Fracture Testing at Small-Length Scales: From Plasticity in Si to Brittleness in Pt

The field of micro-/nano-mechanics of materials has been driven, on the one hand by the development of ever smaller structures in devices, and, on the other, by the need to map property variations in large systems that are microstructurally graded. Observations of `smaller is stronger' have also brought in questions of accompanying fracture property changes in the materials. In the wake of scattered articles on micro-scale fracture testing of various material classes, this review attempts to provide a holistic picture of the current state of the art. In the process, various reliable micro-scale geometries are shown, challenges with respect to instrumentation to probe ever smaller length scales are discussed and examples from recent literature are put together to exhibit the expanse of unusual fracture response of materials, from ductility in Si to brittleness in Pt. Outstanding issues related to fracture mechanics of small structures are critically examined for plausible solutions.

Molecular Cage Impregnated Palladium Nanoparticles: Efficient, Additive-Free Heterogeneous Catalysts for Cyanation of Aryl Halides

Two shape-persistent covalent cages (CC1(r) and CC2(r)) have been devised from triphenyl amine-based trialdehydes and cyclohexane diamine building blocks utilizing the dynamic imine chemistry followed by imine bond reduction. The cage compounds have been characterized by several spectroscopic techniques which suggest that CC1(r) and CC2(r) are 2+3] and 8+12] self-assembled architectures, respectively. These state-of-the-art molecules have a porous interior and stable aromatic backbone with multiple palladium binding sites to engineer the controlled synthesis and stabilization of ultrafine palladium nanoparticles (PdNPs). As-synthesized cage-embedded PdNPs have been characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), and powder X-ray diffraction (PXRD). Inductively coupled plasma optical emission spectrometry reveals that Pd@CC1(r) and Pd@CC2(r) have 40 and 25 wt% palladium loading, respectively. On the basis of TEM analysis, it has been estimated that as small as similar to 1.8 nm PdNPs could be stabilized inside the CC1(r), while larger CC2(r) could stabilize similar to 3.7 nm NPs. In contrast, reduction of palladium salts in the absence of the cages form structure less agglomerates. The well-dispersed cage-embedded NPs exhibit efficient catalytic performance in the cyanation of aryl halides under heterogeneous, additive-free condition. Moreover, these materials have excellent stability and recyclability without any agglomeration of PdNPs after several cycles.

PolyMage: Automatic Optimization for Image Processing Pipelines

This paper presents the design and implementation of PolyMage, a domain-specific language and compiler for image processing pipelines. An image processing pipeline can be viewed as a graph of interconnected stages which process images successively. Each stage typically performs one of point-wise, stencil, reduction or data-dependent operations on image pixels. Individual stages in a pipeline typically exhibit abundant data parallelism that can be exploited with relative ease. However, the stages also require high memory bandwidth preventing effective utilization of parallelism available on modern architectures. For applications that demand high performance, the traditional options are to use optimized libraries like OpenCV or to optimize manually. While using libraries precludes optimization across library routines, manual optimization accounting for both parallelism and locality is very tedious. The focus of our system, PolyMage, is on automatically generating high-performance implementations of image processing pipelines expressed in a high-level declarative language. Our optimization approach primarily relies on the transformation and code generation capabilities of the polyhedral compiler framework. To the best of our knowledge, this is the first model-driven compiler for image processing pipelines that performs complex fusion, tiling, and storage optimization automatically. Experimental results on a modern multicore system show that the performance achieved by our automatic approach is up to 1.81x better than that achieved through manual tuning in Halide, a state-of-the-art language and compiler for image processing pipelines. For a camera raw image processing pipeline, our performance is comparable to that of a hand-tuned implementation.