It has been gradually realized that the sustainable electronic devices are of great prospects for sustainable applications in electronic technology products. From another point of view, a device with multiple physical performances would be a potential candidate for next generation multifunctional electronics. In this report, a flexible memory device based on sweet potato skin (SPS) was demonstrated, which presented a stable memory performance over 500 cycles and a large switching resistance ratio, and the current-voltage (I-V) hysteretic loop accompanied by capacitance effect and negative differential resistance state. In order to carry out detailed mechanism analysis, two materials (ITO or Ti) with different work functions as the bottom electrode and Ag as the top electrode were used to prepare two memristive devices. It can be confirmed that the conductive behavior obeys Schottky emission and direct tunneling at low voltage region, while it follows the hopping conduction at high voltage region. In a word, the selected SPS, a sustainable waste material, can be employed to prepare bioelectronic devices, which has a potential application value in green electronic products.

Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation. To provide a comprehensive understanding of thermal error modeling and its advancements, this paper systematically reviews machine learning-based methods for thermal error compensation. Thermal error modeling is the most critical step in thermal error compensation, as it directly influences the effectiveness of the compensation due to its accuracy and robustness. With the rapid development of big data and artificial intelligence, machine learning has emerged as a powerful tool in thermal error modeling, leading to significant research progress in recent years. In this paper, an overview of the thermal error modeling methods based on deep learning that have been researched and applied in recent years is presented. Specifically, two methods for reducing thermal errors, namely, thermal error suppression and thermal error compensation, are introduced and analyzed. Second, machine learning-based thermal error modeling methods are categorized into traditional machine learning-driven and deep learning-driven approaches. The application of these two methods in thermal error modeling and compensation is reviewed and summarized in detail. By synthesizing these studies, this paper identifies key challenges and trends in machine learning-based thermal error modeling. Finally, the thermal error modeling methods discussed in this paper are summarized, and future research directions are proposed to further enhance modeling accuracy and robustness.

It is the consensus of researchers that the reuse of natural resources is an effective way to solve the problems of environmental pollution, waste and overcapacity. Moreover, compared with the case of inorganic materials, the renewability of natural biomaterials has great prominent advantages. In this study, keratin, which was first extracted from hair due to its high content in hair, was chosen as a functional layer for the fabrication of a resistance switching device with the Ag/keratin/ITO structure; in this device, a stable resistive switching memory behavior with good retention characteristic was observed. Via mechanism analysis, it is expected that there is hopping conduction at low biases, and the formation of a conductive filament occurs at high biases. Furthermore, our device exhibited a stable switching behavior with different conductive materials (Ti and FTO) as bottom electrodes, and the influence of Ag and graphite conductive nanoparticles (NPs) doped into the keratin layer on the switching performance of the device was also investigated. This study not only suggests that keratin is a potential biomaterial for the preparation of memory devices, but also provides a promising route for the fabrication of bio-electronic devices with non-toxicity, degradability, sustainability etc.

Wireless sensor networks play a very important role in environmental monitoring, structural health monitoring, smart city construction, smart grid, and ecological agriculture. The wireless sensor nodes powered by a battery have a limited service life and need periodic maintenance due to the limitation of battery capacity. Fortunately, the development of environmental energy harvesting technology provides an effective way to eliminate the needs and the replacement of the batteries. Among the environmental stray energy, wind energy is rich, almost endless, widely distributed, and clean. Due to the advantages of simple structure, miniaturization, and high power density, wind energy harvesters using piezoelectric materials (PWEHs) have attracted much attention. By the ways of principal exploration, structure design, and performance optimization, great and steady progress has been made in the research of PWEH. This Review is focused on the review of PWEHs. After introducing the basic principle of PWEHs, the structural performance and research status of PWEHs based on different mechanisms, such as a rotating turbine, vortex-induced vibration, flutter, and galloping, are analyzed and summarized. Finally, the development trend of PWEHs has been prospected.