Electric-field alignment of carbon nanotubes (CNT) is widely used to produce composite materials with anisotropic mechanical, electrical, and optical properties. Nevertheless, consistent results are difficult to achieve, and even under identical electric field conditions the resulting aligned morphologies can vary over μm to cm length scales. In order to improve reproducibility, this study addresses (1) how solution processing steps (oxidation, sonication) affect CNT properties, and (2) how CNT chemistry, morphology, and dispersion influence alignment. Aligned CNT were deposited onto PVDF membranes using a combination of electric-field alignment and vacuum-filtration. At each step in solution processing, the CNT chemistry (oxygen content) and morphology (length/diameter) were characterized and compared to the final aligned morphology. Well-dispersed CNT with high oxygen content (>8.5%O) yielded uniform membrane coatings and microscopically aligned CNT, whereas CNT with low oxygen CNT (<2.2%O) produced aligned bundles visible at a macroscopic level, but microscopically the individual CNT remained disordered. Based on regression analysis, CNT with larger mean length and diameter, smaller length and diameter variation, and higher oxygen content yielded increased electrical anisotropy, and bath sonication was slightly preferable to probe sonication for initial dispersion.
Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. Most IV analyses use naturally occurring instruments, such as distance to a hospital. In these analyses, investigators must assume the instrument is as-if randomly assigned. This assumption cannot be tested directly, but it can be falsified. Most IV falsification tests compare relative prevalence or bias in observed covariates between the instrument and exposure. These tests require investigators to make covariate-by-covariate judgments about the validity of the IV design. Often, only some covariates are well-balanced, making it unclear if as-if randomization can be assumed for the instrument. We propose an alternative falsification test that compares IV balance or bias to the balance or bias that would have been produced under randomization. A key advantage of our test is that it allows for global balance measures as well as easily interpretable graphical comparisons. Furthermore, our test does not rely on parametric assumptions and can be used to validly assess if the instrument is significantly closer to being as-if randomized than the exposure. We demonstrate our approach using (SPOT)light, a prospective cohort study in 48 National Health Service hospitals between 1 November 2010 and 31 December 2011. This study used bed availability in the intensive care unit as an instrument for admission to the intensive care unit.
Causal analyses for observational studies are often complicated by covariate imbalances among treatment groups, and matching methodologies alleviate this complication by finding subsets of treatment groups that exhibit covariate balance. It is widely agreed upon that covariate balance can serve as evidence that a matched dataset approximates a randomized experiment, but what kind of experiment does a matched dataset approximate? In this work, we develop a randomization test for the hypothesis that a matched dataset approximates a particular experimental design, such as complete randomization, block randomization, or rerandomization. Our test can incorporate any experimental design, and it allows for a graphical display that puts several designs on the same univariate scale, thereby allowing researchers to pinpoint which design-if any-is most appropriate for a matched dataset. After researchers determine a plausible design, we recommend a randomizationbased approach for analyzing the matched data, which can incorporate any design and treatment effect estimator. Through simulation, we find that our test can frequently detect violations of randomized assignment that harm inferential results. Furthermore, through simulation and a real application in political science, we find that matched datasets with high levels of covariate balance tend to approximate balance-constrained designs like rerandomization, and analyzing them as such can lead to precise causal analyses. However, assuming a precise design should be proceeded with caution, because it can harm inferential results if there are still substantial biases due to remaining imbalances after matching. Our approach is implemented in the randChecks R package, available on CRAN.
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