal-to-noise (too low).

7.6.3.3. Rebinding effects

During dissociation, analytes that detach may rebind to neighboring ligands, giving an artificially slow dissociation rate.

This results in:

• •underestimation of kd,
• •overestimation of affinity.

Rebinding is particularly problematic at:

• •high surface ligand densities,
• •low flow rates.

7.6.3.4. Bulk refractive index (RI) changes

Temperature fluctuations, buffer composition differences, and solvent injections can alter the refractive index independently of binding.

This can:

• •distort baseline,
• •shift the sensorgram artificially,
• •mimic false binding events.

7.6.3.5. Non-specific binding

If analytes bind to the underlying matrix, dextran layer, or hydrophobic metal surface:

• •true binding is masked by background signal,
• •KD and ka estimates become unreliable,
• •sensorgrams show poor fitting characteristics.

7.6.3.6. Instrumental noise and optical drift

Drift in the light source, detector noise, or mechanical instability can cause:

• •baseline fluctuations,
• •uncertainty in low-concentration measurements,
• •reduced precision in ka and kd.

7.6.3.7. Regeneration artifacts

If the regeneration step partially denatures or removes the ligand:

• •Rmax decreases over cycles,
• •kinetic parameters appear progressively altered,
• •reproducibility is compromised.

7.6.4. Best practices for accurate kinetic analysis

To reduce mistakes in kinetic parameter extraction, the following criteria are advised.

• •Optimize ligand density to prevent mass-transport constraints and rebinding.
• •Use high flow rates to explore high-affinity interactions.
• •Match buffer RI for analyte and running buffer.
• •Use reference channels to remove non-specific or bulk signals.
• •Validate the binding model with statistical metrics (χ² and residuals).
• •Repeat measurements for several analyte concentrations.

These methodologies ensure that the derived values accurately represent molecular interactions rather than sensing environment artifacts.

8. Emerging innovations and future directions

While Surface Plasmon Resonance (SPR) biosensors have already transformed the world of molecular detection, continued improvements and inventions are pushing the limits of what is achievable. From incorporating new materials and technologies to overcoming current restrictions, the future of SPR biosensors appears optimistic, with applications continuing to increase in many domains, including as medicine, environmental monitoring, and food safety. Below are some of the most exciting innovations and future directions in SPR biosensing technology.^147,148

8.1. Integration with microfluidics for point-of-care diagnostics

One of the most intriguing developments in SPR biosensors is its combination with microfluidic devices. Microfluidics enables real-time monitoring of molecular interactions in complicated samples by precisely controlling tiny quantities of liquid on a device. Researchers are creating lab-on-a-chip devices that can do quick, label-free biosensing at the point of care, eliminating the need for cumbersome laboratory equipment.^149,150

• •Potential: Portable SPR microfluidic devices can be utilized for on-site diagnostics, particularly in resource-limited environments. They might provide rapid and affordable detection of illnesses such as infections, cancer biomarkers, or environmental toxins.

8.2. SPR imaging (SPRi) for multi-analyte detection

SPR imaging (SPRi) is a new technology that enables the spatially resolved detection of numerous analytes at once. Unlike standard SPR, which monitors the change in SPR angle or intensity at a single spot, SPRi creates a two-dimensional map of the sensor surface, recording interactions across the chip. This method has substantial applications in high-throughput screening and multi-analyte detection.^151,152

• •Potential: SPRi might be used to track many molecular interactions simultaneously, increasing the efficiency of drug screening and biomarker research. It might potentially be used in diagnostics to detect several infections or biomarkers in the same sample.

8.2.1. Mechanisms enabling multiplexed SPR detection

8.2.1.1. Spatial multiplexing (most common in commercial systems)

Different regions of the same sensor surface are functionalized with distinct biorecognition ligands (antibodies, aptamers, peptides, DNA probes). The incident beam interrogates each region independently using¹⁵³:

• •multiple detection spots (as in Biacore and IBIS MX96 platforms),
• •microarray-type surfaces patterned using photolithography or microprinting,
• •segmented microfluidic channels delivering separate analytes.

As a result, independent sensorgrams can be acquired in parallel without optical interference.

8.2.1.2. Spectral multiplexing using wavelength-resolved SPR or LSPR nanostructures

Plasmonic nanostructures with distinct resonance peaks (such as, nanorods vs. nanodisks, gold vs. silver nanoparticles) allow for analyte-specific tracking. Each nanostructure generates a unique spectral signature, and binding events only shift the peak at its resonance wavelength. This allows for simultaneous quantification of multiple analytes in a shared flow channel.¹⁵⁴

8.2.1.3. Angular multiplexing

In prism-based systems, multiple resonance angles correspond to distinct sensing areas or layered structures. Each angle shift corresponds to a specific analyte.¹⁵⁵

8.2.1.4. Microfluidic multiplexing

Microfluidic modules with¹⁵⁶:

• •split channels,
• •laminar flow confinement,
• •individual capture zones,

allow different analytes to pass over specific ligand-coated regions, minimizing unintended interactions.

8.2.2. Design considerations to minimize cross-reactivity and signal interference

Multiplexing increases the complexity of the biochemical and optical environment. Preventing artifacts requires thoughtful design at multiple levels.¹⁵⁷

• ❖Surface Chemistry Strategies
• A.
  Highly Selective Ligand Functionalization¹⁵⁸

  • •
    Use antibodies with well-characterized epitopes.
  • •
    Utilize aptamers or engineered affinity molecules (e.g., affimers) with minimal cross-binding.
  • •
    Employ orthogonal chemistries to ensure strict physical separation of immobilization domains.
• B.
  Optimized Ligand Density¹⁵⁹

  • •
    Excessive ligand density increases non-specific binding and mass-transport interference.
  • •
    Calibrated spacing improves accessibility while avoiding steric hindrance between adjacent sensing zones.
• C.Antifouling Background Layers¹⁶⁰

PEG, zwitterionic polymers, and carboxymethyl dextran reduce:

• •non-specific protein adsorption,
• •signal drift,
• •false positives in shared microfluidic environments.
• ❖Microfluidic and Structural Design Considerations
• A.Physical Separation of Channels¹⁶¹

Using multi-channel microfluidic chips ensures that each analyte flows over its specific capture area only.

• B.Prevention of Lateral Diffusion¹⁶²

Design strategies can include:

• •laminar flow confinement,
• •hydrophobic barriers,
• •micro-post arrays,
• •surface patterning to restrict mass transport between zones.
• C.Control of Hydrodynamics¹⁶³

Stable laminar flow prevents analytes from cross-mixing or occupying unintended sensing areas.

• ❖Optical and Signal Processing Approaches to Reduce Interference¹⁶⁴
• A.Independent Region-of-Interest Analysis

Modern SPR instruments detect intensity or angle changes for each region separately, minimizing optical overlap.

• B.Deconvolution Algorithms

Signal-processing techniques correct for:

• •baseline drift,
• •cross-talk between adjacent spots,
• •global refractive index fluctuations (e.g., temperature or bulk RI shifts).
• C.Referencing Strategies

Use of:

• •blank channels,
• •reference ligands,
• •duplicated sensing spots,

allows subtraction of non-specific contributions.

Multi-analyte SPR detection is accomplished via spatial, spectral, and microfluidic multiplexing methods. These advancements allow for high-throughput biomolecular profiling, notably in diagnostic and clinical settings. To avoid cross-reactivity and produce accurate, analyte-specific sensorgrams, successful multiplexing necessitates careful control of surface chemistry, microfluidic architecture, ligand density, and optical separation. Antifouling coatings, precision microchannel segmentation, and robust referencing procedures are critical for preserving measurement integrity in complicated biological samples.

8.3. Nanomaterials and surface modification for enhanced sensitivity

Nanomaterials, including gold nanoparticles, quantum dots, carbon nanotubes, and graphene, have shown enormous promise in improving the sensitivity and functioning of SPR biosensors. These compounds can boost the local electromagnetic field at the sensor surface, boosting the interaction of light and analytes and resulting in greater SPR signals.^165,166

• •Potential: The inclusion of nanomaterials may enable SPR biosensors to detect ultra-low concentrations of analytes, increasing sensitivity for early detection of illnesses or environmental toxins. Furthermore, graphene-based SPR sensors have demonstrated tremendous potential due to their large surface area, biocompatibility, and simplicity of functionalization.

8.3.1. Mechanisms of Sensitivity Enhancement by nanomaterials

There are numerous ways to use Mechanisms of Sensitivity Enhancement by Nanomaterials. For example, 167, 168, 169.

8.3.1.1. Localized surface plasmon resonance (LSPR) amplification

Metallic nanoparticles (NPs), particularly Au and Ag, enable localized surface plasmon resonance. When located near the SPR sensing interface, they generate highly restricted electromagnetic “hot spots” that enhance the evanescent field.

Mechanisms.

• •Increased local field intensity, which improves sensitivity to small refractive index changes.
• •Enhanced effective mass loading, because analyte binding to nanoparticles induces a larger resonance shift than binding on a flat metal film.
• •Spectral coupling between SPR waves and localized plasmons increases FOM by sharpening resonance dips.

8.3.1.2. Enhanced surface area for biomolecular interactions

Nanostructured thin films (nanoporous gold, nanorods, and nanowires) provide a three-dimensional interaction surface.

• •Enhances analyte binding ability.
• •Boosts signal by increasing effective sensing volume.
• •Improves capture efficiency in diluted samples.

8.3.1.3. Improved charge transfer and electronic effects in 2D materials

Graphene, MoS₂, WS₂, and other 2D materials electronically interact with the metal layer, resulting in increased surface plasmon propagation length, improved biomolecule immobilization through π-π stacking interactions, and reduced non-specific adsorption through hydrophilic surfaces.

These effects combine to produce sharper resonance profiles and higher kinetic resolution.

8.3.1.4. Nanocomposite Metal–Dielectric layers

Dielectric–metal multilayers (TiO₂/Au, SiO₂/Ag, polymer/Au) modify plasmon dispersion:

• •Enhance field penetration depth,
• •Strengthen diffraction coupling,
• •Reduce radiative losses.

This leads to higher sensitivity, particularly for small molecules whose interaction signals are intrinsically weak.

8.3.2. Challenges and limitations in integrating nanomaterials into microfluidic SPR systems

The incorporation of nanomaterials into microfluidic SPR systems has emerged as a potent technique for improving sensing performance; nevertheless, it also brings a number of key technical and physicochemical problems that must be properly addressed. Metallic nanoparticles (Au, Ag), nanoporous films, hybrid metal-dielectric layers, and two-dimensional materials all improve SPR sensitivity, primarily through localized electromagnetic field amplification caused by SPR-LSPR coupling, increased effective surface area for analyte capture, modulation of plasmon dispersion and losses, and improved biointerface properties that promote ligand immobilization and analyte accessibility.

Despite these benefits, microfluidic operation imposes continuous hydrodynamic shear stresses that can compromise the mechanical stability and adhesion of nanoparticles, thin nanostructured films, and weakly anchored 2D materials, resulting in baseline drift, signal irreproducibility, and decreased operational.

Nanomaterials in biological matrices are also subject to colloidal instability caused by variations in ionic strength and pH, as well as protein adsorption and protein corona formation, which can result in nanoparticle aggregation and unanticipated plasmonic response changes. The intrinsic high surface energy of nanostructured surfaces exacerbates non-specific adsorption of proteins, lipids, polysaccharides, and cellular debris, especially in serum or plasma samples, reducing specificity and complicating surface regeneration.

From an optical standpoint, nanostructures may disrupt plasmon propagation by increasing scattering, resonance broadening, and mode mismatch, whereas three-dimensional nanostructured features can locally modify microfluidic channel geometry, disrupting laminar flow profiles and coupling hydrodynamic artifacts with optical readout.

Furthermore, the production of nanomaterial-enhanced SPR microfluidic chips remains difficult due to batch-to-batch variability in nanoparticle size and morphology, limited scalability of high-resolution nanopatterning techniques, and high manufacturing costs, all of which impede reproducibility and large-scale deployment.

Finally, many nanomaterial-based interfaces have poor endurance for severe regeneration conditions and long-term continuous flow, limiting their utility to low-cycle or disposable applications. As a result, while nanomaterials provide significant sensitivity gains, their successful incorporation into microfluidic SPR systems necessitates a careful balance of plasmonic enhancement, mechanical robustness, antifouling behavior, optical integrity, and fabrication reproducibility to ensure consistent performance in real-world biological applications.

8.4. Multi-wavelength SPR for enhanced data accuracy

While standard SPR systems employ just one wavelength of light, multi-wavelength SPR is a novel technique that uses many wavelengths to assess molecule interactions. Using various wavelengths allows for more detailed information regarding the binding kinetics, binding site features, and analyte concentration.¹⁷⁰

• •Potential: Multi-wavelength SPR may considerably increase the accuracy and precision of SPR biosensors, especially in complicated sample matrices. It would also improve the measurement of multi-analyte interactions and give more insight into molecular processes.

8.5. Artificial intelligence and machine learning for data interpretation

One of the most intriguing breakthroughs in SPR biosensing is the use of Artificial Intelligence AI and Machine Learning ML algorithms to interpret SPR data (Fig. 14). These algorithms may be trained to comprehend complicated SPR data, discover patterns, and predict molecular interaction behavior with greater accuracy than previous approaches.¹⁰⁶

• •Potential: AI and ML can improve data processing by automating the identification of tiny changes in SPR signals, lowering human error and increasing the speed and accuracy of biosensor readings. In drug research, for example, AI may assist in analyzing SPR data to forecast which compounds would bind most efficiently to a target biomolecule.

• ❖AI-Enhanced SPR Data Analysis Under Data-Limited Conditions: From BRANN to Intelligent Kinetic Inference¹⁰⁶

Fig. 14.

The use of machine learning to improve the analysis and accuracy of SPR data.¹⁰⁶

Despite significant advances in optical configurations and surface functionalization, SPR technology still has inherent limitations, such as low signal-to-noise ratios at ultra-low analyte concentrations, difficulty resolving heterogeneous or non-Langmuir binding kinetics, sensitivity to bulk refractive index fluctuations, and limited data availability in clinical or point-of-care settings. In this context, artificial intelligence (AI) approaches have emerged as effective tools for improving SPR data interpretation and extracting relevant information from sparse, noisy, or incomplete datasets.

Among these techniques, Bayesian Regularized Artificial Neural Networks (BRANN) are especially well suited to SPR analysis because they include Bayesian inference into the training process, allowing for robust learning from little data while preventing overfitting through automated regularization.

Unlike traditional curve-fitting methods that rely on predefined kinetic models, BRANN can capture complex nonlinear relationships between sensorgram features and underlying molecular interaction parameters, allowing for accurate estimation of kinetic and affinity constants even when classical assumptions are broken. This capacity is particularly useful for weak contacts, multivalent binding events, and partially resolved association or dissociation stages.

Complementary AI methods, such as deep learning models for time-series sensorgram analysis, ensemble learning algorithms for feature extraction and classification, and Bayesian probabilistic frameworks for uncertainty quantification, have been shown to denoise SPR signals, reconstruct incomplete binding curves, and distinguish between specific and non-specific interactions.

Collectively, these data-driven methodologies do not replace SPR's fundamental principles, but rather expand them, allowing for more robust kinetic inference, higher sensitivity under data-limited settings, and increased dependability for portable and real-world SPR biosensing applications.

8.6. Portable and cost-effective SPR devices

The future of SPR biosensors lies in the creation of portable, low-cost devices that are simple to use outside of laboratory environments. Advances in downsizing and integration with other technologies, such as microfluidics, are allowing the development of small SPR devices for point-of-care diagnostics, environmental monitoring, and potentially consumer use.^138,139

• •Potential: These portable SPR devices have the potential to make biosensing more accessible to a larger audience, particularly in impoverished or distant places with limited access to specialized laboratory equipment. SPR technology, by cutting costs and boosting mobility, might be used in routine healthcare and environmental monitoring.

Enabling technologies and the integrated systems they support are two contrasting angles from which to evaluate future developments in SPR biosensing. Sensitivity, specificity, and resilience at the sensor level are continuously being improved thanks to technical advancements in materials science, surface chemistry, nanofabrication, and data-driven analysis. In turn, these supporting technologies enable integrated SPR systems, including point-of-care, multiplexed, and portable platforms intended for practical implementation. However, issues like biofouling, long-term surface stability, reproducibility among devices, cost-effective manufacturing, and regulatory validation continue to impede the move from lab prototypes to commercially viable systems. In order to determine the future effect of SPR biosensors, addressing these practical obstacles will be just as important as ongoing technological improvement.

9. Conclusion

From interaction analysis tools used in laboratories, Surface Plasmon Resonance (SPR) biosensors have developed into increasingly complex sensing platforms that may provide quantitative, label-free, real-time molecular information. The sensitivity, resolution, and functional adaptability of SPR-based systems have all been improved by developments in plasmonic materials, surface immobilization chemistry, nanostructuring techniques, and integrated microfluidic architectures, as this study has demonstrated. Strong kinetic characterization, multiplexed detection, and operation in intricate biological and environmental matrices are now possible because to recent advancements that go beyond basic sensing concepts.

A thorough examination of existing SPR technologies shows that the precise balancing of surface chemistry, mass transport control, signal interpretation, and repeatability across sensor platforms determines performance advances rather than only plasmonic augmentation. When compared to well-known methods like ELISA, lateral flow assays, and electrochemical biosensors, SPR stands out for its exceptional capacity to deliver real-time kinetic and affinity data, despite practical issues with cost, surface stability, and large-scale standardization.

Despite tremendous advancements, important constraints still exist. The extensive use of SPR biosensors outside of controlled laboratory settings is still hampered by biofouling in complex materials, challenges with small-molecule detection, unpredictability brought about by surface immobilization techniques, and limitations on regeneration and long-term stability. In order to overcome these obstacles, systematic sensor design optimization, strict validation procedures, and standardized analytical techniques are all necessary.

In the future, the convergence of enabling technologies and integrated system-level solutions will determine the effect of SPR biosensing. Compact, automated, and application-specific SPR platforms are anticipated to be made possible by developments in nanomaterials, photonic integration, and data-driven analysis, including artificial intelligence-assisted interpretation. However, overcoming manufacturing, legal, and financial obstacles will ultimately be necessary to translate these breakthroughs into industrial sensing solutions, environmental monitoring instruments, and point-of-care diagnostics. SPR biosensors are well-positioned to continue being a key component of next-generation molecular detection with ongoing interdisciplinary research and an emphasis on practical validation.

Availability of data and materials

Although this research does not rely on any specific datasets, data, and materials can be made available upon request. Please contact nidal.elbiyari@femg.ueuromed.org for any inquiries.

Funding

No funding.

Declaration of competing interest

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