The stacking configuration in few-layer two-dimensional (2D) materials results in different structural symmetries and layer-to-layer interactions, and hence it provides a very useful parameter for tuning their electronic properties. For example, ABA-stacking trilayer graphene remains semimetallic similar to that of monolayer, while ABC-stacking is predicted to be a tunable band gap semiconductor under an external electric field. Such stacking dependence resulting from many-body interactions has recently been the focus of intense research activities. Here we demonstrate that few-layer MoS2 samples grown by chemical vapor deposition with different stacking configurations (AA, AB for bilayer; AAB, ABB, ABA, AAA for trilayer) exhibit distinct coupling phenomena in both photoluminescence and Raman spectra. By means of ultralow-frequency (ULF) Raman spectroscopy, we demonstrate that the evolution of interlayer interaction with various stacking configurations correlates strongly with layer-breathing mode (LBM) vibrations. Our ab initio calculations reveal that the layer-dependent properties arise from both the spin-orbit coupling (SOC) and interlayer coupling in different structural symmetries. Such detailed understanding provides useful guidance for future spintronics fabrication using various stacked few-layer MoS2 blocks.

This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system's most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.

Loading metal guests within metal-organic frameworks (MOFs) via secondary functional groups is a promising route for introducing or enhancing MOF performance in various applications. In this work, 14 metal ions (Li, Na, K, Mg, Ca, Ba, Zn, Co, Mn, Ag, Cd, La, In, and Pb) have been successfully introduced within the MIL-121 MOF using a cost-efficient route involving free carboxylic groups on the linker. The local and long-range structure of the metal-loaded MOFs is characterized using multinuclear solid-state NMR and X-ray diffraction methods. Li/Mg/Ca-loaded MIL-121 and Ag nanoparticle-loaded MIL-121 exhibit enhanced H and CO adsorption; Ag nanoparticle-loaded MIL-121 also demonstrates remarkable catalytic activity in the reduction of 4-nitrophenol.

Metal−organic frameworks (MOFs) have shown great promise for the adsorption and separation of gases, including the greenhouse gas CO 2 . In order to improve performance and realize practical applications for MOFs as CO 2 adsorbents, a deeper understanding of the number and type of CO 2 adsorption mechanisms must be unlocked, along with fine details of CO 2 motion within MOFs. Using several complementary characterization methods is a promising protocol for comprehensively investigating the various host− guest interactions between MOFs and CO 2 . In this work, a combination of solid state NMR (SSNMR) and single crystal X-ray diffraction (SCXRD) has been utilized to reveal both the location and dynamics of adsorbed CO 2 within the related PbSDB and CdSDB MOFs, as well as to probe the role of metal centers in CO 2 adsorption. 13 C SSNMR experiments targeting CO 2 reveal the number of unique adsorption sites and the types of CO 2 dynamics present, as well as their associated motional rates and angles. 111 Cd and 207 Pb SSNMR methods are used to probe the influence of CO 2 adsorption on the MOF metal centers, and also to investigate the possibility of any metal−guest interactions. SCXRD experiments yield the exact locations and occupancies of adsorbed CO 2 in both MOFs; by pairing this information with SSNMR data, a comprehensive model of CO 2 adsorption and dynamics in PbSDB and CdSDB has been established. Both MOFs share a common adsorption site in the V-shaped "π-pocket" formed by the phenyl rings of an individual V-shaped organic linker, while CdSDB also features an additional π-pocket adsorption site arising from the phenyl rings of two linkers joined by Cd. SCXRD and SSNMR data indicate that CO 2 adsorbed at the SDB-based π pocket in both MOFs exhibits a local rotation or "wobbling" at an individual adsorption site, as well as a nonlocalized jumping or "hopping" between symmetry-equivalent adsorption sites. The combined analysis of SCXRD and SSNMR data has the potential to yield rich information regarding guest dynamics, adsorption locations, and host−guest interactions in many MOFs.

An exciting advance in materials science is the discovery of hybrid organic-inorganic solids known as metal-organic frameworks (MOFs). Although they have numerous important applications, the local structures, specific molecular-level features, and guest behaviors underpinning desirable properties and applications are often unknown. Solid-state nuclear magnetic resonance (SSNMR) is a powerful tool for MOF characterization as it provides information complementary to that from X-ray diffraction (XRD). We describe our novel pursuits in the three primary applications of SSNMR for MOF characterization: interrogating the metal center, targeting linker molecules, and probing guests. MOFs have relatively low densities, and the incorporated metals are often quadrupolar nuclei, making SSNMR detection difficult. Recently, we examined the local structures of metal centers (i.e., Mg,Ti, Cu,Zn, Ga,Zr, In,Ba, La,Al) in representative MOFs by SSNMR at a high magnetic field of 21.1 T, addressing several important issues: (1) resolving chemically and crystallographically nonequivalent metal sites; (2) exploring the origin of disorder around metals; (3) refining local metal geometry; (4) probing the effects of activation and adsorption on the metal local environment; and (5) monitoring in situ phase changes in MOFs. Organic linkers can be characterized by H,C, and O SSNMR. Although the framework structure can be determined by X-ray diffraction, hydrogen atoms cannot be accurately located, and thus the local structure about hydrogen is poorly characterized. Our work demonstrates that magic-angle spinning (MAS) experiments at very high magnetic field along with ultrafast spinning rates and isotope dilution enables one to obtain ultrahigh resolutionH MAS spectra of MOFs, yielding structural information truly complementary to that obtained from single-crystal XRD. Oxygen is a key constituent of many important MOFs but O SSNMR work on MOFs is scarce due to the low natural abundance ofO. O enriched MOFs can now be prepared in an efficient and economically feasible manner using solvothermal approaches involving labeled HO water; the resulting O SSNMR spectra provide distinct spectral signatures of various key oxygen species in representative MOFs. MOFs are suitable for the capture of the greenhouse gas CO and the storage of energy carrier gases such as H and CH. A better understanding of gas adsorption obtained using C,H, and O SSNMR will enable researchers to improve performance and realize practical applications for MOFs as gas adsorbents and carriers. The combination of SSNMR with XRD allows us to determine the number of adsorption sites in the framework, identify the location of binding sites, gain physical insight into the nature and strength of host-guest interactions, and understand guest dynamics.