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We report what we believe to be a novel and unique approach for achieving high-performance and broadband THz phase shifting based on spatially-resolved photoconductivity modulation (SRPM). By changing the illumination area on a hybrid Au-Ge mesa-array (AGMA) structure in front of an indium tin oxide (ITO) layer for local photoconductivity modulation, the phase difference between the incident- and reflected-waves can be tuned nearly continuously with extremely low reflection loss. For a prototype demonstration, a photonically-driven THz phase shifting device based on the WR-5.1 (140-220 GHz) waveguide configuration was designed, modeled and simulated. To achieve phase tuning in the range of 0° to -180° at 180 GHz (band center frequency), a mesa-array consisting of 12 × 6 unit cells (each 105 μm × 105 μm) was designed, and a distancedof 250 μm between the AGMA and ITO was used. The SRPM is accomplished using computer-generated light patterns from a closely-coupled micro-LED array for through-ITO illumination, without the need for any biasing circuitry. Full wave simulation results have shown that pseudo-continuous and broadband phase shifting can be achieved in the entire WR-5.1 band, and a shifting range of 0° to -180° at 180 GHz can be realized as designed. In addition, by using light patterns of different combinations of vertical strips, a fine phase tuning step as small as ∼0.05° can be demonstrated. For all phase tuning states, the simulated reflection loss is generally less than 1 dB with low loss variation. The proposed technology for high-performance THz phase modulation is promising and powerful, while offering far more design flexibility and frequency scalability than the current state-of-the-art since it requires no biasing wires thus eliminating parasitic-related performance degradation. Therefore, this technology is suitable for the development of large-scale THz phased-arrays, reconfigurable reflectarrays, and tunable metasurfaces for dynamic beam steering/forming required in next generation (6G or beyond) wireless communications. 

Ensuring high-quality prints in additive manufacturing is a critical challenge due to the variability in materials, process parameters, and equipment. Machine learning models are increasingly being employed for real-time quality monitoring, enabling the detection and classification of defects such as under-extrusion and over-extrusion. Vision Transformers (ViTs), with their global self-attention mechanisms, offer a promising alternative to traditional convolutional neural networks (CNNs). This paper presents a transformer-based approach for print quality recognition in additive manufacturing technologies, with a focus on fused filament fabrication (FFF), leveraging advanced self-supervised representation learning techniques to enhance the robustness and generalizability of ViTs. We show that the ViT model effectively classifies printing quality into different levels of extrusion, achieving exceptional performance across varying dataset scales and noise levels. Training evaluations show a steady decrease in cross-entropy loss, with prediction accuracy, precision, recall, and the harmonic mean of precision and recall (F1) scores reaching close to 1 within 40 epochs, demonstrating excellent performance across all classes. The macro and micro F1 scores further emphasize the ability of ViT to handle both class imbalance and instance-level accuracy effectively. Our results also demonstrate that ViT outperforms CNN in all scenarios, particularly in noisy conditions and with small datasets. Comparative analysis reveals ViT advantages, particularly in leveraging global self-attention and robust feature extraction methods, enhancing its ability to generalize effectively and remain resilient with limited data. These findings underline the potential of the transformer-based approach as a scalable, interpretable, and reliable solution to real-time quality monitoring in FFF, addressing key challenges in additive manufacturing defect detection and ensuring process efficiency. 

The inverse design of meta-optics has received much attention in recent years. In this paper, we propose a GPU-friendly inverse design framework based on improved eigendecomposition-free rigorous diffraction interface theory, which offers up to 16.2 × speedup over the traditional inverse design based on rigorous coupled-wave analysis. We further improve the framework’s flexibility by introducing a hybrid parameterization combining neural-implicit and traditional shape optimization. We demonstrate the effectiveness of our framework through intricate tasks, including the inverse design of reconfigurable free-form meta-atoms. 

The flexibility of metal–organic frameworks (MOFs) affects their gas adsorption and diffusion properties. However, reliable force fields for simulating flexible MOFs are lacking. As a result, most atomistic simulations so far have been carried out assuming rigid MOFs, which inevitably overestimates the gas adsorption energy. Here, we show that this issue can be addressed by applying a machine-learning potential, trained on quantum chemistry data, to atomistic simulations. We find that inclusion of flexibility is particularly important for simulating CO2 chemisorption in MOFs with coordinatively unsaturated metal sites. Specifically, we demonstrate that the diffusion of CO2 in a flexible Mg-MOF-74 structure is about one order of magnitude faster than in a rigid one, challenging the rigid-MOF assumption in previous simulations. 

Graphene aerogel (GA), a 3D carbon-based nanostructure built on 2D graphene sheets, is well known for being the lightest solid material ever synthesized. It also possesses many other exceptional properties, such as high specific surface area and large liquid absorption capacity, thanks to its ultra-high porosity. Computationally, the mechanical properties of GA have been studied by molecular dynamics (MD) simulations, which uncover nanoscale mechanisms beyond experimental observations. However, studies on how GA structures and properties evolve in response to simulation parameter changes, which provide valuable insights to experimentalists, have been lacking. In addition, the differences between the calculated properties via simulations and experimental measurements have rarely been discussed. To address the shortcomings mentioned above, in this study, we systematically study various mechanical properties and the structural integrity of GA as a function of a wide range of simulation parameters. Results show that during the in silico GA preparation, smaller and less spherical inclusions (mimicking the effect of water clusters in experiments) are conducive to strength and stiffness but may lead to brittleness. Additionally, it is revealed that a structurally valid GA in the MD simulation requires the number of bonds per atom to be at least 1.40, otherwise the GA building blocks are not fully interconnected. Finally, our calculation results are compared with experiments to showcase both the power and the limitations of the simulation technique. This work may shed light on the improvement of computational approaches for GA as well as other novel nanomaterials. 

Strong adherence to underwater or wet surfaces for applications like tissue adhesion and underwater robotics is a significant challenge. This is especially apparent when switchable adhesion is required that demands rapid attachment, high adhesive capacity, and easy release. Nature displays a spectrum of permanent to reversible attachment from organisms ranging from the mussel to the octopus, providing inspiration for underwater adhesion design that has yet to be fully leveraged in synthetic systems. Here, we review the challenges and opportunities for creating underwater adhesives with a pathway to switchability. We discuss key material, geometric, modeling, and design tools necessary to achieve underwater adhesion similar to the adhesion control demonstrated in nature. Through these interdisciplinary efforts, we envision that bioinspired adhesives can rise to or even surpass the extraordinary capabilities found in biological systems. 

Abstract Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formulated as a sequential decision process, and policy-gradient RL models are employed to maximize the toughness of GO. Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs. In addition, our RL approach reaches optimized functional group distributions within only 5000 rollouts, while the simplest design task has 2 × 10¹¹possibilities. Finally, we show that our approach is scalable in terms of the functional group density and the GO size. The present research showcases the impact of functional group distribution on GO properties, and illustrates the effectiveness and data efficiency of the deep RL approach.