Enhanced Photocatalytic Performance of Nanosized Mixed-Ligand Metal-Organic Frameworks through Sequential Energy and Electron Transfer Process.

Effective sequestration of harmful organic pollutants from wastewater has been a persistent concern in the interest of environmental and ecological protection from pollution and hazards. Currently, common water treatment technologies such as adsorption, coagulation, and membranes are expensive and not greatly effective. A new class of organic and inorganic composite metal-organic frameworks (MOFs) has emerged as an essential class of materials for numerous applications, including photocatalytic degradation of organic pollutants. Herein, we present a nanosize mixed-ligand MOF (nMLM) which was successfully synthesized by reacting a Zr metal source with a mixture of pyrene and porphyrin building units and further utilized as photocatalyst in the photodegradation of rhodamine B (RhB). The nMLM MOF showed excellent photocatalytic efficiency, which was due to the complementary absorption and sequential energy and electron transfer properties of its building blocks, pyrene and porphyrin. We also propose herein a possible mechanism of the photocatalytic function of the material.

A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing.

Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.