In the known field of topological photonics, what remains less so is the breakdown effect of topological phases deteriorated by perturbation. In this study, the authors investigate the variance on topological invariants for a periodic Kekulé medium perturbed in unit cells, which was a gyromagnetic photonic crystal holding topological phases induced by synchronised rotation of unit cells. Two parameters for geometric and material perturbation are respectively benchmarked to characterise the topological degradation. Our calculation demonstrates that such a periodic perturbation easily destructs the topological phase and thus calls for further checkups on robustness under such unit‐cell‐perturbation in realisation.

In our paper, the authors take the simple case of periodic randomisation by disrupting two parameters in unit cells. The authors characterise the topological breakdown via directly calculating variances to Chern numbers under their defined perturbation. Their calculation demonstrates that such a periodic perturbation easily annihilates topological invariants, providing benchmark for future design of these topological devices.image

Fano resonance arises from the superposition of a narrow discrete resonance with a continuous state. In this paper, a Fano resonance‐based plasmonic refractive index sensor based on nanoscale metal‐insulator‐metal (MIM) waveguides is proposed and evaluated by applying the two‐dimensional finite‐difference time‐domain (2D FDTD) method. An MIM‐based single stub vertically attached to an MIM waveguide is considered as the unit cell of the waveguide system of the proposed structure. A transfer matrix of transmission and coupling complex coefficients is defined for the unit cell of the waveguide system of the proposed sensor, and the conditions for the authenticity of its modeling based on the transmission line theory are evaluated. A system of triplet coupled stub resonators (TCSRs) attached to an MIM waveguide is coupled with a disk resonator to induce Fano resonances in the transmission spectrum of the sensor. Fano resonance and its characteristics are utilized to improve the essential operating parameters of the sensor, such as full width at half maximum (FWHM) and figure of merit (FOM). In optimized conditions, for the system of TCSRs coupled to a disk resonator, a resonance with a sensitivity of 684 nm/RIU and an FOM of 621.8 1/RIU exhibited. Also, for the system of TCSRs coupled to a ring resonator, a resonance with a sensitivity of 3524 nm/RIU and an FOM of 35.24 1/RIU was achieved. Eventually, the numerically achieved results for the operating parameters of the sensor are validated using governing analytical methods.

In this study, transmittance characteristics of Fano resonances, induced from triplet coupled stub resonators (TCSRs) coupled with a disk or ring resonator, were extracted based on the numerical 2D FDTD‐based simulations. A transfer matrix of transmission and coupling complex coefficients was defined for the unit cell of the waveguide system of the proposed sensor, and the conditions for the authenticity of modeling it based on the transmission line theory were evaluated. Also, the numerically achieved results for the operational parameters of the sensor were validated using governing analytical methods.image

In rapid development, Selective Laser Sintering (SLS) creates prototypes by processing industrial materials, for example, polymers. Such materials are usually in powder form and fused by a laser beam. The manufacturing quality depends on the interaction between a high‐energy laser beam and the powdered material. However, in‐homogeneous temperature distribution, unstable laser powder, and inconsistent powder densities can cause defects in the final product, for example, Powder Bed Defects. Such factors can lead to irregularities, for example, warping, distortion, and inadequate powder bed fusion. These irregularities may affect the profitable SLS production. Consequently, detecting powder bed defects requires automation. An ensemble learning‐based approach is proposed for detecting defects in SLS powder bed images from this perceptive. The proposed approach first pre‐processes the images to reduce the computational complexity. Then, the Convolutional Neural Network (CNN) based ensembled models (off‐the‐shelf CNN, bagged CNN, and boosted CNN) are implemented and compared. The ensemble learning CNN (bagged and boosted CNN) is good for powder bed detection. The evaluation results indicate that the performance of bagged CNN is significant. It also indicates that preprocessing of the images, mainly cropping to the region of interest, improves the performance of the proposed approach. The training and testing accuracy of the bagged CNN is 96.1% and 95.1%, respectively.

An ensemble learning‐based approach is proposed for detecting defects in SLS powder bed images from this perceptive. The proposed approach first pre‐processes the images to reduce the computational complexity. Then, the Convolutional Neural Network (CNN) based ensembled models (off‐the‐shelf CNN, bagged CNN, and boosted CNN) are implemented and compared. The ensemble learning CNN (bagged and boosted CNN) is good for powder bed detection. The evaluation results indicate that the performance of bagged CNN is significant. It also indicates that preprocessing of the images, mainly cropping to the region of interest, improves the performance of the proposed approach. The training and testing accuracy of the bagged CNN is 96.1% and 95.1%, respectively.image

The authors theoretically designed a highly sensitive SPR biosensor with a hybrid structure using two‐dimensional nanomaterials (BP‐WS₂). Using the transfer matrix method (TMM), the performance of the sensor in terms of reflection, sensitivity, detection accuracy, and quality factor is investigated, and by changing the structural parameters of the sensor, the obtained results are further analysed so that an optimal structure with optimal performance can be achieved. The sensor was optimised with four layers of BP and a single layer of WS₂. This composite structure is placed on a 50 nm thick gold layer and concluded with a maximum sensitivity of 234 deg/RIU with a FOM of 26.53 RIU⁻¹. This biosensor is designed by considering the advantages and characteristics of biosensors in their resistance and high electrical properties, as well as the ability to detect diseases quickly.

The authors theoretically designed a highly sensitive SPR biosensor with a hybrid structure using two‐dimensional nanomaterials (BP‐WS2). This biosensor is designed by considering the advantages and characteristics of biosensors in their resistance and high electrical properties, as well as the ability to detect diseases quickly.image