Although capsule-like materials as host carriers for enzyme encapsulation have been a hot topic in recent years, creating an ideal microenvironment for enhanced enzymatic performance is still a formidable challenge. Herein, we created a template-free method to in situ encapsulate natural enzymes in hollow covalent organic framework (COF) capsules at room temperature. The COF crystallites migrated from the inner core and self-assembled at the outside walls during the inside-out Ostwald ripening process, retaining the enzymes in the cavity. The adjustable hollow structure of the enzyme@COF capsule allowed the basic vibration of the enzyme to maintain a certain degree of freedom, thus significantly enhancing the enzymatic bioactivity. The hollow enzyme@COF capsule has large mesoporous tunnels allowing the efficient transport. In addition, the enzyme encapsulated in the capsule showed superior activity and ultrahigh stability under various extreme conditions that may lead to enzyme inactivation, such as high temperature, organic solvents, chelates, and the denaturing agent. Finally, the prepared hollow GOx@COF capsule was used for electrochemical sensing of glucose in human serum, and the electrochemical sensor exhibited high selectivity and satisfactory test results. This research not only provides a new way for COFs to encapsulate enzymes but also has potential applications in biocatalysis and biosensing, making artificial organelles possible.
Porous structures and heterogeneous compositions are highly desirable to achieve improved sensing performance. Herein, a SnO2/Co3O4 composite with catalytic site and abundant oxygen vacancy was successfully synthesized by pyrolyzation of...
Wood adhesive was prepared using Broussonetia papyrifera waste leaf protein as the raw material. The performance of the B. papyrifera leaf protein adhesive compared to soy protein was investigated using X-ray diffraction, Fourier transform infrared spectroscopy, and differential scanning calorimetry. The results indicated that both B. papyrifera leaf protein and soy protein were spherical proteins that could easily form three-dimensional crosslinked network structures and were of potential for protein adhesive preparation. The B. papyrifera leaf and soy protein-based adhesives had similar curing behaviors, but the crosslinking reaction of B. papyrifera leaf protein-based adhesive seems to be more complex than that of the soy protein-based adhesive. The B. papyrifera leaf protein-based adhesive had a lower increasing trend of particle size and crystallinity than the soy-based protein adhesive, and its water resistance and bonding strength were also weaker. The plywood with BP leaf protein adhesive had dry and wet shear strengths of 0.93 MPa and 0.59 MPa, respectively. These results are promising for future industrial production using Broussonetia papyrifera waste leaf protein as a new protein wood adhesive in the wood industry.
The automatic identification system (AIS) provides a massive database for ocean science. The original AIS data are redundant. Direct use will cause a waste of data storage space and computation costs; hence, data compression must be performed. The Douglas-Peucker algorithm (DP) is an effective trajectory compression algorithm that can well preserve the spatial characteristics of a trajectory but has the following shortcomings: first, it has poor track recovery when compressing multi-turn routes; second, it does not consider the ship speed and heading; and third, it may have the wrong result of the compressed trajectory crossing the obstacle. To address these situations, this study proposes a multi-objective peak DP algorithm (MPDP) that adopts a peak sampling strategy, considers three optimization objectives (spatial characteristics, heading and speed) of trajectory and adds an obstacle detection mechanism to realize a compression algorithm more suitable for curved trajectories. The classical DP algorithm is compared with the MPDP algorithm by simulating trajectory and real trajectory experiments. The results show that the MPDP algorithm optimizes the length loss rate, simultaneous Euclidean distance, and average deviations of the speed and the heading while maintaining a high compression rate similar to that of the DP algorithm. Moreover, it can also successfully avoid obstacles. The optimization effect is most obvious for the multi-turn or hovering trajectory. The optimization rate of length loss, synchronous Euclidean distance, and average deviation of the heading can reach 40%.INDEX TERMS AIS data, Douglas-Peucker algorithm (DP), multi-objective peak Douglas-Peucker algorithm (MPDP), trajectory compression.
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