Growth of thiol-coated Au-nanoparticle Langmuir monolayers through a 2D-network of disk-like islands

RSC Advances ◽  
2016 ◽  
Vol 6 (15) ◽  
pp. 12326-12336 ◽  
Author(s):  
Mala Mukhopadhyay ◽  
S. Hazra

Formation of 2D-networked structures of disk-like islands for ultrathin Langmuir–Schaefer (LS) films of thiol-coated Au-nanoparticles (DT-AuNPs) on H-passivated Si substrates is evidenced for the first time, by X-ray scattering data and AFM images.

2018 ◽  
Vol 122 (45) ◽  
pp. 10320-10329 ◽  
Author(s):  
Amin Sadeghpour ◽  
Marjorie Ladd Parada ◽  
Josélio Vieira ◽  
Megan Povey ◽  
Michael Rappolt

1995 ◽  
Author(s):  
Yibin Zheng ◽  
Peter C. Doerschuk ◽  
John E. Johnson

2020 ◽  
Author(s):  
Steve P. Meisburger ◽  
Da Xu ◽  
Nozomi Ando

AbstractMixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput, or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori. To address this issue, we introduce the REGALS method (REGularized Alternating Least Squares), which incorporates simple expectations about the data as prior knowledge and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, which makes it well-suited for exploring datasets with unknown species. Here we apply REGALS to analyze experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing, and time-resolved temperature jump. Based on its performance with these challenging datasets, we anticipate that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and python and is available freely as an open-source software package.


2008 ◽  
Vol 95 (5) ◽  
pp. 2356-2367 ◽  
Author(s):  
Norbert Kučerka ◽  
John F. Nagle ◽  
Jonathan N. Sachs ◽  
Scott E. Feller ◽  
Jeremy Pencer ◽  
...  

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