Biohybrid microtube swimmers driven by single captured bacteria

Bacteria biohybrids employ the motility and power of swimming bacteria to carry and maneuver microscale particles. They have the potential to perform microdrug and cargo delivery in vivo, but have been limited by poor design, reduced swimming capabilities, and impeded functionality. To address these challenge, motile Escherichia coli are captured inside electropolymerized microtubes, exhibiting the first report of a bacteria microswimmer that does not utilize a spherical particle chassis. Single bacterium becomes partially trapped within the tube and becomes a bioengine to push the microtube though biological media. Microtubes are modified with "smart" material properties for motion control, including a bacteria-attractant polydopamine inner layer, addition of magnetic components for external guidance, and a biochemical kill trigger to cease bacterium swimming on demand. Swimming dynamics of the bacteria biohybrid are quantified by comparing "length of protrusion" of bacteria from the microtubes with respect to changes in angular autocorrelation and swimmer mean squared displacement. The multifunctional microtubular swimmers present a new generation of biocompatible micromotors toward future microbiorobots and minimally invasive medical applications

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell

Effect of various plasticizers and concentration on the physical, thermal, mechanical, and structural properties of cassava-starch-based films

The present study investigated the effects of plasticizers (fructose, urea, tri-ethylene glycol, and triethanolamine) with different concentrations on the physical, thermal, and mechanical properties of cassava-starch-based films. The film samples were prepared using casting methods. The moisture content, water solubility, and water absorption of the films increased with increasing plasticizer content. Fructose-plasticized films show excellent water resistance compared to other plasticizers. Film plasticized with 30% fructose showed the highest density (1.74 g/cm3), but the lowest water content (10.96%) and water absorption (110%). Films containing fructose presented smooth surfaces without pores. The glass transition temperatures of the plasticized film also decreased with increased plasticizer content, irrespective of the plasticizer type. The relative crystallinity decreased with increasing plasticizer content. The film plasticized by 30% fructose presented higher relative crystallinity (0.31). The increase of plasticizer concentration resulted in a decrease of tensile strength, but increased elongation at break of the film samples. Film plasticized with 30% fructose showed the highest tensile strength (4.7 MPa) and tensile modulus (69 MPa). Thus, fructose was the most efficient plasticizer agent among the various plasticizers used in this study. High contents of plasticizer resulted in changes in the properties of the films. Overall, it can be concluded that the plasticizer type and concentration significantly influence the properties of cassava-starch-based film