Elucidating the ligand shell structure and dynamics of Au683MBA32 gold nanocluster using molecular dynamics simulations
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2017Access restrictions
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Synthesising novel gold nanoparticles and -clusters can be often easier than characterising
them, and after experimental analysis many options for the possible molecular
formula of the cluster may remain as equally valid options. These options
may be investigated by quantum computational means, such as density functional
theory, but since DFT includes the electronic interactions, this might take time
and excessive effort if the amount of options is large. In this thesis a new way of
easing the computational task is introduced by a novel method, that combines a
guessing algorithm and molecular dynamics simulations. The algorithm produces
possible structures by utilising existing structures of known clusters, and molecular
dynamics are used to compute the dynamics and interactions in the structures to
differentiate between a good guess and a bad one with lower computational cost.
This method was utilised in this thesis on gold nanocluster Au683MBA32. Three
models were generated using the algorithm with the help of the information gained in
previous research (Pablo D. Jadzinsky et al. Science, 318 (5849):430–433, 2007 and
Azubel et al. Science, 345(6199):909–912, 2014). The structures were simulated in a
custom made GROMACS force-field (Pohjolainen et al. Journal of Chemical Theory and
Computation, 12(3):1342-1350, 2016) for 50 ns in NPT conditions. The resulting
trajectories were examined for indicators of internal stability and compared with
experimental results.
...
Uudenlaisten kultananopartikkelien ja -klusterien syntetisointi on usein helpompaa kuin niiden karakterisointi, ja useiden kokeellisten analyysien jälkeen mahdollisia molekyylikaavoja voi edelleen olla useita. Näitä vaihtoehtoja voidaan tutkia kvanttimekaanisten laskennallisten keinojen avulla, kuten käyttäen tiheysfunktionaaliteoriaa, mutta mikäli vaihtoehtoja on useita voi tulosten saaminen vaatia hyvin paljon aikaa ja vaivaa. Tämä johtuu siitä, että tiheysfunktionaaliteorian metodit sisältävät systeemin elektronien väliset vuorovaikutukset täsmällisesti, mikä vaatii paljon laskentatehoa ja -aikaa. Tässä tutkielmassa esitellään uusi keino tutkia klusterien rakennetta pienemmällä laskennallisella vaivalla. Tämä metodi yhdistää algoritmin, joka arvaa mahdollisia rakenteita uusille klustereille olemassa olevien rakenteiden
perusteella, ja molekyylidynamiikkasimulaatiot, joiden avulla pyritään erottamaan algoritmin antamat hyvät arvaukset huonoista.
Tätä metodia on tässä tutkielmassa käytetty Au683MBA32 kultananoklusterin pintarakenteen etsimiseen ja arviointiin. Kolme erilaista rakennetta luotiin algoritmin avulla aikaisemmissa tutkimuksissa (Pablo D. Jadzinsky et al. Science, 318 (5849):430–433, 2007 ja Azubel et al. Science, 345(6199):909–912, 2014) saatujen tietojen perusteella. Näitä rakenteita simuloitiin sitten tarkoitusta varten räätälöidyssä GROMACS voimakentässä (Pohjolainen et al. Journal of Chemical Theory and Computation, 12(3):1342-1350, 2016) 50 nanosekunnin ajan NPT olosuhteissa. Simulaatioiden tuloksena saaduista liikeradoista etsittiin sisäisestä stabiiliudesta kertovia tekijöitä, ja saatuja tuloksia verrattiin kokeellisten menetelmien avulla saatuihin tuloksiin.
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