## 2 Latest Developed Biosensors for COVID-19
### Electrochemical Biosensor
#### 2.3.1 Electrochemical Biosensor for COVID-19
The sandwich hybridisation of RCA amplicons with probes that are functionalised with redox-active labels (e.g., methylene blue for N gene and actidine orange for S gene) was detected by DPV. The redox-active reaction could detect as low as 1 copy mL\({}^{-1}\) of viral N or S genes in less than 2 hours. Moreover, the use of magnetic capture and separation of targets from non-targets reduces the chance of residual contamination and pipetting error, thereby reducing the risk of erroneous results and improving the assay precision [85]. The potentiostat is a key component for reading electrochemical signals, but the traditionally large and expensive bench-top versions limit its use in resource-limited environments. Hence, a portable potentiostat was developed and used, in conjunction with a computer, for electrochemical measurement at the point of use. The smartphone is the most widely used portable device in the world. It is equipped with powerful connectivity features and researchers have explored the use of smartphones as a wireless diagnostic tool [74; 86]. A portable potentiostat with wireless connectivity to a smartphone would facilitate electrochemical analysis at the point-of-use, where access to a computer or wired connection to a device is difficult or impossible [87]. A smartphone-based supersandwich-type electrochemical biosensor was demonstrated as shown in Figure 5D for SARS-CoV-2 RNA detection without nucleic acid amplification and reverse-transcription [88]. The assay employed for sensing utilised two different kindsof primer A and B. The former contains Au@Fe3O4 nanocomposites, the capture probe (CP) and 1 mM hexane-1-thiol (HT). The latter contains Au@p-sulfocalix[8]arene(SCX8)-toluidineblue(TB)-graphene(RGO) nanocomposites, the labelled probe (LP) and the auxiliary probe (AP). Primer A and then B are mixed with the target for 1 h and 2 h, respectively. Subsequently, the sandwich structure formed was dropped on the SPE for electrochemical measurement of TB signal by a smartphone in less than 10 s. TB is a basic thiazine metachromatic dye with a high affinity for nucleic acids, thereby staining tissues with a high DNA and RNA content [89]. This method requires a long incubation time of about 3 h for the formation of sandwich structure and more than 12 h for the preparation of primer A and B. For detection of SARS-CoV-2 in clinical specimens, the true positive rate was 85.5% and 46.2% for confirmed and recovered patients, respectively. The sensor does not require RNA amplification and it only requires two copies of SARS-CoV-2 for an assay, which is advantageous compared to the existing PCR-based RNA assay. The detection limit of the tested samples was found to be 200 copies mL\({}^{-1}\). Figure 5: Electrochemical biosensor for SARS-CoV-2 detection. **(A)** Graphene-based electrochemical biosensor. Reprinted with permission from [39]. **(B)** MBs-based electrochemical biosensor. Reprinted with permission from [40]. Copyright 2020, Elsevier B.V. **(C)** Electrochemical biosensor with RCA of the N and S genes. Reprinted with permission from [PERSON], [PERSON] (2021). Copyright 2021 Springer. **(D)** Smartphone-based supersandwich-type electrochemical biosensor. Reprinted with permission from [88]. Copyright 2021, Elsevier B.V.  
A few other recently reported works utilised modified screen-printed electrodes with nanostructured materials, as depicted in Figure 6A, to enhance the detection of SARS-CoV-2 spike protein and glycoprotein. In [90], an electrochemical biosensor was fabricated using disposable carbon-based SPE, where the electrode surface was modified with Cu\({}_{2}\)O nanocubes. IgG anti-SARS-CoV-2 was attached to staphylococcal protein A (Prot A) that was loaded on the surface (Figure 6B). The larger surface area of the nanocubes provided more active sites to bind IgG and hence allowed more SARS-CoV-2 to be detected. The biosensor exhibited a LOD of 0.04 \(\mathrm{fg\ mL^{-1}}\), and it was 100% successful in detecting true positive and negative samples, based on 16 clinical samples.