Four kinds of green deep eutectic solvent (DES) were synthesized, including choline chloride (ChCl)-urea, tetramethylammonium chloride (TMACl)-urea, tetrapropylammonium bromide (TPMBr)-urea and ChCl-methylurea. An aqueous two-phase system (ATPS) based ChCl-urea DES was studied for the first time for the extraction of bovine serum albumin (BSA). Single factor experiments proved that the extraction efficiency of BSA was influenced by the mass of the DES, concentration of K2HPO4 solution, separation time and extraction temperature. The optimum conditions were determined through an orthogonal experiment with the four factors described above. The results showed that under the optimum conditions, the average extraction efficiency could reach up to 99.94%, 99.72%, 100.05% and 100.05% (each measured three times). The relative standard deviations (RSD) of extraction efficiencies in precision, repeatability and stability experiments were 0.5533% (n = 5), 0.8306% (n = 5) and 0.9829% (n = 5), respectively. UV-vis and FT-IR spectra confirmed that there were no chemical interactions between BSA and the DES in the extraction process, and the CD spectra proved that the conformation of BSA did not change after extraction. The conductivity, DLS and TEM were combined to investigate the microstructure of the top phase and the possible mechanism for the extraction. The results showed that hydrophobic interactions, hydrogen bonding interactions and the salting-out effect played important roles in the transfer process, and the aggregation and surrounding phenomenon were the main driving forces for the separation. All of these results proved that ionic liquid (IL)-based ATPSs could potentially be substituted with DES-based ATPSs to offer new possibilities in the extraction of proteins.

Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles. Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. To avoid unsatisfactory reactive decisions, it is essential to count long-term future rewards in planning, which requires extending the prediction horizon. In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern. We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden states and connection feature of each interaction pair. The output of our method is a probabilistic likelihood of multiple behavior classes, which matches the multimodal and uncertain nature of the distant future. A systematic comparison of our method against two state-of-the-art methods and another two baseline methods on a publicly available real highway dataset is provided, showing that our method has superior accuracy and advanced capability for interaction modeling.

The relationship between thermodynamic dissolution parameters (enthalpy and entropy) and gelation ability was examined for two different classes of compounds in three different solvent systems. In total, 11 dipeptides and 19 pyridines were synthesized and screened for gelation in aqueous and organic solvents, respectively. The dissolution parameters were determined from the variable-temperature solubilities using the van't Hoff equation. These studies revealed that the majority of gelators had higher dissolution enthalpies and entropies compared to nongelators, consistent with the notion that gelators have stronger intermolecular interactions and more order in the solid state. The dissolution parameters were also found to be solvent-dependent, suggesting that solvent-solute interactions are also important in gelation. Overall, these results indicate that converting nongelators into gelators is attainable when structural modifications or a change in solvent lead to increases in the dissolution parameters.

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