<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Singh, Priyanka</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Mukta V.</style></author><author><style face="normal" font="default" size="100%">Gokhale, Suresh P.</style></author><author><style face="normal" font="default" size="100%">Chikkali, Samir H.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Chandrashekhar V.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhancing the hydrogen storage capacity of Pd-functionalized multi-walled carbon nanotubes</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Surface Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hydrogen based energy sources</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydrogen storage capacity</style></keyword><keyword><style  face="normal" font="default" size="100%">Multiwalled carbon nanotubes</style></keyword><keyword><style  face="normal" font="default" size="100%">Pd functionalization</style></keyword><keyword><style  face="normal" font="default" size="100%">PVP capping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">8</style></number><publisher><style face="normal" font="default" size="100%">ELSEVIER SCIENCE BV</style></publisher><pub-location><style face="normal" font="default" size="100%">PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS</style></pub-location><volume><style face="normal" font="default" size="100%">258</style></volume><pages><style face="normal" font="default" size="100%">3405-3409</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">2.112
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Verma, Ashwini</style></author><author><style face="normal" font="default" size="100%">Wilson, Nikhil</style></author><author><style face="normal" font="default" size="100%">Joshi, Kavita</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solid state hydrogen storage: Decoding the path through machine learning</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Hydrogen Energy </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Enthalpy of hydride formation</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydrogen storage capacity</style></keyword><keyword><style  face="normal" font="default" size="100%">Metal hydrides</style></keyword><keyword><style  face="normal" font="default" size="100%">Predictive machine learning models</style></keyword><keyword><style  face="normal" font="default" size="100%">Solid-state hydrogen storage</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">1518-1528</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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% &amp;gt; 2.5 at room temperature and Delta H &amp;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.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;7.2&lt;/p&gt;
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