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- W4322154911 abstract "Some high-throughput experiments aim to test many samples simultaneously under nominally the same conditions. However, whether a particular high-throughput experiment does so must be certified. We previously described a high-throughput experiment to study the statistics of rupture stretch of materials. In such an experiment, a large set of samples were tested simultaneously. We noticed a systematic bias that samples in different subsets give different statistical distributions of rupture stretch. Here we describe an approach to detect and reduce systematic bias in the high-throughput experiment. We divide the whole set of the data of the experiment into subsets, obtain the statistical distribution of rupture stretches for each subset, and compare the “closeness” of the distributions among the pairs of subsets by using the Anderson-Darling (A-D) test. We then try to reduce the systematic bias by improving the experimental design, such as increasing the rigidity of the frame and the firmness of the grips. With the improved experimental design, the newly obtained rupture data passes the A-D test. We use the new rupture data to fit the Weibull distribution. The improved high-throughput experiment greatly increases the accuracy of fitting and prediction of rare-event rupture stretch." @default.
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- W4322154911 date "2023-05-01" @default.
- W4322154911 modified "2023-09-29" @default.
- W4322154911 title "Detection and reduction of systematic bias in high-throughput rupture experiments" @default.
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- W4322154911 doi "https://doi.org/10.1016/j.jmps.2023.105249" @default.
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