Plasmonic resonances in metallic nanoparticles have been used since antiquity to colour glasses. The use of metal nanostructures for surface colourization has attracted considerable interest following recent developments in plasmonics. However, current top-down colourization methods are not ideally suited to large-scale industrial applications. Here we use a bottom-up approach where picosecond laser pulses can produce a full palette of non-iridescent colours on silver, gold, copper and aluminium. We demonstrate the process on silver coins weighing up to 5 kg and bearing large topographic variations (∼1.5 cm). We find that colours are related to a single parameter, the total accumulated fluence, making the process suitable for high-throughput industrial applications. Statistical image analyses of laser-irradiated surfaces reveal various nanoparticle size distributions. Large-scale finite-difference time-domain computations based on these nanoparticle distributions reproduce trends seen in reflectance measurements, and demonstrate the key role of plasmonic resonances in colour formation.

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Picosecond laser pulses have been used as a surface colouring technique for noble metals, where the colours result from plasmonic resonances in the metallic nanoparticles created and redeposited on the surface by ablation and deposition processes. This technology provides two datasets which we use to train artificial neural networks, data from the experiment itself (laser parameters vs. colours) and data from the corresponding numerical simulations (geometric parameters vs. colours). We apply deep learning to predict the colour in both cases. We also propose a method for the solution of the inverse problem – wherein the geometric parameters and the laser parameters are predicted from colour – using an iterative multivariable inverse design method.

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A recently developed Fe-C interatomic potential has been used to investigate carbon in highly supersaturated ␣ iron with an emphasis on the possible ways in which carbon can arrange itself on the octahedral sites of the tetragonally distorted ␣-iron lattice. Focusing particularly on the composition Fe 16 C 2 , the embedded atom method potential used gives the same ground-state structure of Fe 16 C 2 as density-functional-theory calculations. Moreover, when computing C-C interactions between two carbon atoms at 0 K with energy minimization, the preferred separation distance corresponds exactly to the octahedral site positions expected in Fe 16 C 2 . It has been shown with molecular dynamics that the carbon atoms in Fe 16 C 2 change how they order on octahedral sites during heating and during cooling. Finally, when investigating the interaction energy of Fe 1−x C x for different carbon compositions, it is found that there is a clear minimum at x = 0.11 for the fully ordered structure, corresponding to Fe 16 C 2 composition. The results of these calculations are discussed with particular reference to carbon ordering in ferrous martensite.

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