01850nas a2200253   4500008004100000022001400041245009500055210006900150260007300219300001400292490000800306520089500314653003401209653003001243653003301273653002501306653001601331100002001347700002401367700002401391700002401415700003301439856012401472       2012                        eng d  a0169-433200aEnhancing the hydrogen storage capacity of Pd-functionalized multi-walled carbon nanotubes0 aEnhancing the hydrogen storage capacity of Pdfunctionalized mult  aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcFEB  a3405-34090 v2583 a<p>We demonstrate that the hydrogen storage capacity of multi-walled carbon nanotubes can be enhanced by polyvinylpyrrolidone functionalization. The polyvinylpyrrolidone acts as a stabilizing agent for Pd-nanoparticles, reduces their size and facilitates their uniform and enhanced loading onto multi-walled carbon nanotubes. According to sorption studies, the polyvinylpyrrolidone capping and consequent nanostructural modification enables 2.3 times more hydrogen adsorption than mere Pd-functionalization of multi-walled carbon nanotubes. Corresponding morphological changes before and after polyvinylpyrrolidone capping, characterized using Raman Spectroscopy, X-ray diffraction, TEM and thermal analysis techniques, are also presented. The results contribute towards increasing the efficiency of hydrogen based sustainable energy sources. (C) 2011 Elsevier B.V. All rights reserved.</p>10aHydrogen based energy sources10aHydrogen storage capacity10aMultiwalled carbon nanotubes10aPd functionalization10aPVP capping1 aSingh, Priyanka1 aKulkarni, Mukta, V.1 aGokhale, Suresh, P.1 aChikkali, Samir, H.1 aKulkarni, Chandrashekhar, V.  uhttp://library.ncl.res.in/content/enhancing-hydrogen-storage-capacity-pd-functionalized-multi-walled-carbon-nanotubes-002675nas a2200229   4500008004100000022001400041245007700055210006900132260000800201300001400209490000700223520189800230653003402128653003002162653001902192653003902211653003302250100001902283700001902302700001802321856010602339       2024                        eng d  a0360-319900aSolid state hydrogen storage: Decoding the path through machine learning0 aSolid state hydrogen storage Decoding the path through machine l  cJAN  a1518-15280 v503 a<p>We present a machine learning (ML) framework HEART (HydrogEn storAge propeRty predicTor) for identifying suitable families of metal alloys for hydrogen storage under ambient conditions. Our framework includes two ML models that predict the hydrogen storage capacity (HYST) and the enthalpy of hydride formation (THOR) of multi-component metal alloys. We demonstrate that a chemically diverse set of features effectively describes the hydrogen storage properties of the alloys. In HYST, we use absorption temperature as a feature which improved H2wt% prediction significantly. For out-of-the-bag samples, HYST predicted H2wt% with R2 score of 0.81 and mean absolute error (MAE) of 0.45 wt% whereas R2 score is 0.89 and MAE is 4.53 kJ/molH2 for THOR. These models are further employed to predict H2wt% and Delta H for similar to 6.4 million multi-component metal alloys. We have identified 6480 compositions with superior storage properties (H2wt% &gt; 2.5 at room temperature and Delta H &lt; 60 kJ/molH2). We have also discussed in detail the interesting trends picked up by these models like temperature dependent variation in the rate of hydrogenation and alloying effect on H2wt% and Delta H in different families of alloys. Importantly certain elements like Al, Si, Sc, Cr, and Mn when mixed in small fractions with hydriding elements like Mg, Ti, V etc. systematically reduce Delta H without significantly compromising the storage capacity. Further upon increasing the number of elements in the alloy i.e from binary to ternary to quaternary, the number of compositions with lower enthalpies also increases. From the 6.4 million compositions, we have reported new alloy families having potential for hydrogen storage at room temperature. Finally, we demonstrate that HEART has the potential to scan vast chemical spaces by narrowing down potential materials for hydrogen storage.</p>
10aEnthalpy of hydride formation10aHydrogen storage capacity10aMetal hydrides10aPredictive machine learning models10aSolid-state hydrogen storage1 aVerma, Ashwini1 aWilson, Nikhil1 aJoshi, Kavita  uhttp://library.ncl.res.in/content/solid-state-hydrogen-storage-decoding-path-through-machine-learning