This article reports the fabrication and wetting characteristics of 3-dimensional nanostructured fractal surfaces (3DNFS). Three distinct 3DNFS surfaces, namely cubic, Romanesco broccoli, and sphereflake were fabricated using two-photon direct laser writing. Contact angle measurements were performed on the multiscale fractal surfaces to characterize their wetting properties. Average contact angles ranged from 66.8° for the smooth control

Minimally invasive therapies avoiding surgical complexities evoke great interest in developing injectable biomedical devices. Herein, a versatile approach is reported for engineering injectable and biomimetic nanofiber microspheres (NMs) with tunable sizes, predesigned structures, and desired compositions via gas bubble–mediated coaxial electrospraying. The sizes and structures of NMs are controlled by adjusting processing parameters including air flow rate, applied voltage, distance, and spinneret configuration in the coaxial setup. Importantly, unlike the self‐assembly method, this technique can be used to fabricate NMs from any material feasible for electrospinning or other nanofiber fabrication techniques. To demonstrate the versatility, open porous NMs are successfully fabricated that consist of various short nanofibers made of poly(ε‐caprolactone), poly(lactic‐co‐glycolic acid), gelatin, methacrylated gelatin, bioglass, and magneto‐responsive polymer composites. Open porous NMs support human neural progenitor cell growth in 3D with a larger number and more neurites than nonporous NMs. Additionally, highly open porous NMs show faster cell infiltration and host tissue integration than nonporous NMs after subcutaneous injection to rats. Such a novel class of NMs holds great potential for many biomedical applications such as tissue filling, cell and drug delivery, and minimally invasive tissue regeneration.

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The primary goal of this article is to measure the wetting characteristics of a low melting point metal to determine the efficacy of this type of material for possible use in thermal energy storage applications. Galinstan 1 , a commercially available alloy consisting of Gallium, Indium, and Tin is subjected to contact angle measurements on various silicon surfaces at varying temperatures. Due to the oxidation characteristics of Galinstan, all experiments are conducted in an inert nitrogen environment (<0.5 ppm oxygen) to maintain fluid-like properties. This work finds that although contact angle changes with substrate and surface structure, temperature has no observable effect on contact angle. Contact angles range from 141 on smooth silicon to greater than 160 on silicon micropillars. Although a temperature dependence is not observed over the range of temperatures studied, having wetting properties of Galinstan on various surfaces is a step toward better understanding the capabilities of this and similar materials in energy management.

Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM) to capture complex temporal changes of the time sequence and make multi-step predictions. Specifically, we first present the framework of LSTM-based anomaly detection in non-linear dynamical system, including data preprocessing, multi-step prediction and anomaly detection. According to the prediction requirement, two types of training modes are explored in multi-step prediction, where samples in a wall shear stress dataset are collected by an adaptive sliding window. On the basis of the multi-step prediction result, a Local Average with Adaptive Parameters (LAAP) algorithm is proposed to extract local numerical features of the time sequence and estimate the upcoming anomaly. The experimental results show that our proposed multi-step prediction method can achieve a higher prediction accuracy than traditional method in wall shear stress dataset, and the LAAP algorithm performs better than the absolute value-based method in anomaly detection task.