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Biofouling and microbiologically influenced corrosion are processes of material deterioration that originate from the attachment of microorganisms as quickly as the material is immersed in a nonsterile environment. Stainless steels, despite their wide use in different industries and as appliances and implant materials, do not possess inherent antimicrobial properties. Changes in hygiene legislation and increased public awareness of product quality makes it necessary to devise control methods that inhibit biofilm formation or to act at an early stage of the biofouling process and provide the release of antimicrobial compounds on a sustainable basis and at effective level. These antibacterial stainless steels may find a wide range of applications in fields, such as kitchen appliances, medical equipment, home electronics, and tools and hardware. The purpose of this study was to obtain antibacterial stainless steel and thus mitigate the microbial colonization and bacterial infection. Copper is known as an antibacterial agent; in contrast, niobium has been demonstrated to improve the antimicrobial effect of copper by stimulating the formation of precipitated copper particles and its distribution in the matrix of the stainless steel. Thus, we obtained slides of 3.8% copper and 0.1% niobium alloyed stainless steel; subjected them to three different heat treatment protocols (550 degrees C, 700 degrees C, and 800 degrees C for 100, 200, 300, and 400 hours); and determined their antimicrobial activities by using different initial bacterial cell densities and suspending solutions to apply the bacteria to the stainless steels. The bacterial strain used in these experiments was Escherichia coli CCM 4517. The best antimicrobial effects were observed in the slides of stainless steel treated at 700 degrees C and 800 degrees C using an initial cell density of approximately 10(5) cells ml(-1) and phosphate-buffered saline as the solution in which the bacteria came into contact with copper and niobium-containing steel.

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Stainless steel has proved to be an important material to be used in a wide range of applications. For this reason, ensuring the durability of this alloy is essential. In this work, pitting corrosion behaviour of EN 1.4404 stainless steel is evaluated in marine environment in order to develop a model capable of predicting its pitting corrosion status by an automatic way. Although electrochemical techniques and microscopic analysis have been shown to be very useful tools for corrosion studies, these techniques may present some limitationus. With the aim to solve these drawbacks, a three-step model based on Artificial Neural Networks (ANNs) is proposed. The results reveal that the model can be used to predict pitting corrosion status of this alloy with satisfactory sensitivity and specificity with no need to resort to electrochemical tests or microscopic analysis. Therefore, the proposed model becomes a useful tool to predict the behaviour of the material against pitting corrosion in saline environment automatically.