An integrated-optics, flat-top add-drop filter with low excess loss and high sidelobe suppression ratio (SLSR), is demonstrated. The filter is based on apodized multimode waveguide Bragg grating (AMWBG) developed on a silicon-on-insulator (SOI) platform. Antisymmetric periodic refractive-index perturbations are imposed on both sides of the waveguide to couple light contra-directionally between the fundamental (TE0) and the first-order transverse electric modes (TE1). To achieve a Gaussian apodization profile, the lateral-misalignment between the two sides of the waveguide is modulated by adjusting the positions of both sides of the grating features in a complementary manner. In this way, the undesired phase errors, which exist in conventional misalignment-modulated Bragg gratings and distort the grating responses, can be eliminated. Experimental results show that our AMWBG has a high-quality flat-top response, a high SLSR of >21 dB, a 3-dB bandwidth of 8 nm, a low excess loss of 0.24 dB, and a high roll-off rate at the filter edge. We also demonstrate a dual-stage filter that has a flat-top, drop-port response with a 3-dB bandwidth >7.5 nm, a SLSR >40 dB, and an excess loss<0.5 dB.

Multimode waveguide Bragg gratings (MWBGs) provide significant advantages over traditional single-mode counterparts through their mode-coupling operations. Nevertheless, flexible spectral response design methodologies for MWBG-based filter remain less studied. This work introduces a spectral tailoring methodology enabling physically realizable complex responses in MWBGs. We demonstrate silicon-based multi-channel Gaussian-shaped MWBGs using lateral phase delay modulation (LPDM) apodization. Experimental results confirm close conformance between measured spectral responses and target design specifications.

Machine learning has created new opportunities for gas adsorption research using nanoporous materials, but the field’s evolution remains insufficiently quantified. This study retrieved literature from the Web of Science Core Collection for 2010–2026 and retained 730 valid records from 1581 initial publications after screening. VOSviewer, CiteSpace, and R were used to analyze publication growth, collaboration networks, journal sources, and thematic evolution. Results show that annual output remained generally below 20 before 2019, then increased rapidly and reached approximately 280 publications in 2025, indicating accelerated integration of machine learning with adsorption simulation, material screening, and performance evaluation. The source distribution broadened from a limited set of chemistry and engineering journals to diverse venues, with recent high publication weights in Chemical Engineering Journal, Separation and Purification Technology, ACS Applied Materials & Interfaces, Microporous and Mesoporous Materials, and Journal of Materials Chemistry A. Collaboration analysis identified 10 compact author clusters, including groups associated with Randall Q. Snurr, Seda Keskin, Zhiwei Qiao, Qingyuan Yang, and Chongli Zhong, whereas the weak bridging links among clusters indicate that cross-community collaboration remains limited. Country and institutional analyses show that China, the United States, Canada, Iran, India, South Korea, and the United Kingdom are leading contributors, with Guangzhou University, Koç University, Northwestern University, the Chinese Academy of Sciences, Beijing University of Chemical Technology, and the United States Department of Energy occupying prominent positions. Keyword evolution reveals a shift from adsorption behavior and porous adsorbents toward data-guided material selection, high-throughput screening, deep learning, Bayesian optimization, and performance optimization, offering guidance for data-driven adsorbent discovery.