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A series of different nanoparticle suspensions (Gold, Latex, and SiO2 in varying concentrations) were microprinted onto TEM grids in a 4 x 4 array in the concentration range 7x10^8 to 1x10^11 NP/mL and imaged with SEM and TSEM. Concentrations and printing conditions (temperature, relative humidity) were varied in order to minimize the coffee ring effect.
This document is a collection of three JRC Technical Reports that together form the “NanoDefine Methods Manual”, which has been developed within the NanoDefine project ‘Development of an integrated approach based on validated and standardized methods to support the implementation of the EC recommendation for a definition of nanomaterial’, funded by the European Union’s 7th Framework Programme, under grant agreement 604347. The overall goal of the NanoDefine project was to support the implementation of the European Commission Recommendation on the definition of nanomaterial (2011/696/EU). The project has developed an integrated empirical approach, which allows identifying a material as a nano- or not a nanomaterial according to the EC Recommendation. The NanoDefine Methods Manual consists of three parts: Part 1: The NanoDefiner Framework and Tools, which covers the NanoDefiner framework, general information on measurement methods and performance criteria, and tools developed by NanoDefine such as a materials categorisation system, a decision support flow scheme and an e-tool. Part 2: Evaluation of Methods, which discusses the outcome of the evaluation of the nanomaterials characterisation methods for measuring size. Part 3: Standard Operating Procedures (SOPs), which presents the 23 Standard Operating Procedures developed within the NanoDefine project. In this combined document, these three parts are included as stand-alone reports, each having its own abstract, table of contents, page, table and figure numbering, and references.
These files contain cell models for TOPAS/Geant4 and the inclusion of nano particles in particle scattering simulations. A simple spherical cell with nanoparticles can be generated in a fast manner. The user has the option to include the following organelles: nucleus, mitochondria, cell membrane. Additionally nanoparticles can be included in the cytosol and at the surface of the nucleus and/or the mitochondria.
The C++ classes in this repository extend the functionality of the TOPAS (http://www.topasmc.org/) Monte-Carlo program, which is itself a wrapper of the Geant4 MCS Toolkit (http://geant4.org). The sourcecode together with examples and scorers are provided.
"If you use this extension please cite the following literature:
Hahn, M.B., Zutta Villate, J.M. "Combined cell and nanoparticle models for TOPAS to study radiation dose enhancement in cell organelles." Sci Rep 11, 6721 (2021).
https://doi.org/10.1038/s41598-021-85964-2 "
Nanoparticles (NP) have become important materials for a variety of chemical technologies. The enhance surface-area-to-volume ratio of NPs is very high, making them excellent for use as catalyst, in analytical assays, and for antimicrobial applications.
Nickel NPs have exhibited immense potential as important catalyst for the Sabatier reaction, i.e. converting waste to energy via transformation of CO2 into CH4, and could replace the rare earth elements such as Ru, PT, or Rh. In this work we describe the solvothermal synthesis of monometallic and bimetallic nickel nanoparticles.
Monodisperse monometallic Ni nanoparticles were synthesized using Oleylamin as solvent and reducing agent. The nanoparticles were investigated using small angle scattering (SAXS), scanning transmission electron microscopy (STEM) and energy dispersive X-ray spectroscopy (EDX), showing that the catalytically active sites are accessible. However, Ni has a high propensity to undergo oxidation, and becoming deactivated by coke formation. Hence, we further explore the preparation of bimetallic NPs, where a second metal is added to stabilize the Ni.
Bimetallic Cu-Ni NPs were synthesized by simultaneous solvothermal reduction. These bimetallic NPs exhibit excellent catalytic properties are promising candidates to be used as catalysts for efficient energy storage.
Gold nanocubes with a monodispersed size distribution (edge = 55 nm) deposited on a silicon wafer. These tiny uniform cubes were produced as reference nanoparticles in the frame of the European project nPSize - Improved traceability chain of nanoparticle size measurements. SEM Image acquired at 10 kV with an in-lens secondary electron detector. Image colored in ImageJ using LUT “Orange hot.” Published in Microscopy and Microanalysis 25(S2) (2019) 2328.
Gold nanocubes with a monodispersed size distribution (edge = 55 nm) deposited on a silicon wafer. These tiny uniform cubes were produced as reference nanoparticles within the EMPIR project nPSize - Improved traceability chain of nanoparticle size measurements. SEM image acquired at 10 kV with an in-lens secondary electron detector. Image colored in ImageJ using LUT “Orange hot.” Published in Microscopy and Microanalysis 25(S2) (2019) 2328.
The present study addresses the capability of measurement of relative particle number concentration by scanning electron microscopy for model bimodal silica and gold samples prepared in the frame of the European research project “nPSize - Improved traceability chain of nanoparticle size measurements” as candidate reference nanoparticles.
In this paper one refined approach is applied to determine the exact orientation of bipyramidal TiO2 nanoparticles prepared with good dispersion as almost isolated particles on a carbon TEM grid. The advantages of the recently developed high-throughput Transmission Kikuchi Diffraction (TKD) are used to identify quickly and reliably the geometrical orientation of the crystalline TiO2 nanoparticle bipyramids (anatase) on a statistically relevant number of particles.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.