Liuyang Zhang

Molecular vibrational fingerprints in the terahertz (THz) regime offer unique opportunities for label‐free chemical and biological sensing. Metasensors can amplify these vibrational signals through local near‐field enhancement. However, current THz molecular fingerprint metasensors lack a systematic design and optimization methodology, which severely limits their potential. Here, we unveil the crucial role of resonant loss rate in THz molecular fingerprint sensing and present a loss‐engineering methodology for optimized molecular vibrational sensing. Based on the proposed methodology, we experimentally demonstrate a 12.6‐fold enhancement of the molecular fingerprint signal with three‐dimensionally printed pixelated metasensors. Furthermore, the pixelated metasensors support vibrational strong‐coupling, providing a unified platform for enhanced molecular sensing and strong light‐matter interaction. Overall, the proposed metasensor could bridge THz molecular vibrational fingerprint sensing to end‐use applications in label‐free chemical and biomedical detection.

Although condition monitoring systems (CMS) provide detailed information about the health of critical wind turbine components, their practical application in wind farm operations and maintenance (O&M) remains limited, significantly diminishing their value. The reasons for this situation are not merely technical issues, rather, a complex interplay of various factors besides the condition monitoring algorithms themselves. The purpose of the paper is to investigate these reasons and propose a potential solution. Through in-depth analysis, two gaps are identified. The first gap is the Data-Model Gap, in which operational wind turbine data do not adequately satisfy the assumptions and quality requirements of condition monitoring algorithms. The second is the Model-Decision Gap, where the CMS output fails to provide sufficient information to support maintenance decisions. Based on this, a Data-Model-Decision Gap (DMDG) framework is further developed to explain how maintenance requirements can be used to determine the CMS outputs and, subsequently, the wind turbine condition monitoring data that are needed to produce these CMS outputs. Such a CMS design logic reverses the usual data-to-model development sequence by starting from understanding what kind of CMS outputs can actually support decision-making in practical wind farm O&M. This new CMS design approach emphasizes the alignment of data, models, and decision processes through an iterative and integrated design strategy. Research findings indicate that merely improving algorithm accuracy is insufficient to enhance the practical value of CMS. Bridging the aforementioned gaps is the key to transforming the CMS design from “monitoring-oriented” to “decision-oriented” and in the end, providing “prescriptive maintenance solutions”. This change is critical for unlocking the full potential of CMS in wind farm O&M.

Terahertz (THz) measurement-based parameter reconstruction and defect identification can support integrity assessment of multilayer composites in safety-critical structures. Reliable THz inversion nevertheless remains difficult because multiple physical parameters are usually strongly coupled and the reconstructed quantities are uncertain. This study proposes an uncertainty-aware physics-informed decoupled inversion method. A differentiable electromagnetic model constrains hybrid neural and parametric representations through measured responses. Sensitivity analysis guides parameter grouping and staged inversion, with larger weights assigned to frequencies that better discriminate the active parameter group. Conditional Bayesian inference propagates upstream uncertainty, while reliability-based covariance adjustment regulates its influence on later stages. A matched-model four-layer reflection benchmark demonstrates accurate material and geometry reconstruction and localization of prescribed defects. Evaluation on measured polymer-pellet transmission data further validates thickness reconstruction, with all holdout reference thicknesses contained within the adjusted nominal 95% credible intervals. The method provides physics-consistent parameter estimates, uncertainty characterization, and probabilistic defect decisions for coupled THz inverse problems.

The relentless pursuit of next-generation photonic systems for advanced information processing and terahertz-scale wireless communications necessitates unprecedented precision in arbitrary electromagnetic wave manipulation. By engineering the local phase of individual meta-atoms, metasurfaces enable the precise control of multidimensional optical parameters. Nevertheless, conventional meta-atom design paradigms, heavily reliant on iterative trial-and-error processes, are computationally intensive and resource-demanding. Herein, we propose a task-specific, multifunctional lightweight deep neural network that integrates forward and inverse design modules. This framework enables efficient and accurate exploration within the function space and parameter space. It successfully reconciles the inherent trade-off between computational efficiency and prediction accuracy, thereby enabling a combinatorial inverse design paradigm. This approach, rooted in functional modularity, significantly enhances the efficiency and flexibility of developing highly integrated, multifunctional optical devices. To address the inherent data dependency, we implement a data distillation strategy that reduces the required dataset to just 20% of its original volume while simultaneously enhancing design precision by 12.06%. As a proof of concept, we demonstrate the entire design and validation process of the polarization-multiplexed metalens and vortex-generation metasurfaces. This versatile approach could be readily extended to develop other types of wavefront modulation devices, heralding a new era of customizable and multifunctional meta-devices.

Extending dual‐ or multi‐band wave plates to the terahertz regime remains challenging due to the complex design of high‐efficiency metasurfaces and the scarcity of scalable, low‐cost fabrication techniques. Here, we overcome this limitation by demonstrating a dual‐band metasurface‐based half‐wave plate (HWP) with an out‐of‐plane triple‐bar design, where single‐ and double‐bar pillars are vertically stacked at distinct heights within a single meta‐atom. Simulations reveal that the low‐frequency band is primarily governed by magnetic resonances of the single‐bar pillar, while the high‐frequency band arises from hybrid magnetic‐electric resonances involving the double‐bar pillars. The HWPs are fabricated using a simple projection micro‐stereolithography (PµSL) 3D printing technique. Experimental results show a polarization conversion ratio exceeding 90% across 0.33–0.78 THz (fractional bandwidth, FBW = 81.1%) and 1.29–1.81 THz (FBW = 33.5%). Moreover, by simply tuning the pillar height, the same platform yields quarter‐wave plate behavior with ellipticity exceeding 0.8 across 0.49–0.83 THz (FBW = 51.5%) and below ‐0.8 across 1.14–1.85 THz (FBW = 47.5%). This out‐of‐plane metasurface approach, combined with a simple 3D printing process, establishes a scalable paradigm for high‐performance, low‐cost, and compact THz wave plates.

Mechanically reconfigurable metasurfaces have been designed to achieve continuous modulation of electromagnetic responses through the integration of deformable elements with structurally simple architectures. Although current implementations with independently addressable deformable units enable multifunctionality, their constrained phase modulation range imposes fundamental limitations on achieving fully reprogrammable metasurfaces. Here, we report for the first time a 1-bit phase-coding concept of a deformable unit based on the controllable interference of two three-dimensional (3D) bistable antennas, while maintaining superior broadband performance. By employing a hollow hyper-elastic substrate, we create a reliable platform for 3D buckling and shape switching of antennas, where out-of-plane loadings applied to each unit allow programming of the desired phase patterns. A reprogrammable metasurface prototype is meticulously designed and fabricated, demonstrating reliable, repeatable, and arbitrary functionality-switching capabilities. This concept establishes a robust foundation for mechanically reprogrammable metasurfaces and opens broader possibilities for applications in adaptive imaging, mechanical sensing, cloaking, information processing, and beyond.

Coupled mode theory (CMT) is a universal method for studying resonant systems in various disciplines in science. Combined with traditional fitting methods, implicit physical parameters of the resonant systems can be revealed. However, this methodology fails in tackling the scenario of multi-solution for a given resonant system, resembling a fundamental challenge that has not been addressed yet. In this work, we propose and experimentally demonstrate a CMT physics and data co-driven deep neural network (CMT-NN) that can predict the implicit physical parameters of complex resonant systems in a rapid and precise way. More importantly, the challenge of multi-solution is mitigated by incorporating physical eigenvalues and response of the system in evaluating the physics consistency of the neural network. The applicability and generality of CMT-NN are demonstrated by simulations and experiments, where the CMT-NN can capture subtle spectral features and learn the coupling physical properties effectively. Compared with the traditional fitting method, the average computation time has been reduced by three orders of magnitude and the prediction performance is improved by more than two orders of magnitude. Displacement sensing experiments further validate the robustness of CMT-NN. It is anticipated that the CMT-NN can provide a paradigm shift in using the CMT for studying resonant systems and shed new light on the understanding, design and optimization of various coupled resonant systems.