Mechanically robust, flame-retardant poly(lactic acid) biocomposites via combining cellulose nanofibers and ammonium polyphosphate

Expanding the application range of flame-retardant polymer biocomposites remains a huge challenge for a sustainable society. Despite largely enhanced flame retardancy, until now the resultant poly(lactic acid) (PLA) composites still suffer reduced tensile strength and impact toughness due to improper material design strategies. We, herein, demonstrate the design of a green flame retardant additive (ammonium polyphosphate (APP)@cellulose nanofiber (CNF)) via using the cellulose nanofibers (CNFs) as the green multifunctional additives hybridized with ammonium polyphosphate (APP). The results show that PLA composite with 5 wt % loading of APP@CNF can pass the UL-94 V-0 rating, besides a high limited oxygen index of 27.5%, indicative of a significantly enhanced flame retardancy. Moreover, the 5 wt % of APP@CNF enables the impact strength (σi) of the PVA matrix to significantly improve from 7.63 to 11.8 kJ/m2 (increase by 54%), in addition to a high tensile strength of 50.3 MPa for the resultant flame-retardant PLA composite. The enhanced flame retardancy and mechanical strength performances are attributed to the improved dispersion of APP@CNF and its smaller phase size within the PLA matrix along with their synergistic effect between APP and CNF. This work opens up a facile innovative methodology for the design of high-performance ecofriendly flame retardants and their advanced polymeric composites

Monolayer hydrophilic MoS2 with strong charge trapping for atomically thin neuromorphic vision systems

Effective control of electrical and optoelectronic properties of two-dimensional layered materials, one of the key requirements for applications in advanced optoelectronics with multiple functions, has been hindered by the difficulty of elemental doping, which is commonly utilized in Si technology. In this study, we proposed a new method to synthesize hydrophilic MoS2 monolayers through covalently introducing hydroxyl groups during their growth process. These hydroxyl groups exhibit a strong capability of charge trapping, and thus the hydrophilic MoS2 monolayers achieve excellent electrical, optical, and memory properties. Optical memory transistors, made from a single component of monolayer hydrophilic MoS2, exhibit not only excellent light-dependent and time-dependent photoelectric performance, but also good photo-responsive memory characteristics with over multi-bit storage and more than 104 switching ratios. Atomically thin neuromorphic vision systems (with a concept of proof of 10 × 10 neuromorphic visual image) are manufactured from arrays of hydrophilic MoS2 optical memory transistors, showing high quality image sensing and memory functions with a high color resolution. These results proved our new concepts to realize image memorization and simplify the pixel matrix preparation process, which is a significant step toward the development of future artificial visual systems

Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning accuracy, and nonmatching and mismatching are prone to occur, especially when the initial position error is large. To solve this problem, a new algorithm combining the artificial bee colony (ABC) and particle swarm optimization (PSO) was proposed according to the principle of terrain matching, to improve the matching effect. Considering that PSO easily falls into a local optimum, the acceleration factor and inertia weight of PSO were improved. The improved PSO was called WAPSO. ABC was introduced based on WAPSO and could help WAPSO escape local optimum. The final algorithm was termed ABC search-based WAPSO (F-WAPSO). During the continuous iteration of particles, F-WAPSO seeks the optimal position for the particles. Simulation tests show that F-WAPSO can effectively improve the matching accuracy. When the initial position error is 1000 m, the matching error can be reduced to 93.5 m, with a matching time of only 13.7 s

Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes

A human papillomavirus type plays an important role in the early diagnosis of cervical cancer. Most of the prediction methods use protein sequence and structure information, but the reduced amino acid modes have not been used until now. In this paper, we introduced the modes of reduced amino acids to predict high-risk HPV. We first reduced 20 amino acids into several nonoverlapping groups and calculated their structure and physicochemical modes for high-risk HPV prediction, which was tested and compared with the existing methods on 68 samples of known HPV types. The experiment result indicates that the proposed method achieved better performance with an accuracy of 96.49%, indicating that the reduced amino acid modes might be used to improve the prediction of high-risk HPV types

High-performance and flexible photodetectors based on chemical vapor deposition grown two-dimensional In2Se3 nanosheets

Two-dimensional (2D) In2Se3 with unique optical and electrical properties has great potential in next generation optoelectronics and multilevel phase-change memories. Here, for the first time, we report high-performance rigid and flexible photodetectors based on chemical vapor deposition (CVD) grown 2D In2Se3. Both rigid and flexible 2D In2Se3 photodetectors show a broadband response range from ultraviolet (254 nm) to visible light (700 nm). High photoresponsivities of 578 and 363 A · W-1 are achieved using rigid and flexible 2D In2Se3 photodetectors, respectively, under 700 nm light illumination, which are higher than those of photodetectors based on mechanically exfoliated 2D In2Se3 and physical vapor deposition grown 2D In2Se3. Furthermore, flexible 2D In2Se3 photodetectors show good mechanical durability and photoresponse stability under repeated bending tests. A high and stable photoresponse provides an opportunity for CVD-grown 2D In2Se3 applications in flexible optoelectronic and photovoltaic devices

Prediction of High-Risk Types of Human Papillomaviruses Using Statistical Model of Protein “Sequence Space”

Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein “sequence space” to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% and F1-score 90.91%, which indicates that protein “sequence space” could potentially be used to improve prediction of high-risk types of HPVs