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Abstract Electromagnetic hyperbolicity has driven key functionalities in nanophotonics, including super-resolution imaging, efficient energy control, and extreme light manipulation. Central to these advances are hyperbolic polaritons—nanometer-scale light-matter waves—spanning multiple energy-momentum dispersion orders with distinct mode profiles and incrementally high optical momenta. In this work, we report the mode conversion of hyperbolic polaritons across different dispersion orders by breaking the structure symmetry in engineered step-shape van der Waals (vdW) terraces. The mode conversion from the fundamental to high-order hyperbolic polaritons is imaged using scattering-type scanning near-field optical microscopy (s-SNOM) on both hexagonal boron nitride (hBN) and alpha-phase molybdenum trioxide (α-MoO₃) vdW terraces. Our s-SNOM data, augmented with electromagnetics simulations, further demonstrate the alteration of polariton mode conversion by varying the step size of vdW terraces. The mode conversion reported here offers a practical approach toward integrating previously independent different-order hyperbolic polaritons with ultra-high momenta, paving the way for promising applications in nano-optical circuits, sensing, computation, information processing, and super-resolution imaging. 

Abstract MotivationCell communication is predominantly governed by secreted proteins, whose diverse secretion patterns often signify underlying physiological irregularities. Understanding these secreted signals at an individual cell level is crucial for gaining insights into regulatory mechanisms involving various molecular agents. To elucidate the array of cell secretion signals, which encompass different types of biomolecular secretion cues from individual immune cells, we introduce the secretion-signal map (S2Map). ResultsS2Map is an online interactive analytical platform designed to explore and interpret distinct cell secretion-signal patterns visually. It incorporates two innovative qualitative metrics, the signal inequality index and the signal coverage index, which are exquisitely sensitive in measuring dissymmetry and diffusion of signals in temporal data. S2Map’s innovation lies in its depiction of signals through time-series analysis with multi-layer visualization. We tested the SII and SCI performance in distinguishing the simulated signal diffusion models. S2Map hosts a repository for the single-cell’s secretion-signal data for exploring cell secretio-types, a new cell phenotyping based on the cell secretion signal pattern. We anticipate that S2Map will be a powerful tool to delve into the complexities of physiological systems, providing insights into the regulation of protein production, such as cytokines at the remarkable resolution of single cells. Availability and implementationThe S2Map server is publicly accessible via https://au-s2map.streamlit.app/. 

The mid‐infrared with a characteristic wavelength of 3–20 μm is important for a wealth of technologies. In particular, mid‐infrared spectroscopy can reveal material composition and structure information by fingerprinting chemical bonds’ infrared resonances. Despite these merits, state‐of‐the‐art mid‐infrared techniques are spatially limited above tens of micrometers due to the fundamental diffraction law. Herein, recent progress in the scanning probe nanoscale infrared characterization of biochemical materials and natural specimens beyond this spatial limitation is reviewed. By leveraging the strong tip–sample local interactions, scanning probe nano‐infrared methods probe nanoscale optical and mechanical responses to disclose material composition, heterogeneity, orientation, fine structure, and phase transitions at unprecedented length scales. These advances, therefore, revolutionize the understanding of a broad range of biochemical and natural materials and offer new material manipulation and engineering opportunities close to the ultimate length scales of fundamental physical, chemical, and biological processes. 

Block polymers are an attractive platform for uncovering the factors that give rise to self-assembly in soft matter owing to their relatively simple thermodynamic description, as captured in self-consistent field theory (SCFT). SCFT historically has found great success explaining experimental data, allowing one to construct phase diagrams from a set of candidate phases, and there is now strong interest in deploying SCFT as a screening tool to guide experimental design. However, using SCFT for phase discovery leads to a conundrum: How does one discover a new morphology if the set of candidate phases needs to be specified in advance? This long-standing challenge was surmounted by training a deep convolutional generative adversarial network (GAN) with trajectories from converged SCFT solutions, and then deploying the GAN to generate input fields for subsequent SCFT calculations. The power of this approach is demonstrated for network phase formation in neat diblock copolymer melts via SCFT. A training set of only five networks produced 349 candidate phases spanning known and previously unexplored morphologies, including a chiral network. This computational pipeline, constructed here entirely from open-source codes, should find widespread application in block polymer phase discovery and other forms of soft matter.