Hydrogel-based plant bioelectronics are emerging as promising platforms for real-time monitoring and modulation of plant physiology, stress responses, environmental interactions, and growth. Compared with rigid electrodes and conventional polymer films, hydrogels provide a soft, hydrated, conductive, and tunable interface that reduces mechanical mismatch with growing plant tissues while enabling electrochemical, electrophysiological, optical, and multimodal sensing. This review examines recent advances in hydrogel materials for plant bioelectronics, focusing on how network structure, design requirements, materials strategies including crosslinking chemistry, porosity, swelling, adhesion, conductivity, transparency, gas permeability, and biocompatibility affect plant-device performance. Applications in monitoring plant physiology, hormones, pH, moisture, glucose, and overall plant health are highlighted. Reported hydrogel systems exhibit Young’s moduli from ∼ 1 kPa to several MPa and ionic conductivities of 10−3-10−1 S cm−1. Several plant-interfacing devices sustain strains above 300 %, maintain stable electrical performance over 10,000 loading cycles, and support continuous growth monitoring for up to 14 days. Despite these advances, standardised evaluation under realistic agricultural conditions remains limited. Future research should prioritise standardised testing, biodegradable biomass-derived materials, multimodal sensing integration, and closed-loop bioelectronic systems to advance precision agriculture and bio-regenerative life-support applications.
Abstract Coastal wetland erosion is a significant environmental concern along the Texas coast with various contributing factors. This paper evaluates wave activity at four coastal field sites near the Gulf Intracoastal Waterway (GIW) in Galveston Bay and quantifies the contributions of hurricanes, front events, and ship wake dynamics to the wetland boundary changes through eventful and seasonal field measurement campaigns. Utilizing the field hydrodynamic data, the wave-energy flux during the time of a Category 1 hurricane event (Hurricane Nicholas), prevailing warm or cold fronts, and ship wake dynamics are estimated to represent the erosion potential of wetland edges. The results revealed that Hurricane Nicholas contributed 13.3% of the wave-energy flux throughout the year (August 2021 to July 2022), warm or cold front events constituted 54.2%, and 11.2% were produced from ship wake hydrodynamics. The hurricane event exhibited the highest wave energy per occurrence, while front events led to the highest total wave energy throughout the year. Because of the typical shallow bay environment and heavy maritime traffic (measured at an average of 26 barge transits per day) along the channel, the total wave energy induced by vessel wakes accounts for a significant portion and needs to be considered when developing management strategies for the GIW.
Reconstructing a coupled multi-physics state from sparse and heterogeneous measurements is central to real-time monitoring and digital twinning, yet it is challenging when only a subset of fields is observable and sensors operate over field-dependent regions. This work introduces the Multi-ield Generalized Empirical Interpolation Method, which extends the Generalized Empirical Interpolation Method to product spaces by treating the full coupled state as a single element of a multi-field Hilbert space while allowing measurements to be selected across multiple fields and sensing modalities. In the offline phase, the Multi-field Generalized Empirical Interpolation Method constructs a reduced basis and a corresponding set of measurement functionals through a greedy procedure that (i) simultaneously identifies the global basis function and the field to be sensed and (ii) improves numerical robustness by applying an explicit scaling factor to each selected measurement functional. In the online phase, the method reconstructs all fields, including unmeasured ones, by solving a small interpolation system from noisy measurements. Global and field-wise stability measures (Lebesgue constants) and trace-based noise-amplification indicators are also introduced to provide an exact characterization of the expected mean-square contribution of Gaussian measurement perturbations. Numerical experiments on a two-dimensional molten salt reactor benchmark demonstrate accurate reconstruction under realistic observability constraints and quantify the trade-off between reduced-space approximation and noise sensitivity.
In this article, convex optimization is introduced as a promising tool to study Eshelby based inverse micromechanics problems. The focus is on inverse micromechanics using the Mori–Tanaka model given the dielectric constants of the composite material and of all of its components. The model is exactly the same for the conductivity properties (thermal and electrical) as well. This choice of model is made since the model is fairly simple, has a closed form analytical solution, and is known to perform well for the case of spheroidal inclusions as well. The forward or direct micromechanics problem deals with the determination of effective properties of a composite material given the properties of its components and microstructural information. The focus is on isotropic composites, and the distribution of inclusions is assumed to be such that this holds. The inverse micromechanics problem considered in this paper deals with the determination of microstructural information given the properties of the composite material and all of its components. Since in this paper the isotropy of the composite and only spherical inclusions are considered, the goal is to determine only the volume fractions of the components of the composite material. The inverse problem is formulated as a Linear Programming problem and is solved. Before this, the inverse problem and certain important variants of it are examined through the lens of convex optimization. Lastly, promising results are presented on the relationship between dispersive materials, noise in measurements, and the quality of the obtained volumetric splits. The scope of the use of convex optimization in inverse micromechanics is discussed.
The intrinsic features of spent nuclear fuel (SNF) could enhance the proliferation resistance (PR) of plutonium (Pu) to minimize proliferation risk. One key intrinsic PR feature is the presence of the isotope 238Pu in Pu. Increased 238Pu content helps minimize proliferation risk owing to its high spontaneous fission neutron (SFN) emission rate and decay heat (DH). This study focused on the PR assessment of reprocessed uranium (RepU) that is recycled multiple times as fuel in nuclear reactors. The growth of the uranium (U) isotope,236U, in SNF and its co-enrichment while re-enriching RepU resulted in the enhanced production of 238Pu supporting the intrinsic PR of Pu. In addition to enhancing the intrinsic PR of Pu, the presence of 236U in SNF denatures U, which enables the minimization of the proliferation risk of U to a large extent because it limits the 235U enrichment.
