Controlling alloy formation and optical properties by galvanic replacement of sub-20 nm silver nanoparticles in organic media

Galvanic replacement is a versatile synthetic strategy for the synthesis of alloy and hollow nanostructures. The structural evolution of single crystalline and multiply twinned nanoparticles <20 nm in diameter and capped with oleylamine is systematically studied. Changes in chemical composition are dependent on the size and crystallinity of the parent nanoparticle. The effects of reaction temperature and rate of precursor addition are also investigated. Galvanic replacement of single crystal spherical and truncated cubic nanoparticles follows the same mechanism to form hollow octahedral nanoparticles, a mechanism which is not observed for galvanic replacement of Ag templates in aqueous systems. Multiply twinned nanoparticles can form nanorings or solid alloys by manipulating the reaction conditions. Oleylamine-capped Ag nanoparticles are highly adaptable templates to synthesize a range of hollow and alloy nanostructures with tuneable localised surface plasmon resonance.

Detection of pitting corrosion in steel using image processing

This paper presents an image processing based detection method for detecting pitting corrosion in steel structures. High Dynamic Range (HDR) imaging has been carried out in this regard to demonstrate the effectiveness of such relatively inexpensive techniques that are of immense benefit to Non – Destructive – Tesing (NDT) community. The pitting corrosion of a steel sample in marine environment is successfully detected in this paper using the proposed methodology. It is observed, that the proposed method has a definite potential to be applied to a wider range of applications.

Improved image analysis based corrosion assessment using preprocessing techniques

The importance of non-destructive techniques (NDT) in structural health monitoring programmes is being critically felt in the recent times. The quality of the measured data, often affected by various environmental conditions can be a guiding factor in terms usefulness and prediction efficiencies of the various detection and monitoring methods used in this regard. Often, a preprocessing of the acquired data in relation to the affecting environmental parameters can improve the information quality and lead towards a significantly more efficient and correct prediction process. The improvement can be directly related to the final decision making policy about a structure or a network of structures and is compatible with general probabilistic frameworks of such assessment and decision making programmes. This paper considers a preprocessing technique employed for an image analysis based structural health monitoring methodology to identify sub-marine pitting corrosion in the presence of variable luminosity, contrast and noise affecting the quality of images. A preprocessing of the gray-level threshold of the various images is observed to bring about a significant improvement in terms of damage detection as compared to an automatically computed gray-level threshold. The case dependent adjustments of the threshold enable to obtain the best possible information from an existing image. The corresponding improvements are observed in a qualitative manner in the present study.