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Abstract Conventional lubricants face significant challenges in electric vehicle (EV) systems due to their low electrical conductivity and inability to mitigate tribo-electrification effects which can result in increased friction, wear, and electrical discharge damage under external electrification. Consequently, conductive lubricants like ionic liquids (ILs) have emerged as promising alternatives, offering enhanced compatibility with EV applications. This study investigated the tribological behavior of four phosphonium-based room temperature ionic liquids (PRTILs) with trihexyltetradecyl phosphonium [P6,6,6,14] or tributyltetradecyl phosphonium [P4,4,4,14] cations and saccharinate [Sacc] or benzoate [Benz] anions under electrified conditions, targeting potential EV applications. Physicochemical properties, including viscosity and ionic conductivity, were measured using a viscometer and a conductivity meter, while tribological properties were evaluated using an electrified mini-traction machine and an electrified rotary ball-on-disk setup. The results revealed that all the PRTILs exhibited superior tribological (friction and wear) performance than mineral oil with or without electrification. PRTILs with the [Sacc] anion feature a double aromatic ring structure, while those with the [Benz] anion feature a single aromatic ring structure. Under low electrification (10 mA), [P6,6,6,14][Sacc] outperformed [Benz]-based PRTILs, showing a lower coefficient of friction and wear due to their higher viscosity and lower ionic conductivity. Additionally, [P6,6,6,14][Sacc] showed a power loss lower than [P4,4,4,14][Sacc] but higher than [Benz]-based PRTILs under tribo-electrification. The addition of graphene nanoplatelets (GNPs) reduced the power loss of [P6,6,6,14][Sacc] by 24% by reducing the electric contact resistance. Overall, double-ring aromatic [P6,6,6,14][Sacc] demonstrated superior tribological performance, and GNP additives enhanced their power efficiency, offering a promising pathway for IL-based lubricant development for electrified conditions. 

his study examined six phosphonium-based room-temperature ionic liquids (PRTILs) having trihexyltetradecyl- or tributyltetradecyl-phosphonium cations with saccharinate, salicylate, or benzoate anions, and obtained a feature parameter to correlate their cationic chain length, anionic ring size, and contact angle with tribological properties. PRTILs with trihexyltetradecyl-phosphonium cations had lower coefficient of friction (COF) and wear than PRTILs with tributyltetradecyl- phosphonium cations, a trend attributed to the additional methylene groups providing lower contact angle. For either cation, PRTILs with the saccharinate anion exhibited much lower COF and wear than single-ring anions, due to the formation of a low-shear-strength-tribofilm facilitated by the double-ring structure and sulfur of saccharinate. Overall, this study revealed PRTIL interfacial mechanisms that can be used to identify anion-cation combinations with optimal tribological performance. 

Abstract We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.