Silver sulfide as a staining agent for scanning electron microscopy of nylon 6/MXD6 blends

BACKGROUND: In transmission and scanning electron microscopy imaging, the ability to obtain sufficient contrast between the components of a blend when they are both of a similar chemical structure still remains problematic. This paper investigates the domain morphology of a polymer blend containing two polyamides, nylon 6 and the semi-aromatic polyamide poly(m-xylene adipamide) (MXD6), using scanning electron microscopy in backscattered electron imaging mode. The efficiency of three staining agents, ruthenium tetroxide, phosphotungstic acid and silver sulfide, in obtaining optimum phase contrast between the two polymers is discussed.
RESULTS: The use of silver sulfide as a staining agent was found to be a fast and reliable approach which required basic sample preparation and provided excellent compositional contrast between the phases present in the nylon 6/MXD6 blends compared to the other staining agents.
CONCLUSIONS: The technique described in this paper is believed to be a novel and versatile method that has the potential to further improve the ability to study complex polymer blends where one polymer contains an aromatic ring.

Quantitative secondary electron imaging for work function extraction at atomic level and layer identification of graphene

Two-dimensional (2D) materials usually have a layer-dependent work function, which require fast and accurate detection for the evaluation of their device performance. A detection technique with high throughput and high spatial resolution has not yet been explored. Using a scanning electron microscope, we have developed and implemented a quantitative analytical technique which allows effective extraction of the work function of graphene. This technique uses the secondary electron contrast and has nanometre-resolved layer information. The measurement of few-layer graphene flakes shows the variation of work function between graphene layers with a precision of less than 10 meV. It is expected that this technique will prove extremely useful for researchers in a broad range of fields due to its revolutionary throughput and accuracy.