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Two opposing microtribometry approaches have been developed over the past decade to help connect the dots between fundamental and practical tribology measurements: spring-based (e.g., AFM) approaches use low speed, low stiffness, and long relative slip length to quantify friction, while quartz crystal microbalance (QCM)based approaches use high speed, high stiffness, and short relative slip length. Because the friction forces generated in these experiments are attributed to entirely different phenomena, it is unclear if or how the resulting friction forces are related. This study aims to resolve this uncertainty by integrating these distinct techniques into a single apparatus that allows two independent measurements of friction at a single interface. Alumina microspheres were tested against single-crystal MoS 2 , a model nominally wear-free solid lubricant, and gold, a model metal control, at loads between 0.01 and 1 mN. The combined results from both measurement approaches gave friction coefficients (mean ± standard error) of 0.087 ± 0.007 and 0.27 ± 0.02 for alumina-MoS 2 and alumina-gold, respectively. The observed agreement between these methods for two different material systems suggests that friction in microscale contacts can be far less sensitive to external effects from compliance and slip speed than currently thought. Perhaps more importantly, this Article describes and validates a novel approach to closing the "tribology gap" while demonstrating how integration creates new opportunities for fundamental studies of practical friction.

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis depends on the quality of PV timeseries data. We propose a novel Spatio-Temporal Denoising Graph Autoencoder STD-GAE framework to impute missing PV Power Data. STD-GAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module to create plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers and denoising autoencoder to improve the accuracy of imputation accuracy at PV fleet level. Experimental results on two PV datasets show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods.

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