172 research outputs found
Atomic resolution mapping of phonon excitations in STEM-EELS experiments
Atomically resolved electron energy-loss spectroscopy experiments are
commonplace in modern aberrationcorrected transmission electron microscopes.
Energy resolution has also been increasing steadily with the continuous
improvement of electron monochromators. Electronic excitations however are
known to be delocalised due to the long range interaction of the charged
accelerated electrons with the electrons in a sample. This has made several
scientists question the value of combined high spatial and energy resolution
for mapping interband transitions and possibly phonon excitation in crystals.
In this paper we demonstrate experimentally that atomic resolution information
is indeed available at very low energy losses around 100 meV expressed as a
modulation of the broadening of the zero loss peak. Careful data analysis
allows us to get a glimpse of what are likely phonon excitations with both an
energy loss and gain part. These experiments confirm recent theoretical
predictions on the strong localisation of phonon excitations as opposed to
electronic excitations and show that a combination of atomic resolution and
recent developments in increased energy resolution will offer great benefit for
mapping phonon modes in real space
Same Data, Different Conclusions: Radical Dispersion in Empirical Results When Independent Analysts Operationalize and Test the Same Hypothesis
In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed
Long-Range Domain Structure and Symmetry Engineering by Interfacial Oxygen Octahedral Coupling at Heterostructure Interface
In epitaxial thin film systems, the crystal structure and its symmetry deviate from the bulk counterpart due to various mechanisms such as epitaxial strain and interfacial structural coupling, which is accompanyed by a change in their properties. In perovskite materials, the crystal symmetry can be described by rotations of sixfold coordinated transition metal oxygen octahedra, which are found to be altered at interfaces. Here, it is unraveled how the local oxygen octahedral coupling at perovskite heterostructural interfaces strongly influences the domain structure and symmetry of the epitaxial films resulting in design rules to induce various structures in thin films using carefully selected combinations of substrate/buffer/film. Very interestingly it is discovered that these combinations lead to structure changes throughout the full thickness of the film. The results provide a deep insight into understanding the origin of induced structures in a perovskite heterostructure and an intelligent route to achieve unique functional properties
Coupling charge and topological reconstructions at polar oxide interfaces
In oxide heterostructures, different materials are integrated into a single
artificial crystal, resulting in a breaking of inversion-symmetry across the
heterointerfaces. A notable example is the interface between polar and
non-polar materials, where valence discontinuities lead to otherwise
inaccessible charge and spin states. This approach paved the way to the
discovery of numerous unconventional properties absent in the bulk
constituents. However, control of the geometric structure of the electronic
wavefunctions in correlated oxides remains an open challenge. Here, we create
heterostructures consisting of ultrathin SrRuO, an itinerant ferromagnet
hosting momentum-space sources of Berry curvature, and LaAlO, a polar
wide-bandgap insulator. Transmission electron microscopy reveals an atomically
sharp LaO/RuO/SrO interface configuration, leading to excess charge being
pinned near the LaAlO/SrRuO interface. We demonstrate through
magneto-optical characterization, theoretical calculations and transport
measurements that the real-space charge reconstruction modifies the
momentum-space Berry curvature in SrRuO, driving a reorganization of the
topological charges in the band structure. Our results illustrate how the
topological and magnetic features of oxides can be manipulated by engineering
charge discontinuities at oxide interfaces.Comment: 5 pages main text (4 figures), 29 pages of supplementary informatio
Deciding what to replicate: a decision model for replication study selection under resource and knowledge constraints
Robust scientific knowledge is contingent upon replication of original findings. However, replicating researchers are constrained by resources, and will almost always have to choose one replication effort to focus on from a set of potential candidates. To select a candidate efficiently in these cases, we need methods for deciding which out of all candidates considered would be the most useful to replicate, given some overall goal researchers wish to achieve. In this article we assume that the overall goal researchers wish to achieve is to maximize the utility gained by conducting the replication study. We then propose a general rule for study selection in replication research based on the replication value of the set of claims considered for replication. The replication value of a claim is defined as the maximum expected utility we could gain by conducting a replication of the claim, and is a function of (a) the value of being certain about the claim, and (b) uncertainty about the claim based on current evidence. We formalize this definition in terms of a causal decision model, utilizing concepts from decision theory and causal graph modeling. We discuss the validity of using replication value as a measure of expected utility gain, and we suggest approaches for deriving quantitative estimates of replication value. Our goal in this article is not to define concrete guidelines for study selection, but to provide the necessary theoretical foundations on which such concrete guidelines could be built.Translational Abstract Replication-redoing a study using the same procedures-is an important part of checking the robustness of claims in the psychological literature. The practice of replicating original studies has been woefully devalued for many years, but this is now changing. Recent calls for improving the quality of research in psychology has generated a surge of interest in funding, conducting, and publishing replication studies. Because many studies have never been replicated, and researchers have limited time and money to perform replication studies, researchers must decide which studies are the most important to replicate. This way scientists learn the most, given limited resources. In this article, we lay out what it means to think about what is the most important thing to replicate, and we propose a general decision rule for picking a study to replicate. That rule depends on a concept we call replication value. Replication value is a function of the importance of the study, and how uncertain we are about the findings. In this article we explain how researchers can think precisely about the value of replication studies. We then discuss when and how it makes sense to use replication value as a measure of how valuable a replication study would be, and we discuss factors that funders, journals, or scientists could consider when determining how valuable a replication study is.Multivariate analysis of psychological dat
The Meta-Plot: A Graphical Tool for Interpreting the Results of a Meta-Analysis
The meta-plot is a descriptive visual tool for meta-analysis that provides information on the primary studies in the meta-analysis and the results of the meta-analysis. More precisely, the meta-plot portrays (1) the precision and statistical power of the primary studies in themetaanalysis, (2) the estimate and confidence interval of a random-effects meta-analysis, (3) the results of a cumulative random-effects metaanalysis yielding a robustness check of the meta-analytic effect size with respect to primary studies' precision, and (4) evidence of publication bias. After explaining the underlying logic and theory, the meta-plot is applied to two cherry-picked meta-analyses that appear to be biased and to 10 randomly selected meta-analyses from the psychological literature. We recommend accompanying any meta-analysis of common effect size measures with the meta-plot
Response to Comment on “Estimating the reproducibility of psychological science”
Gilbert et al. conclude that evidence from the Open Science Collaboration's Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.status: publishe
Evaluation of chloroform/methanol extraction to facilitate the study of membrane proteins of non-model plants
Membrane proteins are of great interest to plant physiologists because of their important function in many physiological processes. However, their study is hampered by their low abundance and poor solubility in aqueous buffers. Proteomics studies of non-model plants are generally restricted to gel-based methods. Unfortunately, all gel-based techniques for membrane proteomics lack resolving power. Therefore, a very stringent enrichment method is needed before protein separation. In this study, protein extraction in a mixture of chloroform and methanol in combination with gel electrophoresis is evaluated as a method to study membrane proteins in non-model plants. Benefits as well as disadvantages of the method are discussed. To demonstrate the pitfalls of working with non-model plants and to give a proof of principle, the method was first applied to whole leaves of the model plant Arabidopsis. Subsequently, a comparison with proteins extracted from leaves of the non-model plant, banana, was made. To estimate the tissue and organelle specificity of the method, it was also applied on banana meristems. Abundant membrane or lipid-associated proteins could be identified in both tissues, with the leaf extract yielding a higher number of membrane proteins
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