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Exceptional points (EPs), representing non-Hermitian singularities where eigenvalues and eigenvectors coalesce, have emerged as an intriguing concept in modern physics. Their unique topological and asymmetric responses have enabled new pathways for controlling wave propagation, sensing, and signal processing. Among the diverse platforms, plasmonic systems have proven especially attractive, as their inherent loss, strong dispersion, and tunable resonances and coupling can be utilized to tailor EPs at the subwavelength scale. In this review, we provide a comprehensive overview of recent advances at the intersection of EP physics and plasmonics. We first introduce the mathematical and physical foundations of EPs in non-Hermitian systems. We then discuss key experimental and theoretical developments in plasmonic EPs, including polarization manipulation, asymmetric wavefront control, mode conversion, and enhanced sensitivity. Particular attention is given to strategies that exploit symmetry breaking, loss engineering, and geometric phases to realize novel and robust EP-driven functionalities. Finally, we discuss emerging trends, such as the use of EP pairs for full vectorial control and the integration of non-Hermitian plasmonic structures with tunable, active, and even nonreciprocal thermal emission. By reframing loss as a design resource and leveraging the Riemann-surface geometry of non-Hermitian spectra, plasmonic EPs offer a new route to compact, robust, and programmable nanophotonic devices. 

Machine learning offers a promising approach to addressing the unique challenges of non-Hermitian systems, particularly in classifying topological phases and overcoming the limitations of traditional methods. However, research remains sparse for complex non-Hermitian systems involving higher-order hopping terms. In this work, we employ uniform manifold approximation and projection (UMAP), an unsupervised machine learning technique, to identify phase transition points in systems exhibiting the non-Hermitian skin effect (NHSE). Conventionally, NHSE is analyzed using non-Bloch theory, which requires redefining the Brillouin zone as the generalized Brillouin zone and solving for the complex eigenvalue contours. In contrast, our UMAP-based approach can successfully identify NHSE phase transitions in a system featuring higher-order hopping terms without relying on prior knowledge. Beyond classification, we introduce a unified framework that integrates information from three major theoretical perspectives: numerical solutions under different boundary conditions, non-Bloch theory, and Bloch theory. This framework quantitatively connects these traditionally separate and sometimes incompatible analyses. UMAP’s distance metric captures the topological phase structure in a way that bypasses the need for selecting specific analytical methods or performing multiple complex computations. This approach not only circumvents the limitations of detailed analytical methods but also offers physical insights into complex topological phenomena, representing a fundamental theoretical advancement in non-Hermitian physics. We anticipate that our work will further stimulate interdisciplinary research at the intersection of physics, photonics, and artificial intelligence. 

Plasmonics and optical metastructures represent cutting-edge frontiers in nanophotonics, enabling on-demand control of light at the subwavelength scale. This special topic of the Journal of Applied Physics highlights the recent advancements and synergy of the two fields, delving into the fundamental physics governing plasmonic phenomena and showcasing innovative metastructures that hold significant potential for diverse applications, including sensing, optical manipulation, wireless communication, optical computing, and beyond. 

Abstract The identification of Chiral molecules is essential in pharmaceutical and food science. However, conventional methods are complex and cost‐prohibitive. This study introduces a sustainable method using hydroxypropyl cellulose (HPC) gel to identify amino acids enantiomers, such as phenylalanine and alanine, through visible light. By integrating the structural color properties of HPC, this research demonstrates the HPC gel's capability to distinguish L (Levo)‐phenylalanine (L‐Phe), D (Dextro)‐phenylalanine (D‐Phe), and DL (racemic mixture)‐phenylalanine (DL‐Phe) supplemented with visible circular dichroism (CD) spectra or hydrochloric acid (HCl) as visual indicators. Similar chiral sensing results are observed with D‐alanine, L‐alanine, and DL‐alanine. Unlike traditional UV‐based detection requiring expensive equipment, this approach simplifies the process while maintaining sensitivity. Varying phenylalanine concentrations altered the CD response without disrupting the gel's helical structure, and color changes in response to HCl addition facilitated visual identification of enantiomers. Furthermore, adding various salts generates colorful HPC/Phe gels, demonstrating their suitability for 3D printing. Meanwhile, the HPC gels remained functional for three months, indicating long‐term stability. These advancements are significant for pharmaceutical and biotechnological industries, facilitating efficient low‐concentration chirality detection (0.2 wt.%). Continued development and refinement of this technology are expected to expand its applications and improve analytical capabilities for future chirality‐related studies and photonic gel 3D printing.