Algorithmic Reconstruction of Multimodal Copper Wire Explosion Products from Extinction Spectra

Wire explosion (WE) inherently generates particle ensembles spanning the nano- to microscale, posing challenges for conventional characterization methods in terms of capturing the full particle population. To address this issue, spectrophotometric analysis combined with algorithmic spectrum reconstruction based on Mie theory and constrained distribution models were employed to characterize copper WE products formed in aqueous surroundings within the 4–12 kV discharge voltage range. Three independent fitting strategies, specifically a semimanual fitting, an evolutionary algorithm, and a grid search, were applied to retrieve the size distributions and relative shares of copper and copper oxide particles as a function of discharge voltage. Based on experimental and theoretical findings, lognormal and normal distributions across the 10–300 nm diameter range were assumed as constraints for oxide and metallic fractions, respectively. The reconstructed metallic copper population exhibited mean diameters ranging from 123 to 181 nm, while oxidized fractions followed lognormal distributions centred near 10 nm mode diameters. Voltage-dependent trends revealed an optimal discharge regime between 6 kV and 8 kV, where the exploded fraction reached approximately 63% and the metallic mass share exceeded 80%. These results confirmed that spectrophotometry represents an essential tool for the quantitative characterization of such complex, wide-range systems.