Photoresponsive smart actuators based on carbon materials are attracting increasing attention. However, the low content of carbon materials currently limits the development of carbon material actuators. In this work, we designed and prepared a multifunctional bilayer composite actuator with controllable structures and high photothermal conversion efficiency. The actuator consists of a graphene/polydimethylsiloxane (PDMS) composite layer and a PDMS layer. With an ultrahigh graphene mass fraction (30%), the actuator exhibits a good hydrophobicity, unexpectedly high photothermal conversion performance (from room temperature to 120 °C within 1 s), and rapid photo-response capability. By thermal regulation, ultraviolet laser cutting, and assembly, the actuator can achieve shape programmable configuration in three-dimensional directions. Bionic crawling robots achieve a crawling speed of 0.065 mm/s, and liquid tracking robots achieve a rotational motion of 106°/s, a linear motion of 8.42 mm/s, and a complex “W”-shaped trajectory motion. This work provides a simple and effective method for the preparation and realization of multifunctional actuators based on graphene composite materials.

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Bilayer graphene (BG) may achieve better optoelectronic and sensing applications after modification due to its direct induced band gap and better plasma processing stability. Defect engineering based on oxygen plasma treatment has been proved to be an effective method to achieve the modification of graphene. Understanding the formation and evolution of graphene defects, which cannot be observed directly by the experiment, can help achieve more precise and controllable modification of graphene. Here, we investigate the effect of oxygen plasma treatment on the structure and mechanical properties of BG by molecular dynamics simulations. We report two mechanisms of BG destruction by oxygen plasma: direct sputtering and cascade sputtering. When the defects are mainly sp3 defects, we report that the elastic modulus is strongly related to oxygen density, and it decreases by only about 16% at low oxygen density (3.92 × 1021 atoms·cm–3). In contrast, the mechanical properties of BG decrease more significantly when vacancy defects are predominant. Based on the formation and evolution of sp3 defects and vacancy defects, we establish the relationship between defect types and the mechanical properties of BG. This study makes it possible to precisely control the graphene structure for optoelectronic and sensing applications.

Accurately predicting the mechanical properties of graphene-reinforced metal matrix composites is of utmost importance due to its critical role in the design and utilization of nanocomposite materials. The conventional approach of employing molecular dynamics (MD) simulations for this purpose faces a substantial increase in computational costs when considering the combined effects of multiple factors. In contrast, machine learning (ML) models offer a rapid and efficient alternative by swiftly comprehending and predicting material properties following adequate training. In this paper, we employed a long short-term memory (LSTM) model, based on MD calculation data, to accurately predict the mechanical response and key mechanical properties of nickel–graphene composite nanomaterials. Specifically, we thoroughly investigated the comprehensive impact of temperature, graphene orientation angle, and graphene volume fraction on the mechanical properties. Our verification process revealed that high graphene volume and high orientation angles led to increased dislocation absorption, consequently weakening the composite material. To assess the hardness prediction capabilities, we conducted a comparative analysis between the LSTM model and classical multilayer perceptron (MLP) neural networks, as well as the traditional nonlinear regression method, support vector machine (SVM). The obtained results demonstrated that the LSTM models exhibited a remarkable ability to accurately predict the mechanical properties of nickel–graphene composite nanomaterials, showcasing Pearson correlation coefficients exceeding 0.95 when compared to the calculation data. Moreover, the LSTM model effectively comprehends and predicts the complete indentation depth–force curve, thus providing enhanced predictions of material properties. This study proposes an innovative combination of MD simulations and ML models, which holds significant application potential in predicting and designing the performance of graphene-reinforced metal matrix composite materials.