Nanomaterials for Hydrogen Storage Applications: A Review

Nanomaterials have attracted great interest in recent years because of the unusual mechanical, electrical, electronic, optical, magnetic and surface properties. The high surface/volume ratio of these materials has significant implications with respect to energy storage. Both the high surface area and the opportunity for nanomaterial consolidation are key attributes of this new class of materials for hydrogen storage devices. Nanostructured systems including carbon nanotubes, nano-magnesium based hydrides, complex hydride/carbon nanocomposites, boron nitride nanotubes, TiS2/MoS2 nanotubes, alanates, polymer nanocomposites, and metal organic frameworks are considered to be potential candidates for storing large quantities of hydrogen. Recent investigations have shown that nanoscale materials may offer advantages if certain physical and chemical effects related to the nanoscale can be used efficiently. The present review focuses the application of nanostructured materials for storing atomic or molecular hydrogen. The synergistic effects of nanocrystalinity and nanocatalyst doping on the metal or complex hydrides for improving the thermodynamics and hydrogen reaction kinetics are discussed. In addition, various carbonaceous nanomaterials and novel sorbent systems (e.g. carbon nanotubes, fullerenes, nanofibers, polyaniline nanospheres and metal organic frameworks etc.) and their hydrogen storage characteristics are outlined

Palladium-based ferroelectrics and multiferroics : theory and experiment

Palladium normally does not easily substitute for Ti or Zr in perovskite oxides. Moreover, Pd is not normally magnetic (but becomes ferromagnetic under applied uniaxial stress or electric fields). Despite these two great obstacles, we have succeeded in fabricating lead zirconate titanate with 30% Pd substitution. For 20:80 Zr:Ti the ceramics are generally single-phase perovskite (>99%), but sometimes exhibit 1% PbPdO2, which is magnetic below T=90K. The resulting material is multiferroic (ferroelectric-ferromagnet) at room temperature. The processing is slightly unusual (>8 hrs in high-energy ball-milling in Zr balls), and the density functional theory provided shows that it occurs because of Pd+4 in the oversized Pb+2 site; if all Pd+4 were to go into the Ti+4 perovskite B-site, no magnetism would result.PostprintPeer reviewe

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14