Infrared spectroscopy provides a variety of information simultaneously, such as the pressure applied to the lubrication field, the thickness and molecular orientation of the oil component under a shearing condition, and the concentration of the lubricant if it is a mixture, and so on. Therefore, in situ observation of the lubrication interface using infrared spectroscopy is very useful for understanding tribological phenomena that change with friction time. In addition, because the analysis probe, which is a focused infrared light from outside the system, is irradiated, two-dimensional visualization of information is possible in the actual environment of the lubrication field. In this review, several examples of in situ observations of lubrication interfaces using infrared spectroscopy will be presented to illustrate the usefulness of infrared spectroscopy in elucidating tribological phenomena.

To understand tribology, especially lubrication phenomena, in detail, it is important to observe the behavior of molecules at interfaces in sliding conditions in real time. In this explanation, we review the recent progress of in-situ observation techniques using non-linear optical spectroscopy for friction interfaces, including previous studies and examples of the our results.

Quantum beam analysis is useful for understanding the elementary processes of tribological phenomena, and is particularly key to investigating the correlation between the microscopic structure of lubricating oil and sliding surfaces and macroscopic friction properties. In this paper, some examples of how the results of quantum beam analysis using X-ray and neutron beams are linked to tribological phenomena, and the future prospects are introduced.

In the field of tribology, AFM is used to measure surface topography and friction force distribution. By utilizing this AFM as a nano-tribo-tester for single asperity contact, it is possible to observe the process of tribo-film formation in situ. Moreover, with the recently developed FM-AFM, it has become possible to visualize adsorption layers at solid-liquid interfaces. This article introduces new applications of AFM based on measurement examples.

In order to examine the mechanism of friction property development, it is essential to understand the composition and shape of the surface, which is the front line of friction and wear. In this report, sliding tests were conducted using oil before and after degradation, and the correlation between friction characteristics and sliding film was investigated by various surface analyses and electron microscopy. As a result, it was found that there is a strong correlation between the friction coefficient μand the amount of MoS₂formed on the sliding surface.

The efficiency of engine oils is determined by additives in the lubricant. Molybdenum dithiocarbamate (MoDTC) is a friction modifier that has been used in automotive engines. However, decomposition mechanism of MoDTC in commercial engine lubricant is not fully understood.In this study, commercial engine oil (0W-20) was used as lubricant. The degraded oils were prepared by oxidation test and NOx gas bubbling assuming a gasoline engine. Liquid chromatography-mass spectrometry (LC-MS) was used to analyze additive reduction in engine oils. The SRV tribotester was used to evaluate the friction properties of the degraded oils. In addition, the chemical state of sliding surface after SRV tests was analyzed by Raman spectroscopy and Atomic Froce Microscope (AFM).

In a previous paper, the empirical extended Barus viscosity and theoretical van der Waals type viscosity equations were derived to estimate high-pressure viscosity. Furthermore, various analyses using statistical methods, such as machine learning, are becoming popular. Thus, predicting physical properties without it being based on an experiment or theory are becoming possible. In this study, the possibility of estimating the high-pressure viscosity of a lubricant via statistical analysis is investigated. Hence, a quadratic polynomial multiple regression viscosity equation with two variables is derived. Although this equation lacks physical meaning, it can provide viscosity via automatic calculation using the Excel regression analysis. Additionally, this equation is equivalent to the extended Barus viscosity and van der Waals type viscosity equations and has a small standard deviation of error% in the interpolation region. Hence, this equation is highly effective and exhibits estimation ability equivalent to those of the extended Barus viscosity and van der Waals type viscosity equations. Thus, the proposed equation provides a different perspective on the physical properties of viscosity. These results indicate that the prediction of high-pressure viscosity equivalent to the empirical and theoretical equations is possible via statistical analysis using a multiple regression equation.