ORCID Profile
0000-0003-1542-6170
Current Organisation
Institute for Basic Science
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Publisher: American Chemical Society (ACS)
Date: 17-03-2022
DOI: 10.1021/JACS.2C00879
Abstract: Despite three decades of intense research efforts, the most fundamental question "why do carbon nanotubes grow?" remains unanswered. In fact, carbon nanotubes (CNTs) should not grow since the encapsulation of a catalyst with graphitic carbon is energetically more favorable than CNT growth in every aspect. Here, we answer this question using a theoretical model based on extensive first-principles and molecular dynamics calculations. We reveal a historically overlooked yet fundamental aspect of the CNT-catalyst interface, viz., that the interfacial energy of the CNT-catalyst edge is contact angle-dependent. The contact angle increases via graphitic cap lift-off, drastically decreasing the interfacial formation energy by up to 6-9 eV/nm, overcoming van der Waals cap-catalyst adhesion, and driving CNT growth. Mapping this remarkable and simple interplay allows us to understand, for the first time, why CNTs grow.
Publisher: MDPI AG
Date: 25-06-2020
DOI: 10.20944/PREPRINTS202005.0370.V2
Abstract: Bangladesh has adopted some special steps to control the quick spread of the COVID-19 pandemic situation. However, the local residents& rsquo knowledge, attitudes, and practices towards the disease have a direct impact on the success of the controlling measures taken by the state. This article explores knowledge (K) about preventions, attitude (A) to the disease, and practices (P) of preventing COVID-19 situation of the young age groups residing in Bangladesh. Quantitative data were collected online using a KAP questionnaire from 932 participants. Results show the population is generally aware of the symptoms, keeping social distance by staying home and are concerned about re-spreading after the lock-down period. However, they are quite unsure about the possible medicines frequently talked about in the media and the necessity of avoiding animal protein. One of the major limitations is, these findings should not be generalized due to the low number of participants compared to the total population in Bangladesh.
Publisher: AIP Publishing
Date: 06-2021
DOI: 10.1063/5.0052870
Abstract: Carbon materials and their unique properties have been extensively studied by molecular dynamics, thanks to the wide range of available carbon bond order potentials (CBOPs). Recently, with the increase in popularity of machine learning (ML), potentials such as Gaussian approximation potential (GAP), trained using ML, can accurately predict results for carbon. However, selecting the right potential is crucial as each performs differently for different carbon allotropes, and these differences can lead to inaccurate results. This work compares the widely used CBOPs and the GAP-20 ML potential with density functional theory results, including lattice constants, cohesive energies, defect formation energies, van der Waals interactions, thermal stabilities, and mechanical properties for different carbon allotropes. We find that GAP-20 can more accurately predict the structure, defect properties, and formation energies for a variety of crystalline phase carbon compared to CBOPs. Importantly, GAP-20 can simulate the thermal stability of C60 and the fracture of carbon nanotubes and graphene accurately, where CBOPs struggle. However, similar to CBOPs, GAP-20 is unable to accurately account for van der Waals interactions. Despite this, we find that GAP-20 outperforms all CBOPs assessed here and is at present the most suitable potential for studying thermal and mechanical properties for pristine and defective carbon.
Location: Japan
No related grants have been discovered for Daniel Hedman.