Spacer arm of ionic liquids facilitated laccase immobilization on magnetic graphene enhancing its stability and catalytic performance.

To fulfill the requirements of environmental protection, a magnetically recoverable immobilized laccase has been developed for water pollutant treatment. In order to accomplish this objective, we propose a polydopamine-coated magnetic graphene material that addresses the challenges associated with accumulation caused by electrostatic interactions between graphene and enzyme molecules, which can lead to protein denaturation and inactivation. To achieve this, we present a polydopamine-coated magnetic graphene material that binds to the enzyme molecule through flexible spacer arms formed by ionic liquids. The immobilized laccase exhibited a good protective effect on laccase and showed a high stability and recycling ability. Laccase-ILs-PDA-MGO has a wider pH and temperature range and retains about 80% of its initial activity even after incubation at 50 °C for 2 h, which is 2.2 times more active than free laccase. Furthermore, the laccase-ILs-PDA-MGO exhibited a remarkable removal efficiency of 97.0% and 83.9% toward 2,4-DCP and BPA within 12 h at room temperature. More importantly, laccase-ILs-PDA-MGO can be recovered from the effluent and used multiple times for organic pollutant removal, while maintaining a relative removal efficiency of 80.6% for 2,4-DCP and 81.4% for BPA after undergoing seven cycles. In this study, a strategy for laccase immobilization by utilizing ILs spacer arms to modify GO aims to provide valuable insights into the advancement of efficient enzyme immobilization techniques and the practical application of immobilized enzymes in wastewater treatment.

Toward Fine-Grained Sketch-Based 3D Shape Retrieval.

In this paper we study, for the first time, the problem of fine-grained sketch-based 3D shape retrieval. We advocate the use of sketches as a fine-grained input modality to retrieve 3D shapes at instance-level - e.g., given a sketch of a chair, we set out to retrieve a specific chair from a gallery of all chairs. Fine-grained sketch-based 3D shape retrieval (FG-SBSR) has not been possible till now due to a lack of datasets that exhibit one-to-one sketch-3D correspondences. The first key contribution of this paper is two new datasets, consisting a total of 4,680 sketch-3D pairings from two object categories. Even with the datasets, FG-SBSR is still highly challenging because (i) the inherent domain gap between 2D sketch and 3D shape is large, and (ii) retrieval needs to be conducted at the instance level instead of the coarse category level matching as in traditional SBSR. Thus, the second contribution of the paper is the first cross-modal deep embedding model for FG-SBSR, which specifically tackles the unique challenges presented by this new problem. Core to the deep embedding model is a novel cross-modal view attention module which automatically computes the optimal combination of 2D projections of a 3D shape given a query sketch.