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Raman spectroscopy is a common identification and analysis technique used in research and manufacturing industries. This study investigates the use of Raman spectroscopy and deep learning techniques for identifying various nanofabrication chemicals. Four solvents and SU-8 developer were identified inside common chemical storage and distribution containers. The containers attenuated the spectra and contributed varying amounts of background fluorescence, making manual identification difficult. Two varieties of SU-8 photoresist were differentiated inside amber glass jars, and cured samples of three ratios of polydimethylsiloxane (PDMS) were differentiated using Raman microscopy. The neural network accurately identified the nanofabrication chemicals 100% of the time, without additional preprocessing. This investigation demonstrates the use of Raman spectroscopy and neural networks for the identification of nanofabrication chemicals and makes recommendations for use in other challenging identification applications. 

Several key features of nanoscale friction phenomena observed in experiments, including the stick-slip to smooth sliding transition and the velocity and temperature dependence of friction, are often described by reduced-order models. The most notable of these are the thermal Prandtl–Tomlinson model and the multibond model. Here we present a modified multibond (mMB) model whereby a physically-based criterion—a critical bond stretch length—is used to describe interfacial bond breaking. The model explicitly incorporates damping in both the cantilever and the contacting materials. Comparison to the Fokker–Planck formalism supports the results of this new model, confirming its ability to capture the relevant physics. Furthermore, the mMB model replicates the near-logarithmic trend of increasing friction with lateral scanning speed seen in many experiments. The model can also be used to probe both correlated and uncorrelated stick slip. Through greater understanding of the effects of damping and noise in the system and the ability to more accurately simulate a system with multiple interaction sites, this model extends the range of frictional systems and phenomena that can be investigated. This article is part of the theme issue ‘Nanocracks in nature and industry’. 

Abstract In atomic force microscopy, the cantilever probe is a critical component whose properties determine the resolution and speed at which images with nanoscale resolution can be obtained. Traditional cantilevers, which have moderate resonant frequencies and high quality factors, have relatively long response times and low bandwidths. In addition, cantilevers can be easily damaged by excessive deformation, and tips can be damaged by wear, requiring them to be replaced frequently. To address these issues, new cantilever probes that have hollow cross‐sections and walls of nanoscale thicknesses made of alumina deposited by atomic layer deposition are introduced. It is demonstrated that the probes exhibit spring constants up to ≈100 times lower and bandwidths up to ≈50 times higher in air than their typical solid counterparts, allowing them to react to topography changes more quickly. Moreover, it is shown that the enhanced robustness of the hollow cantilevers enables them to withstand large bending displacements more readily and to be more resistant to tip wear.