Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors

• Joyita Roy and
• Kunal Roy

Beilstein J. Nanotechnol. 2024, 15, 297–309, doi:10.3762/bjnano.15.27

• model. Model development Zeta potential QSPR model To develop the property-based QSPR model, the training set was utilized for model development. The training set of 13 compounds was processed through feature selection via stepwise regression and genetic algorithm (GA) [29]. After feature selection, the

Atomic-level characterization and cilostazol affinity of poly(lactic acid) nanoparticles conjugated with differentially charged hydrophilic molecules

• María Francisca Matus,
• Martín Ludueña,
• Cristian Vilos,
• Iván Palomo and
• Marcelo M. Mariscal

Beilstein J. Nanotechnol. 2018, 9, 1328–1338, doi:10.3762/bjnano.9.126

• docking calculations. AutoDock uses a rapid grid-based method for energy evaluation. A grid volume large enough to cover the entire surface of the PLA core was built (126 × 126 × 126 Å3) using a grid spacing of 0.5 Å. The grid parameters were generated using AutoGrid 4.2.6 and the Lamarckian genetic

• algorithm (LGA) was used to perform a search of the conformational space of the drug. The docking runs were set to 100. The docking poses were analyzed by examining their binding energy score and the most visited “hot spots” (putative binding sites). The most energetically favorable conformations were

Electron interactions with the heteronuclear carbonyl precursor H₂FeRu₃(CO)₁₃ and comparison with HFeCo₃(CO)₁₂: from fundamental gas phase and surface science studies to focused electron beam induced deposition

• Ragesh Kumar T P,
• Paul Weirich,
• Lukas Hrachowina,
• Marc Hanefeld,
• Ragnar Bjornsson,
• Helgi Rafn Hrodmarsson,
• Sven Barth,
• D. Howard Fairbrother,
• Michael Huth and
• Oddur Ingólfsson

Beilstein J. Nanotechnol. 2018, 9, 555–579, doi:10.3762/bjnano.9.53

Comparative study of post-growth annealing of Cu(hfac)₂, Co₂(CO)₈ and Me₂Au(acac) metal precursors deposited by FEBID

• Marcos V. Puydinger dos Santos,
• Aleksandra Szkudlarek,
• Artur Rydosz,
• Carlos Guerra-Nuñez,
• Fanny Béron,
• Kleber R. Pirota,
• Stanislav Moshkalev,
• José Alexandre Diniz and
• Ivo Utke

Beilstein J. Nanotechnol. 2018, 9, 91–101, doi:10.3762/bjnano.9.11

• methods are not utilized in depositions of non-noble metals in order to avoid oxidation of the metals. For W–C deposits, an electrical conductivity improvement of one order of magnitude was obtained using a genetic algorithm to optimize the deposition parameters [44]. In addition, high-purity W deposits

Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C₆₀ fullerene using Nano-QSPR

• Piotr Urbaszek,
• Agnieszka Gajewicz,
• Celina Sikorska,
• Maciej Haranczyk and
• Tomasz Puzyn

Beilstein J. Nanotechnol. 2017, 8, 752–761, doi:10.3762/bjnano.8.78

• adsorption energy values (ΔEads) for 32 Br/Cl dibenzo-p-dioxin congeners adsorbed on a C60 fullerene surface and carefully selected structural descriptors, we developed a Nano-QSPR model, employing a hybrid genetic algorithm, partial least squares linear regression (GA-PLS), as the modeling method. The

• Information File 1. Holland’s genetic algorithm (GA) [61] was used for the selection of the optimal combination of molecular descriptors and redundancy elimination in the structural data. Partial least squares (PLS) regression was applied as the method of modeling to solve the common problem of co-linearity

Calculating free energies of organic molecules on insulating substrates

• Julian Gaberle,
• David Z. Gao and
• Alexander L. Shluger

Beilstein J. Nanotechnol. 2017, 8, 667–674, doi:10.3762/bjnano.8.71

• atom type inside molecules and the KCl surface using a genetic algorithm method. A detailed discussion of this potential-fitting method can be found in prior publications [25][43]. Briefly, a fitting dataset composed of 240 configurations was generated using density functional theory (DFT). These

• second-order Møller–Plesset perturbation theory (MP2) [51][52] calculations of smaller molecular fragments [43]. A genetic algorithm was employed to fit parameters against that dataset, where the fitness criterion was defined as the force between the molecule and the surface. The total population size

Tandem polymer solar cells: simulation and optimization through a multiscale scheme

• Fanan Wei,
• Ligang Yao,
• Fei Lan,
• Guangyong Li and
• Lianqing Liu

Beilstein J. Nanotechnol. 2017, 8, 123–133, doi:10.3762/bjnano.8.13

• photovoltaics to achieve high performance. Consistency between the optimization results and the reported experimental results proved the effectiveness of the proposed simulation scheme. Keywords: genetic algorithm; Monte Carlo simulation; simplex searching; tandem polymer solar cells; Introduction Polymer

• -benzothiadiazole)])/PCBM (PCPDTBT/PCBM). The J–V curves of the device were acquired through a multiscale simulation scheme. Then performance indices were evaluated and related to the thickness, inner morphologies and the weight ratio of the active layers. Finally, using a simplex searching algorithm and genetic

• algorithm (GA), a global optimal PCE value was found and the corresponding device parameters were obtained. In order to prove the viability of our proposed simulation approach, two different configurations of the tandem devices, as shown in Figure 1b, were both tested and compared with each other

In situ growth optimization in focused electron-beam induced deposition

• Paul M. Weirich,
• Marcel Winhold,
• Christian H. Schwalb and
• Michael Huth

Beilstein J. Nanotechnol. 2013, 4, 919–926, doi:10.3762/bjnano.4.103

• Paul M. Weirich Marcel Winhold Christian H. Schwalb Michael Huth Physikalisches Institut, Goethe Universität, Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany 10.3762/bjnano.4.103 Abstract We present the application of an evolutionary genetic algorithm for the in situ optimization of

• largely suppressed. The presented technique can be applied to all beam-induced deposition processes and has great potential for a further optimization or tuning of parameters for nanostructures that are prepared by FEBID or related techniques. Keywords: electron beam induced deposition; genetic algorithm

• changing the deposition parameters. Here, we present a first implementation of such a feedback control mechanism and employ an evolutionary genetic algorithm (GA) for the in situ optimization of the electrical conductivity of nanostructures that are prepared by FEBID [17]. By using the time gradient of the