Refine
Document Type
- Article (3)
- Postprint (1)
- Working Paper (1)
Language
- English (5)
Is part of the Bibliography
- yes (5)
Keywords
- conomics (1)
- open science (1)
- political science (1)
- replication (1)
- reproduction (1)
- research transparency (1)
Institute
Temperature-memory polymers remember the temperature, where they were deformed recently, enabled by broad thermal transitions. In this study, we explored a series of crosslinked poly[ethylene-co-(vinyl acetate)] networks (cPEVAs) comprising crystallizable polyethylene (PE) controlling units exhibiting a pronounced temperature-memory effect (TME) between 16 and 99 °C related to a broad melting transition (∼100 °C). The nanostructural changes in such cPEVAs during programming and activation of the TME were analyzed via in situ X-ray scattering and specific annealing experiments. Different contributions to the mechanism of memorizing high or low deformation temperatures (Tdeform) were observed in cPEVA, which can be associated to the average PE crystal sizes. At high deformation temperatures (>50 °C), newly formed PE crystals, which are established during cooling when fixing the temporary shape, dominated the TME mechanism. In contrast, at low Tdeform (<50 °C), corresponding to a cold drawing scenario, the deformation led preferably to a disruption of existing large crystals into smaller ones, which then fix the temporary shape upon cooling. The observed mechanism of memorizing a deformation temperature might enable the prediction of the TME behavior and the knowledge based design of other TMPs with crystallizable controlling units.
Temperature-memory polymers remember the temperature, where they were deformed recently, enabled by broad thermal transitions. In this study, we explored a series of crosslinked poly[ethylene-co-(vinyl acetate)] networks (cPEVAs) comprising crystallizable polyethylene (PE) controlling units exhibiting a pronounced temperature-memory effect (TME) between 16 and 99 °C related to a broad melting transition (∼100 °C). The nanostructural changes in such cPEVAs during programming and activation of the TME were analyzed via in situ X-ray scattering and specific annealing experiments. Different contributions to the mechanism of memorizing high or low deformation temperatures (Tdeform) were observed in cPEVA, which can be associated to the average PE crystal sizes. At high deformation temperatures (>50 °C), newly formed PE crystals, which are established during cooling when fixing the temporary shape, dominated the TME mechanism. In contrast, at low Tdeform (<50 °C), corresponding to a cold drawing scenario, the deformation led preferably to a disruption of existing large crystals into smaller ones, which then fix the temporary shape upon cooling. The observed mechanism of memorizing a deformation temperature might enable the prediction of the TME behavior and the knowledge based design of other TMPs with crystallizable controlling units.
Temperature-memory polymers remember the temperature, where they were deformed recently, enabled by broad thermal transitions. In this study, we explored a series of crosslinked poly[ethylene-co-(vinyl acetate)] networks (cPEVAs) comprising crystallizable polyethylene (PE) controlling units exhibiting a pronounced temperature-memory effect (TME) between 16 and 99 degrees C related to a broad melting transition (similar to 100 degrees C). The nanostructural changes in such cPEVAs during programming and activation of the TME were analyzed via in situ X-ray scattering and specific annealing experiments. Different contributions to the mechanism of memorizing high or low deformation temperatures (T-deform) were observed in cPEVA, which can be associated to the average PE crystal sizes. At high deformation temperatures (>50 degrees C), newly formed PE crystals, which are established during cooling when fixing the temporary shape, dominated the TME mechanism. In contrast, at low T-deform (<50 degrees C), corresponding to a cold drawing scenario, the deformation led preferably to a disruption of existing large crystals into smaller ones, which then fix the temporary shape upon cooling. The observed mechanism of memorizing a deformation temperature might enable the prediction of the TME behavior and the knowledge based design of other TMPs with crystallizable controlling units.
Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.