The sudden rise of the worldwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in early 2020 has called into drastic action measures to perform instant detection and reduce the rate of spread. Common clinical and nonclinical diagnostic testing methods have been partially effective in satisfying the increasing demand for fast detection point-of-care (POC) methods to slow down further spread. However, accurate point-of-risk diagnosis of this emerging viral infection is paramount as the need for simultaneous standard operating procedures and symptom management of SARS-CoV-2 will be the norm for years to come. A sensitive, cost-effective biosensor with mass production capability is crucial until a universal vaccination becomes available. Optical biosensors can provide a noninvasive, extremely sensitive rapid detection platform with sensitivity down to ∼67 fg/ml (1 fM) concentration in a few minutes. These biosensors can be manufactured on a mass scale (millions) to detect the COVID-19 viral load in nasal, saliva, urine, and serological samples, even if the infected person is asymptotic. Methods investigated here are the most advanced available platforms for biosensing optical devices that have resulted from the integration of state-of-the-art designs and materials. These approaches include, but are not limited to, integrated optical devices, plasmonic resonance, and emerging nanomaterial biosensors. The lab-on-chip platforms examined here are suitable not only for SARS-CoV-2 spike protein detection but also for other contagious virions such as influenza and Middle East respiratory syndrome (MERS).
Artificial neural networks are employed to predict the band structure of the one-dimensional photonic crystal nanobeam, and to inverse-design the geometry structure with on-demand band edges. The data sets generated by 3D finite-difference time-domain based on elliptical-shaped hole nanobeams are used to train the networks and evaluate the networks’ accuracy. Based on the well-trained forward prediction and inverse-design network, an ultrabroad bandgap elliptical hole dielectric mode nanobeam cavity is designed. The bandgap achieves 77.7 THz for the center segment of the structure, and the whole designing process takes only 0.73 s. The approach can also be expanded to fast-design elliptical hole air mode nanobeam cavities. The present work is of significance for further research on the application of artificial neural networks in photonic crystal cavities and other optical devices design.
We theoretically propose an ultracompact large-dynamic-range dual-parameter sensor using a broad free spectral range (FSR) multimode photonic crystal nanobeam cavity (MM-PCNC). In the multimode regime, each resonant mode is exploited as an independent sensing channel. Broad FSR (>100 nm) is achieved by PCNC consisting of composite lattice cells (CLCs). The CLC is designed for the special bands property enabling the excitation of multiple resonant modes with broad FSR possible. Notably, an interesting stability of the mirror strength is achieved for the CLC, which provides a new perspective for further optimizing ultracompact PCNCs with high quality factor (Q) and broad FSR. Additionally, due to the special structure of the CLC, the energy of resonant modes can be effectively localized in the low dielectric area, which are quantitatively indicated by the calculated optical overlap integrals, resulting in strong light-matter interactions. Simultaneous detection of the refractive index (RI) and temperature is conducted by multiplexly using the fundamental mode and the first-order mode of the PCNC, with the optimal RI and temperature sensitivities of 413 nm/RIU and 62.9 pm/K, and the corresponding detection limits of 7.2 × 10 −6 RIU and 0.117 K, respectively. Large-dynamic-range sensing supported by the broad FSR is also analyzed. Therefore, due to the broad FSR, high Q, and ultracompact size, the proposed MM-PCNCs are promising platforms for realizing applications such as large-dynamic-range detection, high integration large scale on-chip sensing, and multifunctional detection in the future.
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