| 1. |
- Li-Juan, Zhang, et al.
(författare)
-
A novel water layer structure inside nanobubbles at room temperature
- 2014
-
Ingår i: NUCL SCI TECH. - 1001-8042. ; 25:6, s. 060503-
-
Tidskriftsartikel (refereegranskat)abstract
- Molecularly thin water layer, with a hydrogen bonding network different from those in bulk water and ice, has unique properties and is generally involved in many important processes such as wetting, erosion, atmosphere chemical reaction, protein folding and biomolecular interaction. Here, we report a new water layer structure at room temperature, which is found inside nanobubbles by using synchrotron based scanning transmission soft X-ray microscopy (STXM). The three peaks 535.0, 536.8 and 540.9 eV at O K edge inside the nanobubbles show a novel characteristics of very thin water layers, which has never been observed before.
|
|
| 2. |
- Alhassan, Erwin, et al.
(författare)
-
Iterative Bayesian Monte Carlo for nuclear data evaluation
- 2022
-
Ingår i: NUCLEAR SCIENCE AND TECHNIQUES. - : Springer Nature. - 1001-8042 .- 2210-3147. ; 33:4
-
Tidskriftsartikel (refereegranskat)abstract
- In this work, we explore the use of an iterative Bayesian Monte Carlo (iBMC) method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library (TENDL) framework. The goal is to probe the model and parameter space of the TALYS code system to find the optimal model and parameter sets that reproduces selected experimental data. The method involves the simultaneous variation of many nuclear reaction models as well as their parameters included in the TALYS code. The `best' model set with its parameter set was obtained by comparing model calculations with selected experimental data. Three experimental data types were used: (1) reaction cross sections, (2) residual production cross sections, and (3) the elastic angular distributions. To improve our fit to experimental data, we update our 'best' parameter set-the file that maximizes the likelihood function-in an iterative fashion. Convergence was determined by monitoring the evolution of the maximum likelihood estimate (MLE) values and was considered reached when the relative change in the MLE for the last two iterations was within 5%. Once the final 'best' file is identified, we infer parameter uncertainties and covariance information to this file by varying model parameters around this file. In this way, we ensured that the parameter distributions are centered on our evaluation. The proposed method was applied to the evaluation of p+ Co-59 between 1 and 100 MeV. Finally, the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2019, JENDL/He-2007 and JENDL-4.0/HE nuclear data libraries.
|
|
