Haverford College

Haverford College: Haverford Scholarship
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    8201 research outputs found

    Functional traits of fossil plants

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    Polymorphism in the Ruddlesden–Popper Nickelate La3Ni2O7: Discovery of a Hidden Phase with Distinctive Layer Stacking

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    We report the discovery of a novel form of Ruddlesden–Popper (RP) nickelate that stands as the first example of long-range, coherent polymorphism in this class of inorganic solids. Rather than the well-known, uniform stacking of perovskite blocks ubiquitously found in RP phases, this newly discovered polymorph of the bilayer RP phase La3Ni2O7 adopts a novel stacking sequence in which single-layer and trilayer blocks of NiO6 octahedra alternate in a “1313” sequence. Crystals of this new polymorph are described in space group Cmmm, although we note evidence for a competing Imam variant. Transport measurements at ambient pressure reveal metallic character with evidence of a charge density wave transition with an onset at T ≈ 134 K. The discovery of such polymorphism could reverberate to the expansive range of science and applications that rely on RP materials, particularly the recently reported signatures of superconductivity in bilayer La3Ni2O7 with Tc as high as 80 K above 14 GPa

    Find it like a dog: Using Gesture to Improve Object Search

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    Pointing is an intuitive and commonplace communication modality. In human-robot collaborative tasks, human pointing has been modeled using a variety of approaches, such as the forearm vector or the vector from eye to hand. However, models of the human pointing vector have not been uniformly or comprehensively evaluated. We performed a user study to compare five different representations of the pointing vector and their accuracies in identifying the human\u27s intended target in an object selection task. We also compare the vectors\u27 performances to that of domestic dogs to assess a non-human baseline known to be successful at following human points. Additionally, we developed an observation model to transform the vector into a probability map for object search. We implemented our system on our robot, enabling it to locate and fetch the user\u27s desired objects efficiently and accurately

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    Haverford College: Haverford Scholarship
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