The high negative bias of a sample in a scanning electron microscope constitutes the “cathode lens” with a strong electric field just above the sample surface. This mode offers a convenient tool for controlling the landing energy of electrons down to units or even fractions of electronvolts with only slight readjustments of the column. Moreover, the field accelerates and collimates the signal electrons to earthed detectors above and below the sample, thereby assuring high collection efficiency and high amplification of the image signal. One important feature is the ability to acquire the complete emission of the backscattered electrons, including those emitted at high angles with respect to the surface normal. The cathode lens aberrations are proportional to the landing energy of electrons so the spot size becomes nearly constant throughout the full energy scale. At low energies and with their complete angular distribution acquired, the backscattered electron images offer enhanced information about crystalline and electronic structures thanks to contrast mechanisms that are otherwise unavailable. Examples from various areas of materials science are presented.

Mechanical properties of complex steels are affected by their multi-phase structure. Scanning electron microscopy (SEM) is routinely used for characterizing dual-phase (DP) steels, although the identification of steel constituents is not straightforward. In fact, there are several ways of enabling the ferrite–martensite segmentation by SEM, and a wide range of electron energies can be utilized. This study demonstrates the phase identification of DP steels at high, low and extremely low landing energies of the primary electrons from tens of keV to tens of eV. Visualization of the specimen surface at very low landing energies has been achieved by inserting an earthed detector between the pole piece and the negatively biased specimen. This ‘cathode lens mode’ enables the use of the full energy range up to the primary electron energies. It has been found that extremely slow electrons (<100 eV) are exceptionally suitable for separation of the martensite from the ferrite matrix due to high surface sensitivity, enabling visualization of very fine features. Moreover, the channelling contrast is significantly suppressed at the landing energy of tens of eV of the primary electrons, which enables separation of the phases clearly even in the images acquired at low magnification. The contrast between the phases at tens of eV can be explained by the different thickness of native oxide covering the martensite and the ferrite phase.

The presence of Al-Si coating on 22MnB5 leads to the formation of large ferritic bands in the dominantly martensitic microstructure of butt laser welds. Rapid cooling of laser weld metal is responsible for insufficient diffusion of coating elements into the steel and incomplete homogenization of weld fusion zone. The Al-rich regions promote the formation of ferritic solid solution. Soft ferritic bands cause weld joint weakening. Laser welds reached only 64% of base metal’s ultimate tensile strength, and they always fractured in the fusion zone during the tensile tests. We implemented hybrid laser-TIG welding technology to reduce weld cooling rate by the addition of heat of the arc. The effect of arc current on weld microstructure and mechanical properties was investigated. Thanks to the slower cooling, the large ferritic bands were eliminated. The hybrid welds reached greater ultimate tensile strength compared to laser welds. The location of the fracture moved from the fusion zone to a tempered heat-affected zone characterized by a drop in microhardness. The minimum of microhardness was independent of heat input in this region.

Polyurethane nanofibres in situ doped with silver nanoparticles arising from three different silver precursors as well as zero-valent silver nanoparticles were prepared using free-surface electrospinning with no post-treatment. Throughout the experiment, the best results from all combinations tested were achieved for silver behenate doped nanofibres. These had homogenous nanoparticle coverage (some particles 99% as well as inhibition zones of 1.20 ± 0.12 mm for E. coli, 1.45 ± 0.08 mm for Staphylococcus aureus and 0.62 ± 0.12 mm for Pseudomonas aeruginosa at concentrations corresponding to 1 McFarland. Moreover, after 168 h, silver behenate doped nanofibres displayed the lowest silver leaching (5.8% of original silver content) of all samples prepared in this study. These nanofibres, therefore, represent a promising alternative to current silver-doped nanofibres prepared in situ.

The success of machine learning (ML) models in object or pattern recognition naturally leads to ML being employed in the classification of the microstructure of steel surfaces. Light optical microscopy (LOM) is the traditional imaging process in this field. However, the increasing use of ML to extract or relate more aspects of the aforementioned materials and the limitations of LOM motivated us to provide an improvement to the established image acquisition process. In essence, we perform style transfer from LOM to scanning electron microscopy (SEM) combined with “intelligent” upscaling. This is achieved by employing an ML model trained on a multimodal dataset to generate an SEM-like image from the corresponding LOM image. This transformation, in our opinion, which is corroborated by a detailed analysis of the source, target and prediction, successfully pushes the limits of LOM in the case of steel surfaces. The expected consequence is the improvement of the precise characterization of advanced multiphase steels’ structure based on these transformed LOM images.