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- ItemDegradation analysis of tribologically loaded carbon nanotubes and carbon onions([London] : Macmillan Publishers Limited, 2023) MacLucas, T.; Grützmacher, P.; Husmann, S.; Schmauch, J.; Keskin, S.; Suarez, S.; Presser, V.; Gachot, C.; Mücklich, F.Coating laser-patterned stainless-steel surfaces with carbon nanotubes (CNT) or carbon onions (CO) forms a tribological system that provides effective solid lubrication. Lubricant retention represents the fundamental mechanism of this system, as storing the particles inside the pattern prevents lubricant depletion in the contact area. In previous works, we used direct laser interference patterning to create line patterns with three different structural depths on AISI 304 stainless-steel platelets. Electrophoretic deposition subsequently coated the patterned surfaces with either CNTs or COs. Ball-on-disc friction tests were conducted to study the effect of structural depth on the solid lubricity of as-described surfaces. The results demonstrated that the shallower the textures, the lower the coefficient of friction, regardless of the applied particle type. This follow-up study examines the carbon nanoparticles’ structural degradation after friction testing on substrates patterned with different structural depths (0.24, 0.36, and 0.77 µm). Raman characterization shows severe degradation of both particle types and is used to classify their degradation state within Ferrari’s three-stage amorphization model. It was further shown that improving CNT lubricity translates into increasing particle defectivity. This is confirmed by electron microscopy, which shows decreasing crystalline domains. Compared to CNTs, CO-derived tribofilms show even more substantial structural degradation.
- ItemNew methodology to process shifted excitation Raman difference spectroscopy data : a case study of pollen classification([London] : Macmillan Publishers Limited, 2020) Korinth, F.; Mondol, A.S.; Stiebing, C.; Schie, I.W.; Krafft, C.; Popp, J.Shifted excitation Raman difference spectroscopy (SERDS) is a background correction method for Raman spectroscopy. Here, the difference spectra were directly used as input for SERDS-based classification after an optimization procedure to correct for photobleaching of the autofluorescence. Further processing included a principal component analysis to compensate for the reduced signal to noise ratio of the difference spectra and subsequent classification by linear discriminant analysis. As a case study 6,028 Raman spectra of single pollen originating from plants of eight different genera and four different growth habits were automatically recorded at excitation wavelengths 784 and 786 nm using a high-throughput screening Raman system. Different pollen were distinguished according to their growth habit, i.e. tree versus non-tree with an accuracy of 95.9%. Furthermore, all pollen were separated according to their genus, providing also insight into similarities based on their families. Classification results were compared using spectra reconstructed from the differences and raw spectra after state-of-art baseline correction as input. Similar sensitivities, specificities, accuracies and precisions were found for all spectra with moderately background. Advantages of SERDS are expected in scenarios where Raman spectra are affected by variations due to detector etaloning, ambient light, and high background.