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The demand for stretchable strain sensors with customizable sensitivities has increased across a spectrum of applications, spanning from human motion detection to plant growth monitoring. Nevertheless, a major challenge remains in the digital fabrication of scalable and cost‐efficient strain sensors with tailored sensitivity to diverse demands. Currently, there is a lack of simple digital fabrication approaches capable of adjusting strain sensitivity in a controlled way with no changes to the material and without affecting the linearity. In this study, parallel microgates‐based strain sensors whose strain sensitivity can be adjusted systematically throughout an all‐laser‐based fabrication process without any material replacement are presented. The technique employs a two‐step direct laser writing method that combines the well‐established capabilities of laser ablation and laser marking, boasting a varying gauge factor of up to 433% (GF = 168), while paving the way for the mass production of nanocomposite strain sensors. Parallel microgates‐based strain sensors exhibit a remarkable signal‐to‐noise ratio at ultralow strains (ɛ = 0.001), rendering them ideal for monitoring the gradual growth of plants. As an application demonstration, the proposed sensors are deployed on tomato plants to capture their growth under varying planting conditions including hydroponic and soil mediums, as well as diverse irrigation regimens. [ABSTRACT FROM AUTHOR]

Ice buildup can significantly and negatively impact system performance in various industrial sectors, and has remained a persistent challenge for decades. Many compliant materials exhibit excellent de‐icing performance but are easily eroded by impacts from supercooled water droplets, sand, dust, and debris. A composite panel inspired by animal skin, consisting of a facesheet protecting a nanofluid layer beneath, which exhibits durable anti‐icing and tunable photothermal properties is proposed. The viscous liquid layer beneath the facesheet increases flexural rigidity, preventing large deflections and increasing deformation resistance, which alters ice's adhesion to the surface. The non‐uniform fluid pressure exerted by the viscous nanofluid‐filled composite panels facilitates ice detachment, resulting in ice adhesion strengths as low as τice ≈ 10 kPa. Further, by altering the fluid properties, different additional functionalities can be endowed to the system. Incorporating fumed silica in a fluid‐filled composite panel results in rheopectic behavior, and this doubles their impact resistance when the shear thickening properties are properly tuned. Additionally, the combination of a transparent facesheet and a solar light absorbent nanofluid allows for tunable photothermal properties, further enhancing the anti‐icing performance of the system. This durable and tunable nanofluid‐filled composite panel shows great promise as a multifunctional de‐icing material. [ABSTRACT FROM AUTHOR]

This paper presents a study on the effect of unit-cell geometrical parameters in predicting elastic properties of a typical wood plastic composite (WPC). The ultimate goal was obtaining the optimal values of representative volume element (RVE) parameters to accurately predict the mechanical behavior of the WPC. For each unit cell, defined by a given combination of the above geometrical parameters, finite element simulation in ABAQUS was carried out, and the corresponding stress-strain curve was obtained. A uniaxial test according to ASTM D638-02a type V was performed on the composite specimen. Modulus of elasticity was determined using hyperbolic tangent function, and the results were compared to the sets of finite element analyses. Main effects of RVE parameters and their interactions were demonstrated and discussed, specially regarding the inclusion of two adjacent wood particles within one unit cell of the material. Regression analysis was performed tomathematicallymodel the RVE parameter effects and their interactions over themodulus of elasticity response. The model was finally employed in an optimization analysis to arrive at an optimal set of RVE parameters that minimizes the difference between the predicted and experimental moduli of elasticity. [ABSTRACT FROM AUTHOR]