ELECTROCATALYTIC WATER OXIDATION BY METAL ORGANIC FRAMEWORKS

Master'sMASTER OF SCIENC

Geological conditions of coal reservoir occurrence in the Southern Qinshui Basin and its impact on permeability

The occurrence and output of coalbed methane (CBM) are controlled by the occurrence geological conditions of coal reservoirs, such as stress, pressure, and temperature. The correct analysis of the occurrence geological conditions of coal reservoirs and their impact on permeability is a key issue of concern for an effective development of CBM. Based on the test data of 63 CBM wells in the southern part of the Qinshui Basin, the ground stress, pressure and temperature conditions of coal reservoirs in the study area are systematically analyzed, the variation law of coal reservoir stress, pressure and temperature with burial depth is revealed, and the relationship between the minimum horizontal principal stress and the vertical principal stress and the pressure of coal reservoir is established. Using the triaxial seepage test system, the experiment of CBM seepage under different stress, pressure and temperature conditions is carried out, and the variation law and control mechanism of coal sample permeability under different temperature, stress and pressure conditions are revealed. The results show that the maximum and minimum horizontal principal stresses of the coal reservoirs in the study area are 6.62−42.06 MPa and 3.30−26.40 MPa, respectively, with the gradients of 1.20−5.26 MPa/hm and 0.99−2.95 MPa/hm, respectively. The coal reservoir pressures and their gradients are 0.99−12.63 MPa and 0.23−1.18 MPa/hm; the coal reservoir temperatures and their gradients are 19.36−38.84 ℃ and 1.98 ℃/hm, respectively. The coal reservoir stress, pressure and temperature increase linearly with the increase of depth. With the increase of effective stress, the permeability of the coal reservoir decreases continuously, the permeability decreases greatly in the initial pressurization stage, but decrease slows down with the increase of effective stress. Under the same stress conditions, the permeability of coal samples and the decrease rate of permeability decrease continuously with the increase of temperature. With the increase of effective stress and temperature, the permeability of coal reservoir decreases according to the law of negative exponential function. With the decrease of pore pressure, the effective stress increases, but the permeability of coal reservoir decreases. In the initial depressurization stage, the permeability of the coal reservoir decreases sharply, and with the reduction of pore pressure, the decrease rate of permeability gradually slows down. When the pore pressure is less than 0.6 MPa, the permeability of the coal reservoir increases with the decrease of pore pressure. Under the condition of high pore pressure, the permeability decreases with the increase of temperature in a negative exponential function, while under the condition of low pore pressure, the permeability of coal reservoir decreases linearly with the increase of temperature. Based on the above results, the relationship model between coal reservoir permeability and stress, pressure and temperature is established. Also, the law and control mechanism of coal reservoir permeability decrease according to negative exponential function with the increase of stress, pressure and temperature stress are expounded

When Machine Learning Meets 2D Materials:A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.</p

Silver/gold core-shell nanoprism-based plasmonic nanoprobes for highly sensitive and selective detection of hydrogen sulfide

Chemistry - A European Journal213988-99

A real-time colorimetric assay for label-free detection of microRNAs down to sub-femtomolar levels

10.1039/c3cc41565aChemical Communications49434959-496

Detection of single-nucleotide polymorphisms based on the formation of an electron-transfer impeding layer on an electrode surface

10.1039/C2CC37450AChemical Communications494370-37

A label-free microRNA biosensor based on DNAzyme-catalyzed and microRNA-guided formation of a thin insulating polymer film

10.1016/j.bios.2013.01.028Biosensors and Bioelectronics44171–17