**5. Prospects of molecular diagnostics and biosensing using plasmonic nanostructures**

### **5.1 Gold standard methods**

Covid-19 is an acute respiratory disease that has infected over 500 million people worldwide and has taken lives of over 6 million people (as of November 2021). During the pandemic, governments offered tests sights in various locations to test as many people as possible using traditional testing methods. However, long queues, registering procedures and traveling proved to be major challenges. To curb the spread of this virus government required diagnostic sensors that are timely, portable, and accurate POC so that more people could be tested [44].

There are diagnostic methods that are regarded as gold standards for detection of infectious diseases such as detection of TB and HIV using immunoassay and Covid-19 via PCR [73]. These diagnostic standards are regarded as low cost, extremely sensitive and easy to operate with low limit of detection [74]. However, the recent 2020 worldwide Covid-19 pandemic forced a divergence use of the traditional gold standards methods. The gold standards methods often requires a well-trained physicians and it is time consuming. The alternative testing such as lateral flow immunoassay and other plasmonic nano sensors significantly reduces the time for results down to 15 minutes with an added advantage of portability. Even though the detection limit of the gold standard methods is superior to the plasmonic nano sensors, there is a consensus prospects that these diagnostic nano sensors based on plasmonic nanoparticles would be use more in the future to reach masses for screening of infectious diseases [44].

### **5.2 Miniaturization**

The optical biosensors often require a reliable light source and an optical read out instrument. The optical biosensors mostly relies on the natural sun light as the source of light and the human eye as the photo-detector in the visible wavelength. A good example of these optical biosensors are the colorimetric pregnancy tests [44]. Where the nanoparticles such as gold produces LSPR that induces light absorption change detectable by the naked eye. The future of this technology heavily depends on the development of semiconductor technology to produce extremely sensitive, reliable and enhance resolution results. The fast growing semiconductor technology in the cell phone industry means that cell phones are now equipped with powerful computing power and high resolution cameras. These cell phone capabilities means that in the future, the light emitted diode (LED) from cell phones, could be used as a light source with accurately controlled wavelength and intensity. The high-resolution camera of a cell phone could be used as sensitive photo-detectors for the sensing. Hence, in the future, cell phones would be used to provide real-time diagnostic results via analysis of optical signal analyzed with complex algorithms [44].

### **5.3 Theranostics**

Theranostics is a nanomedicine that comprises of both diagnosis and therapies of illnesses achieved by using a biomaterial based nanoplatform. This nanomedicine technology uses nanostructures within the 1 to 100 nm range that are functionalised with organic or inorganic materials and often with an engineered compound such

*Application of Plasmonic Nanostructures in Molecular Diagnostics and Biosensor Technology… DOI: http://dx.doi.org/10.5772/intechopen.108319*

**Figure 8.**

*Various therapies and diagnostic approaches used for the theranostic concept [75].*

as phthalocyanine that could disrupt the functionality of the cells of the infectious disease [75]. The functional groups on the surface of the nanoparticles usually directs them to the desired location of the infectious disease that could be detected using ultrasound, MRI, or CT scans (diagnosis). Once at the target site, the engineered compounds are activated by energy source such as laser (phototherapy), causing them to release energy causing a cell destruction (therapy) such as stubborn cancer cells. The advantages of theranostics technology that would make this the technology of choice in the future are; improved targeting effect, reduced systematic toxicity and enhanced solubilizing potential to hydrophobic drugs [76]. **Figure 8** shows a schematic of the dual effects of theranostic approaches.

Even though application of plasmonic materials in bio-molecular sensing has seen great leaps in the past couple of decades and has circumvented multiple challenges. The areas that are leading the path into the future for the biosensing technologies is machine learning and artificial intelligence.

### **5.4 Machine learning**

Plasmonic biosensors uses plasmonic nanoparticles to enhance the surface Plasmon effect, which highly depends on the morphology of the nanoparticles. The different morphology of these nanoparticles offer difference optical properties. Hence, it is crucial to design the plasmonic nanoparticles to suit its application [77, 78]. Difference morphology are often required for the detection of different infectious diseases such as human immunodeficiency virus (HIV), tuberculosis (TB), hepatitis B virus (HBV) with varying limits of detection. The various morphology of plasmonic nanoparticles is shown in **Table 1**.

The prospects of using plasmonic nanoparticles would depend on better understanding and accurate prediction of the plasmonic properties of the nanoparticles. Often optical properties of nanoparticles are computed using molecular modeling. However, it is challenging to predict with a high degree of accuracy the perfect nanoparticle morphology with the required optical properties. Furthermore, molecular modeling is time consuming [79, 80]. The recent developments of machine learning and artificial intelligence are the answer for this challenge and holds hope for future applications. Machine learning is more accurate in predicting the required properties for the application. Furthermore, machine learning has the capabilities to optimize the design parameters of the plasmonic nanoparticles to achieve the required optical properties [81]. Hence, machine learning serves as the powerful tool that can be used extensively in various plasmonic image analysis for better understanding their optical properties.
