Yiren Lu

Hierarchical WO ₃ ·0.33H ₂ O/Cr ₂ O ₃ nanofibrous networks achieve a high surface area of 192.61 m ² g ⁻¹ and demonstrate ultrafast selective adsorption of cationic dyes from complex mixed solutions via electrostatic and hydrogen-bonding interactions.

A dissolution-recrystallization route enables facet-preferential growth in p-NiO/n-BiPO ₄ heterojunctions with atomic-scale intimate interfaces, achieving efficient charge separation for pollutant degradation.

We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL using a hierarchical model to enable generalization to arbitrary goal routes, and measure performance using a closed-loop evaluation framework with simulated interactive agents. We train policies from expert trajectories collected from real vehicles driving over 100,000 miles in San Francisco, and demonstrate a steerable policy that can navigate robustly even in a zero-shot setting, generalizing to synthetic scenarios with novel goals that never occurred in real-world driving. We also demonstrate the importance of mixing closed-loop MGAIL losses with open-loop behavior cloning losses, and show our best policy approaches the performance of the expert. We evaluate our imitative model in both average and challenging scenarios, and show how it can serve as a useful prior to plan successful trajectories.

Thermal catalytic oxidation technology is an effective way to eliminate refractory volatile organic pollutants, such as Benzene. Nevertheless, a high reaction temperature is usually an obstacle to practical application. Here, GdMn₂O₅ mullite (GMO-H) catalyst with disordered surface Gd-deficient and oxygen-vacancy-rich concentrations was synthesized via a controllable low-temperature acid-etching route. Results show that the preferentially broken Gd-O bond is conducive to exposing more Mn-Mn active sites, which Gd species covered. The affluent surface oxygen vacancies supply sufficient adsorption sites for oxygen molecules, facilitating the oxygen cycles during Benzene catalytic oxidation. Furthermore, surface exposed Mn³⁺ species were oxidized to Mn⁴⁺, which is beneficial to increase catalytic activity at a lower temperature. Compared with the conventional GdMn₂O₅, the reaction temperature for removing 90% Benzene over GMO-H was dropped from 405 to 310 °C with WHSV of 30,000 mL g⁻¹ h⁻¹. Significantly, during a 72 h catalytic test, the catalytic activity remains constant at 90% of the Benzene removal at 300 °C, indicating excellent activity stability. This work reported an efficient approach to preparing manganese-base mullite thermal catalyst, providing insight into the catalytic oxidation of Benzene.