Supramolecular self-assembly can not only lead to a better understanding of biological systems, but also can enable rational building of complex and functional materials. In this report, hierarchical one-dimensional (1D) architectures involving nanotubes, coiled-coil ropelike structures, nanohelices, and nanoribbons are created via lanthanum-cholate supramolecular self-assembly. These sophisticated self-assemblies are proven to be mediated by temperature. The entanglement of one-dimensional nanostructures is demonstrated to give rise to fascinating "super" hydrogel, which can realize water gelation at extremely low concentration. Unprecedented water gelation behaviors, that is, heating-enhanced stiffness and heating-promoted gelation, are found in lanthanum-cholate supramolecular hydrogel. The driving forces of self-assembled complex nanostructures and the unique role of temperature are also discussed.

We report a facile method to synthesize water-soluble gold nanoparticles (AuNPs) using a biosurfactant sodium cholate as reducing reagents and protective groups in aqueous solution at ambient temperature. The diameters (13-70 nm) of uniform AuNPs can be readily adjusted by changing the initial molar ratio of sodium cholate to chloroauric acid (HAuCl(4)). Also, the alkaline condition of preparative solution is found to affect the size of as-synthesized AuNPs. This synthetic approach is one-step and "green". The obtained AuNPs exhibit a good electrocatalytic activity toward methanol oxidation. Meanwhile, the AuNPs thin films can serve as an efficient substrate for surface-enhanced Raman scattering (SERS). Furthermore, platinum nanoparticles (PtNPs) are also prepared by reducing sodium tetrachloro platinate hydrate with sodium cholate.

Recently, the spatio-temporal residual network (ST-ResNet) has leveraged the power of deep learning (DL) to predict citywide spatio-temporal flow volume.However, this model, neglects the dynamic dependency of the input series in the temporal dimension, which affects the captured spatio-temporal features. The present study introduces the long short-term memory (LSTM) neural network into ST-ResNet, to form a hybrid integrated DL model for citywide spatio-temporal flow volume prediction (called HIDLST). The new model is capable of dynamically learning the temporal dependency via the feedback connection of the LSTM, improving the accuracy of the spatio-temporal features. We test the HIDLST model by predicting the citywide taxi flow volume in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. Comparative experiments indicate that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models with regard to prediction accuracy. Additionally, we also discuss the distribution of prediction errors and the contributions of different spatio-temporal patterns.