Using machine learning models, this study innovatively introduces multi-element compositions to optimize the performance of spinel refractories. A total of 1120 spinel samples were fabricated at 1600 degrees C for 2 h...
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Using machine learning models, this study innovatively introduces multi-element compositions to optimize the performance of spinel refractories. A total of 1120 spinel samples were fabricated at 1600 degrees C for 2 h, and an experimental database containing 112 data points was constructed. High-throughput performance predictions and experimental verifications were conducted, identifying the sample with the highest hardness, (Al2Fe0.25Zn0.25Mg0.25Mn0.25)O4 (1770.6 +/- 79.1 HV1, 3.35 times that of MgAl2O4), and the highest flexural strength, (Al2Cr0.5Zn0.1Mg0.2Mn0.2)O4 (161.2 +/- 9.7 MPa, 1.4 times that of MgAl2O4). Further analysis of phase composition and microstructure shows that the mechanism of hardness enhancement is mainly the solid solution strengthening of multi-element doping, the energy dissipation of the large-grain layered structure, and the reinforcement of the zigzag grain boundary. In addition to solid solution strengthening and a compact low-pore structure, the mechanism of improving bending strength also includes second-phase strengthening and phase concentration gradient distribution. This method provides a promising way to optimize the performance of refractory materials.
A new response function for ICP-AES is proposed in which the resultant compromise plasma conditions are independent of the concentration of the analytes in the test solution, The response function is designed to be us...
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A new response function for ICP-AES is proposed in which the resultant compromise plasma conditions are independent of the concentration of the analytes in the test solution, The response function is designed to be used in situations where the concentrations of the analytes in the sample are unknown, or, when many samples, of varying composition, are to be analysed, In addition, the use of the signal-to-root background ratio (SRBR) as the measurement of the analytical performance of an element for multi-element analysis by ICP-AES, with a charge-coupled device detector, is described, The use of the SRBR was found to give better detection limits than those obtained with the more commonly used signal-to-background ratio (SBR), For instance, the detection Limit for Mn II at 257.610 nm is improved from 13 ng ml(-1) when using the SBR and the proposed response function, to 2.8 ng ml(-1) when using the SRBR. The compromise operating parameters, and hence detection limits for the analytes, were shown to be independent of the composition of the test solution for the new function, In comparison, the detection limit of Mn II varied between 2.9 and 40 ng ml(-1), depending on the test solution composition when optimizing with a previously reported response function, Furthermore, biasing the proposed function for a particular analyte by increasing a weighting parameter is demonstrated, For instance, the detection limit of Pb I at 280.199 nm improved from 54 to 41 ng ml(-1), by increasing the weighting parameter for Pb from 1 to 10.
Two objective functions for multi-element optimization in ICP-AES were compared using signal-to-background ratios as a figure of merit. Complete three-dimensional response surfaces were generated for a number of eleme...
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Two objective functions for multi-element optimization in ICP-AES were compared using signal-to-background ratios as a figure of merit. Complete three-dimensional response surfaces were generated for a number of elements (Ca, Cu, Al, Na, Ni, Mn and Ba) and two artificial 'elements' to evaluate the performance of both objective functions in locating the optimum compromise instrumental operating conditions in multi-element determinations, In the determination of the best compromise instrument operating conditions for most combinations of the elements used, both objective functions performed equally well;however, one occasionally performed significantly better than the other.
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