Curious to know your opinion on any characterization technique or step that should be looked into more detail & more often covered in order to increase the quality and robustness of experimental results.
Hey there Miguel Pereira-Silva!
As an engineer and researcher, I can tell you Miguel Pereira-Silva that one often overlooked but crucial step in nanoparticle characterization is the detailed assessment of surface chemistry. While many studies focus on size, shape, and distribution, the surface chemistry plays a pivotal role in determining the interaction of nanoparticles with biological systems and their overall stability.
For instance, methods like X-ray Photoelectron Spectroscopy (XPS) or Fourier Transform Infrared Spectroscopy (FTIR) provide deep insights into the chemical composition and functional groups present on the nanoparticle surface. These details are vital because they influence properties such as biocompatibility, reactivity, and aggregation behavior in different environments.
Moreover, a thorough understanding of surface modifications can lead to better reproducibility of experimental results. Often, minor variations in surface chemistry can lead to significant differences in biological responses or catalytic activities, which are critical for applications in drug delivery, imaging, and catalysis.
An interesting article to read:
Article Hierarchical Ni-Mn Double Layered/Graphene Oxide with Excell...
So, enhancing focus on surface chemistry characterization can vastly improve the quality and robustness of nanoparticle-related research. It’s an area that definitely deserves more attention to ensure comprehensive and reliable experimental outcomes.
Hope this gives you Miguel Pereira-Silva some valuable insight!
Cheers
"increase the quality and robustness of experimental results" - this clearly leads to experimental design approaches. Fractional factorial design helps in screening factors that influence specific properties, while factorial design is used to understand main effects and interaction effects, followed by conducting gradient descent sequential experiments. Eventually, response surface methodologies, such as central composite design or Box-Behnken design, are employed to investigate potential quadratic effects and ultimately to statistically optimize the properties of nanoparticles.