Acrylamide-sodium acrylate copolymers: A comparison between copolymerization and hydrolysis

Polyacrylamide-co-sodium acrylate has been prepared via copolymerization of acrylamide and acrylic acid, or the hydrolysis of a pre-formed nonionic polymer. Both macromolecules had a charge of 24 mol% with a molar mass of 25 million g/mol. While one might expect similar performance, the hydrolyzed materials were superior in rheology modification, while the copolymerized samples worked better as flocculants. This hydrolysis reaction is influenced by the dyad sequence and produced a relatively uniform charge distribution along the backbone. These partially hydrolyzed polyelectrolytes had approximately 40% higher solution viscosities compared to the copolymerized samples. The rheological properties were also more tolerant to temperature, electrolytes, and hardness. Such molecules would be expected to perform well as pushing fluids as used in enhanced oil recovery processes. In contrast, when tested on an inorganic sludge, with a high level of polar and soluble hydrophobic molecules, the copolymerized sample provided larger flocs, a clearer filtrate, and faster settling. The hydrolyzed macromolecules essentially functioned only as a coagulant generating pin flocs. Depending on the application, polymer structure can be tuned by the method of synthesis. To the authors' knowledge, this is the first report comparing the influence of synthesis method on polyacrylamide solution properties and application performance.     

AI-Enabled Metal-Polymer Plain Bearing Based on the Triboelectric Principle

With the rapid development of the Internet of Things and artificial intelligence (AI), the requirement for sensing technologies for smart bearings has increased dramatically. The general bearing sensors can only recognize the basic information from temperature or vibration, far from satisfying the self-diagnosis and self-maintenance. Recently, self-powered sensing technologies based on triboelectric nanogenerators have paved a new route for fabricating smart bearings. In this study, the triboelectric principle is applied to a commercial metal-polymer plain bearing (MPPB) bearing, which can achieve self-sensing, self-diagnosis, and self-maintenance. The geometrical structure of the triboelectric MPPB (T-MPPB) is designed to balance the output efficiency and external load, and the super durability and load capability are verified. Besides, the mechanism behind the output change trend under boundary and hydrostatic fluid lubrication is revealed for the first time. Furthermore, the deep learning algorithm can classify the lubrication states with highly accurate performance. The proposed T-MPPB has the potential to achieve self-maintenance with the lubricating pump according to the lubrication condition classified by the AI. This research not only establishes the feasibility of designing self-powered smart MPPB but also demonstrates a way for identifying lubrication states, thus achieving self-diagnosis and self-maintenance ability by self-powered sensors.