Long-term observation of the triboelectric effect has not only proved the feasibility of many novel and useful tribo-devices (e.g., triboelectric nanogenerators), but also constantly motivated the exploration of its mysterious nature. In the pursuit of a comprehensive understanding of how the triboelectric process works, a more accurate description of the triboelectric effect and its related parameters and factors is urgently required. This review critically goes through the fundamental theories and basic principles governing the triboelectric process. By investigating the difference between each charging media, the electron, ion, and material transfer is discussed and the theoretical deduction in the past decades is provided. With the information from the triboelectric series, interesting phenomena including cyclic triboelectric sequence and asymmetric triboelectrification are precisely analyzed. Then, the interaction between the tribo-system and its operational environment is analyzed, and a fundamental description of its effects on the triboelectric process and results is summarized. In brief, this review is expected to provide a strong understanding of the triboelectric effect in a more rigorous mathematical and physical sense.

It is a great challenge to achieve robustly bonded, fully covered, and nanoscaled coating on the surface of electrospun nanofibers. Herein, we develop a controllable, facile, and versatile strategy to in-situ grow superlubricated nano-skin (SLNS) on the single electrospun nanofiber. Specifically, zwitterionic polymer chains are generated from the nanofiber subsurface in an inside-out way, which consequently form a robust network interpenetrating with the polymeric chains of the nanofiber matrix. The nanofibers with SLNS are superlubricated with the coefficient of friction (COF) lower than 0.025, which is about 16-fold of reduction than the original nanofibers. The time-COF plot is very stable after 12, 000 cycles of friction test, and no abrasion is observed. Additionally, the developed nanofibrous membranes possess favorable tensile property and biocompatibility. Furthermore, the nanofibrous membranes with SLNS achieve prevention of post-operative adhesion, which is confirmed in both rat tendon adhesion model and abdominal adhesion model. Compared with clinically-used antiadhesive membranes such as Interceed and DK-film, our nanofibrous membranes are not only more effective but also have the advantage of lower production cost. Therefore, this study demonstrates a potential of the superlubricated nanofibrous membranes in-situ grown based on a SLNS strategy for achieving prevention of post-operative adhesion in clinics.

With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of “tribo-informatics” as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of “AI for tribology”. In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (T_s), and derived state information (D_s). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.

Tribology research mainly focuses on the friction, wear, and lubrication between interacting surfaces. With the continuous increase in the industrialization of human society, tribology research objects have become increasingly extensive. Tribology research methods have also gone through the stages of empirical science based on phenomena, theoretical science based on models, and computational science based on simulations. Tribology research has a strong engineering background. Owing to the intense coupling characteristics of tribology, tribological information includes subject information related to mathematics, physics, chemistry, materials, machinery, etc. Constantly emerging data and models are the basis for the development of tribology. The development of information technology has provided new and more efficient methods for generating, collecting, processing, and analyzing tribological data. As a result, the concept of “tribo-informatics (triboinformatics)” has been introduced. In this paper, guided by the framework of tribo-informatics, the application of tribo-informatics methods in tribology is reviewed. This article aims to provide helpful guidance for efficient and scientific tribology research using tribo-informatics approaches.

Metal matrix nanocomposites (MMNCs) become irreplaceable in tribology industries, due to their supreme mechanical properties and satisfactory tribological behavior. However, due to the dual complexity of MMNC systems and tribological process, the anti-friction and anti-wear mechanisms are unclear, and the subsequent tribological performance prediction and design of MMNCs are not easily possible: A critical up-to-date review is needed for MMNCs in tribology. This review systematically summarized the fabrication, manufacturing, and processing techniques for high-quality MMNC bulk and surface coating materials in tribology. Then, important factors determining the tribological performance (mainly anti-friction evaluation by the coefficient of friction (CoF) and anti-wear assessment with wear rate) in MMNCs have been investigated thoroughly, and the correlations have been analyzed to reveal their potential coupling/synergetic roles of tuning tribological behavior of MMNCs. Most importantly, this review combined the classical metal/alloy friction and wear theories and adapted them to give a (semi-)quantitative description of the detailed mechanisms of improved anti-friction and anti-wear performance in MMNCs. To guarantee the universal applications of these mechanisms, their links with the analyzed influencing factors (e.g., loading forces) and characteristic features like tribo-film have been clarified. This approach forms a solid basis for understanding, predicting, and engineering MMNCs’ tribological behavior, instead of pure phenomenology and experimental observation. Later, the pathway to achieve a broader application for MMNCs in tribo-related fields like smart materials, biomedical devices, energy storage, and electronics has been concisely discussed, with the focus on the potential development of modeling, experimental, and theoretical techniques in MMNCs’ tribological processes. In general, this review tries to elucidate the complex tribo-performances of MMNCs in a fundamentally universal yet straightforward way, and the discussion and summary in this review for the tribological performance in MMNCs could become a useful supplementary to and an insightful guidance for the current MMNC tribology study, research, and engineering innovations.