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Micro- or nano-diodes suspended in water can propel controllably under alternating-current fields or light without the need for fuels such as hydrogen peroxide or urea. This makes them promising candidates for miniature motors in biomedical and other applications. However, the mechanisms underlying their propulsion remain unclear. This study investigates the propulsion of diodes floating in an aqueous solution at the millimeter scale, which facilitates observation of motion, allowing direct correlation with electrical measurements of device properties. We find that the diode’s propulsion is driven by forward current under an alternating-current field and by photocurrent under illumination. The velocity of propulsion scales linearly with the net current, with the rectified or photogenerated current creating an imbalance of ions at the ends of the diodes. This, in turn, generates an electric field that induces electrophoretic flow around the diode and propels the diode. Additionally, we assess the velocity of diodes intentionally damaged by high reverse bias and find that it decreases significantly because of the reduced difference between forward and reverse currents. These results suggest potential uses of diode propulsion for characterizing and separating bottom-up-grown nano-/micro- diodes based on their reverse-saturation current, as well as nanomotors formed from multiple-junction nanowire diodes that can self-propel in water under light. 

Doped semiconductor nanowires are emerging as next-generation electronic colloidal materials, and the efficient manipulation of such nanostructures is crucial for technological applications. In fluid suspension, pn nanowires (pn NWs), unlike homogeneous nanowires, have a permanent dipole, and thus, experience a torque under an external DC field that orients the nanowire with its n-type end in the direction of the field. Here, we quantitatively measure the permanent dipoles of various Si nanowire pn diodes and investigate their origin. By comparing the dipoles of pn NWs of different lengths and radii, we show that the permanent dipole originates from non-uniform surface-charge distributions, rather than the internal charges at the p–n junction as was previously proposed. This understanding of the mechanism for pn NWs orientation has relevance to the manipulation, assembly, characterization, and separation of nanowire electronics by electric fields. 

Yttrium-doped barium zirconate is one of the fastest solid-state proton conductors. While previous studies suggest that proton–tuples move as pairs in yttrium-doped barium zirconate, a systematic catalog of possible close proton–tuple moves is missing. Such a catalog is essential to simulating dual proton conduction effects. Density functional theory with the Perdew–Burke–Ernzerhof functional is utilized to obtain the total electronic energy for each proton–tuple. The conjugate gradient and nudged elastic band methods are used to find the minima and transition states for proton–tuple motion. In the lowest-energy configuration, protons are in close proximity to each other and the dopant, significantly affecting the backbone structure. The map of moves away from the global minimum proton–tuple shows that the most critical move for long-range proton conduction is a rotation with a barrier range of 0.31–0.41 eV when the two protons are in close proximity. 

In developing countries, high schoolers rarely have opportunities to conduct chemical experiments due to the lack of facilities. There-fore, chemistry experiment simulation is an alternative environment for students to do the chemistry lab assignments. Despite the need of creating virtual simulations to expand the application usability, it is challenging to synthesize a realistic environment given the limited computing resources. In this paper, we propose Chemisim, a highly realistic web-based VR laboratory simulation for students with high quality and usability. In particular, we make use of the fluid simulation system to mimic real chemical reactions. The imple-mented simulation was based on the chemistry assignments in the national education system, consulted by chemical teachers. Then we deployed the simulator on the web to promote a wide range of students usage. The system was evaluated by collecting and analyzing feedback from chemical teachers based on four criteria, namely, convenience, realism, functionality, and preferences. Our experimental findings address educational challenges and produce innovative technical solutions to solve them in developing countries. 

Abstract Seed-mediated synthesis strategies, in which small gold nanoparticle precursors are added to a growth solution to initiate heterogeneous nucleation, are among the most prevalent, simple, and productive methodologies for generating well-defined colloidal anisotropic nanostructures. However, the size, structure, and chemical properties of the seeds remain poorly understood, which partially explains the lack of mechanistic understanding of many particle growth reactions. Here, we identify the majority component in the seed solution as an atomically precise gold nanocluster, consisting of a 32-atom Au core with 8 halide ligands and 12 neutral ligands constituting a bound ion pair between a halide and the cationic surfactant: Au₃₂X₈[AQA⁺•X^-]₁₂(X = Cl, Br; AQA = alkyl quaternary ammonium). Ligand exchange is dynamic and versatile, occurring on the order of minutes and allowing for the formation of 48 distinct Au₃₂clusters with AQAX (alkyl quaternary ammonium halide) ligands. Anisotropic nanoparticle syntheses seeded with solutions enriched in Au₃₂X₈[AQA⁺•X^-]₁₂show narrower size distributions and fewer impurity particle shapes, indicating the importance of this cluster as a precursor to the growth of well-defined nanostructures. 

Federated Learning (FL) is a recently proposed learning paradigm for decentralized devices to collaboratively train a predictive model without exchanging private data. Existing FL frameworks, however, assume a one-size-fit-all model architecture to be collectively trained by local devices, which is determined prior to observing their data. Even with good engineering acumen, this often falls apart when local tasks are different and require diverging choices of architecture modelling to learn effectively. This motivates us to develop a novel personalized neural architecture search (NAS) algorithm for FL, which learns a base architecture that can be structurally personalized for quick adaptation to each local task. On several real- world datasets, our algorithm, FEDPNAS is able to achieve superior performance compared to other benchmarks on heterogeneous multitask scenarios.