Through experimental study, we reveal superlubricity as the mechanism of self-retracting motion of micrometer sized graphite flakes on graphite platforms by correlating respectively the lock-up or self-retraction states with the commensurate or incommensurate contacts. We show that the scale-dependent loss of self-retractability is caused by generation of contact interfacial defects.A HOPG structure is also proposed to understand our experimental observations, particularly in term of the polycrystal structure. The realisation of the superlubricity in micrometer scale in our experiments will have impact in the design and fabrication of micro/nanoelectromechanical systems based on graphitic materials. Nano-mechanical devices based on van de Waals forces in multi-walled carbon nanotubes (MWCNT) and HOPG (i.e., multilayered graphenes) have attracted intensive experimental and theoretical studies, owing to their superior properties, e.g., the nearly `freely' motion of inner shell inside the outer shell of a MWCNT [1,2,3], the MWCNT based oscillator with GHz resonance frequency [4], the extremely fast self-retraction motion of graphite flakes in HOPG islands [5] and so on. The role of the interlayer van de Waals interaction in driving the motion of such van de Waals devices has been well recognised and studied by various theoretical analysis and molecular dynamic simulations [3,4,6,7]. On the other hand, the interlayer van de Waals interactions also leads to potential corrugations due to the periodic atomic structures of the graphene layers, and in turn results in the interlayer friction/resistance force. The role of such friction force in the van de Waals micro/nano-mechanical devices, however, is largely overlooked and there is no experimental studies in micrometer scale up to now (except few scanning probe microscope (SPM) experiments with nanoscale sharp tip scanning on top of a graphene [8,9,10,11]). In this Letter, we will reveal the decisive role of such friction force in the van de Waals nano-mechanical devices. Our resultsshow that the superlubricity, as a result of the incommensurate contact of different graphene layers, is the necessary condition for the self-driven motion of CNT/graphene based micro/nanomechanical devices.Superlubricity is a phenomenon that friction force vanish or almost vanish when two solid surfaces are sliding over each other [12], and has attracted many attentions [13,14,15,16] since the introduction of the concept [17]. The structural incommensurate between two crystalline solid

between the electronic structure and catalytic efficiency of heterogenous catalysts. Dating back to 1930s, the concept of "electronic factor" was proposed by G. M. Schwab to describe the influence of electronic interaction on catalytic behavior of supported catalyst and divided the interaction into two parts, structural and synergetic ones. [4] Electrons transferring between the metal and the support was first under consideration when it came to the catalysis. After that, S. J. Tauster used the term of "strong metal-support interaction" (SMSI) to describe the chemisorption properties of group VIII elements supported by a metal oxide (e.g. SiO 2 , MgO) in 1978. [5] Later the concept was broadened to interaction between any metallic species and support, based on the experimental phenomena. [6] Till that time, based on the early characterization of surface science, researchers had realized that the active site might change during the process from metallic to an SMSI state, which was indicated to be covered or encapsulated by the support. [7] Among many hypotheses concerning the mechanism of SMSI, electron transfer between the metal and the support has been adapted by many researchers, and was confirmed by Rodriguez based on X-ray crystallography and UV photoemission spectroscopy in 1990s. [8] Then almost at the same time that the term of SACs was formally put forward, C. T. Campbell proposed the concept of "electronic metal-support interaction" (EMSI). As Campbell described, the chemical and catalytic properties might be affected by the electronic perturbations (i.e., shifts in the energy of d-band center) due to the EMSI. [9] In other words, the EMSI gave a much more detailed explanation of the enhanced properties of supported catalysts than SMSI, indicating that the study on catalysis was finally pushed to the electronic scale after so many years. [9b,c,10] Unfortunately, for metal particles or clusters, the accurate identification of electronic state is often difficult or even impossible. [11] The most reliable way to overcome the barrier above is to develop the catalyst based on single-atom metal that avoids intrinsic metal effects, including the electronic quantum size effect as well as structure-sensitivity geometrical effect. [12] Therefore, the rise of SACs provides a nearly perfect model to study the EMSI. As the supported metal species downsize to the single atom, the interaction between active site and support is always uniform, which can be much easier to be characterized by both experiment and theoretical calculation. [13] With the help of advanced techniques, for instance, X-ray absorption spectroscopy (XAS), the information about electronic The electronic metal-support interaction (EMSI), which acts as a bridge between theoretical electronic study and the design of heterogenous catalysts, has attracted much attention. Utilizing the interaction between the metal and the support is one of the most essential strategies to enhance electrocatalytic efficiency due to structural and synergetic promotion. To ...

The basal plane cleavage energy (CE) of graphite is a key material parameter for understanding many of the unusual properties of graphite, graphene and carbon nanotubes. Nonetheless, a wide range of values for the CE has been reported and no consensus has yet emerged. Here we report the first direct, accurate experimental measurement of the CE of graphite using a novel method based on the self-retraction phenomenon in graphite. The measured value, 0.37±0.01 J m−2 for the incommensurate state of bicrystal graphite, is nearly invariant with respect to temperature (22 °C≤T≤198 °C) and bicrystal twist angle, and insensitive to impurities from the atmosphere. The CE for the ideal ABAB graphite stacking, 0.39±0.02 J m−2, is calculated based on a combination of the measured CE and a theoretical calculation. These experimental measurements are also ideal for use in evaluating the efficacy of competing theoretical approaches.

Manipulating the coordination environment of the active center via anion modulation to reveal tailored activity and selectivity has been widely achieved, especially for carbon-based single-atom site catalysts (SACs). However, tuning ligand fields of the active center by single-site metal cation regulation and identifying the effects on the resulting electronic configuration is seldom explored. Herein, we propose a single-site Ru cation coordination strategy to engineer the electronic properties by constructing a Ru/LiCoO 2 SAC with atomically dispersed RuÀ Co pair sites. Benefitting from the strong electronic coupling between Ru and Co sites, the catalyst possesses an enhanced electrical conductivity and achieves near-optimal oxygen adsorption energies. Therefore, the optimized catalyst delivers superior oxygen evolution reaction (OER) activity with low overpotential, the high mass activity of 1000 A g oxide À 1 at a small overpotential of 335 mV, and excellent long-term stability. It also exhibits rapid kinetics with superior rate capability and outstanding durability in a zinc-air battery.

The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this prediction. A new peptide RT prediction tool, DeepRT, was designed using a capsule network model, and the public data sets containing peptides separated by reverse-phase liquid chromatography were used to evaluate the DeepRT performance. Compared with other prevailing RT predictors, DeepRT attained overall improvement in the prediction of peptide RTs with an R of ∼0.994. Moreover, DeepRT was able to accommodate to the peptides that were separated by different types of LC, such as strong cation exchange (SCX) and hydrophilic interaction liquid chromatography (HILIC) and to reach the RT prediction with R values of ∼0.996 for SCX and ∼0.993 for HILIC, respectively. If a large peptide data set is available for one type of LC, DeepRT can be promoted to DeepRT(+) using transfer learning. Based on a large peptide data set gained from SWATH, DeepRT(+) further elevated the accuracy of RT prediction for peptides in a small data set and enabled a satisfactory prediction upon limited peptides approximating hundreds. Further, DeepRT automatically learns retention-related properties of amino acids under different separation mechanisms, which are well consistent with retention coefficients (Rc) of the amino acids. DeepRT was thus proven to be an improved RT predictor with high flexibility and efficiency. DeepRT is available at https://github.com/horsepurve/DeepRTplus .