Laser ablation mass spectrometry (LA-MS) has always been an interesting method for the elemental analysis of solid samples. Chemical analysis with a laser requires small amounts of material. Depending on the analytical detection system, subpicogram quantities may be sufficient. In addition, a focused laser beam permits the spatial characterization of heterogeneity in solid samples typically with micrometer resolution in terms of lateral and depth dimensions. With the advent of high-energy, ultra-short pulse lasers, new possibilities arise. The task of this review is to discuss the principle differences between the ablation process of short (>1 ps) and ultra-short (<1 ps) pulses. Based on the timescales and the energy balance of the process that underlies an ablation event, it will be shown that ultra-short pulses are less thermal and cause less collateral damages than longer pulses. The confinement of the pulse energy to the focal region guarantees a better spatial resolution in all dimensions and improves the analytical figures of merit (e.g., fractionation). Applications that demonstrate these features and that will be presented are in-depth profiling of multi-layer samples and the elemental analysis of biological materials.

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Pulsed laser welding of metal-polymer sandwich materials using pulse shaping

The laser generation of size-controlled semiconductor nanoparticle formation under gas phase conditions is investigated. It is shown that the size distribution can be changed if picosecond pulse sequences of tailored ultra short laser pulses (<200 fs) are employed. By delivering the laser energy in small packages, a temporal energy flux control at the target surface is achieved, which results in the control of the thermodynamic pathway the material takes. The concept is tested with silicon and germanium, both materials with a predictable response to double pulse sequences, which allows deduction of the materials' response to complicated pulse sequences. An automatic, adaptive learning algorithm was employed to demonstrate a future strategy that enables the definition of more complex optimization targets such as particle size on materials less predictable than semiconductors.