{"type": "FeatureCollection", "features": [{"id": "10.1103/physrevapplied.20.044047", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:24Z", "type": "Journal Article", "created": "2023-10-18", "title": "Asymmetric spoof-fluid-spoof acoustic waveguide and its application as a  CO2  sensor", "description": "Open AccessWe study pressure acoustic propagation in asymmetric spoof-fluid-spoof acoustic waveguides and its potential application in acoustic gas sensors. First, a stable and efficient analytical method is established for fast calculation of the dispersion curves based on spectral expansion and enforcement of continuity between segments at suitable collocation points. The analysis is validated by a commercial finite element software. The geometric design of the waveguide is then optimized for the emergence of a nearly-flat dispersion curve associated with vertical geometric asymmetry. The waveguide is fabricated using 3D printing technology and the measurement results corroborate the numerical simulations. Based on the nearly-flat dispersion curve supported by this waveguide, a CO$_2$ sensor is proposed allowing to relate the phase difference measured between two points in the waveguide to the composition of the gas in the waveguide. The proposed sensor is experimentally validated in a controlled environment and the measurement results match the computational predictions well. The sensor is robust with respect to noise and signal-recording duration due to fast phase measurements and shows high sensitivity to gas concentration due to reliance on the second, nearly-flat, dispersion curve. 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Such high dimensional observations can be naturally encoded as tensors, exhibiting significant redundancies across dimensions. This property is exploited by tensor decomposition techniques that are being increasingly used for compactly encoding large multidimensional arrays. While efficient, these methods are incapable of utilizing prior information present in training data. In this paper, a novel tensor decomposition learning method is proposed for the compression of high dimensional signals. Specifically, instead of extracting independent bases for each example, our method learns an appropriate basis for each dimension from a set of training samples by solving a constrained optimization problem. As such, each sample is quantized and encoded into a reduced-size core tensor of coefficients that corresponds to the multilinear combination of the learned basis matrices. Furthermore, the proposed method employs a symbol encoding dictionary for binarizing the decomposition outputs. 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The growth model is explicitly parameterised with SRXCT data and simulates three-dimensional root hair ideotypes in silico, which are suitable for both ideotypic analysis and parameterisation of 3D geometry in mathematical models. The study considers different hypothetical conditions governing root hair interactions with soil matrices, with their respective effects on hair morphology being compared between idealised and image-derived soil/root geometries. The studies in idealised geometries suggest that packing arrangement of soil affects hair tortuosity more than the particle diameter. Results in field-derived soil suggest that hair access to poorly mobile nutrients is particularly sensitive to the physical interaction between the growing hairs and the phase of the soil in which soil water is present (i.e. the hydrated textural phase). 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