RESUMEN

Solid nitrogen exhibits a panoply of phenomena ranging from complex molecular crystalline configurations to polymerization and closing band gap at higher densities. Among the elemental molecular solids, nitrogen stands apart for having phases, which can only be stabilized following particular pressure-temperature pathways, indicative of metastability and kinetic barriers. Here, through the combination of Raman spectroscopy and dynamic compression techniques, we find that the appearance of the whole nitrogen phase diagram is determined by the P-T paths taken below 2 GPa. We reveal the existence of the path- and phase-dependent triple point between the ß - N 2 , δ loc - N 2 and γ - or ϵ - N 2 . We further show that the ß - N 2 towards γ - N 2 path below the triple point, that evades δ ( δ loc )- N 2 , results in the formation of γ - N 2 , which in turn becomes a dominant phase. We then demonstrate, that the ß - N 2 through δ ( δ loc )- N 2 above the triple point path leads to the formation of ϵ - N 2 and the "well-established" phase diagram. An additional pathway, which by-passes the rotationally inhibited modifications δ ( δ loc )- N 2 , via rapid compression is found to produce γ - N 2 at higher temperatures. We argue that the pathway and phase sensitive triple point and the compression rate dependent phase formation challenge our understanding of this archetypal dense molecular solid.

RESUMEN

Pressure-induced magnetic phase transitions are attracting interest as a means to detect superconducting behaviour at high pressures in diamond anvil cells, but determining the local magnetic properties of samples is a challenge due to the small volumes of sample chambers. Optically detected magnetic resonance of nitrogen vacancy centres in diamond has recently been used for the in situ detection of pressure-induced phase transitions. However, owing to their four orientation axes and temperature-dependent zero-field splitting, interpreting these optically detected magnetic resonance spectra remains challenging. Here we study the optical and spin properties of implanted silicon vacancy defects in 4H-silicon carbide that exhibit single-axis and temperature-independent zero-field splitting. Using this technique, we observe the magnetic phase transition of Nd2Fe14B at about 7 GPa and map the critical temperature-pressure phase diagram of the superconductor YBa2Cu3O6.6. These results highlight the potential of silicon vacancy-based quantum sensors for in situ magnetic detection at high pressures.

RESUMEN

There is an ever increasing interest in studying dynamic-pressure dependent phenomena utilizing dynamic Diamond Anvil Cells (dDACs), devices capable of a highly controlled rate of compression. Here, we characterize and compare the compression rate of dDACs in which the compression is actuated via three different methods: (1) stepper motor (S-dDAC), (2) gas membrane (M-dDAC), and (3) piezoactuator (P-dDAC). The compression rates of these different types of dDAC were determined solely on millisecond time-resolved R1-line fluorescence of a ruby sphere located within the sample chamber. Furthermore, these different dynamic compression-techniques have been described and characterized over a broad temperature and pressure range from 10 to 300 K and 0-50 GPa. At room temperature, piezoactuation (P-dDAC) has a clear advantage in controlled extremely fast compression, having recorded a compression rate of ∼7 TPa/s, which is also found to be primarily influenced by the charging time of the piezostack. At 40-250 K, gas membranes (M-dDAC) have also been found to generate rapid compression of ∼0.5-3 TPa/s and are readily interfaced with moderate cryogenic and ultrahigh vacuum conditions. Approaching more extreme cryogenic conditions (<10 K), a stepper motor driven lever arm (S-dDAC) offers a solution for high-precision moderate compression rates in a regime where P-dDACs and M-dDACs can become difficult to incorporate. The results of this paper demonstrate the applicability of different dynamic compression techniques, and when applied, they can offer us new insights into matter's response to strain, which is highly relevant to physics, geoscience, and chemistry.

RESUMEN

Carbon disulfide is an archetypal double-bonded molecule belonging to the class of group IV-group VI, AB2 compounds. It is widely believed that, upon compression to several GPa at room temperature and above, a polymeric chain of type (-(C═S)-S-)n, named Bridgman's black polymer, will form. By combining optical spectroscopy and synchrotron X-ray diffraction data with ab initio simulations, we demonstrate that the structure of this polymer is different. Solid molecular CS2 polymerizes at ∼10-11 GPa. The polymer is disordered and consists of a mixture of 3-fold (C3) and 4-fold (C4) coordinated carbon atoms with some C═C double bonds. The C4/C3 ratio continuously increases upon further compression to 40 GPa. Upon decompression, structural changes are partially reverted, while the sample also undergoes partial disproportionation. Our work uncovers the nontrivial high-pressure structural evolution in one of the simplest molecular systems exhibiting molecular as well as polymeric phases.

RESUMEN

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China's urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Asunto(s)