Improving the performance of natural rubber using graphene and its derivatives

In this research project, modified graphene was employed as filler to enhance the electrical conductivity and to reinforce mechanical properties of natural rubber (NR). The defect sites in the graphene sheets were investigated for further modification. The latex mixing and mechanical mixing methods to load functional graphene sheets into the NR matrix, improved the mechanical and electrical properties of the composite material. Graphene was prepared by a chemical oxidation-reduction approach to fill the NR matrix. The oxidation approaches were employed in progress, which will induce various defects in the final product. It is known that these defects decrease the properties of the graphene and graphene/natural rubber composites, which are prepared by traditional method as well. However, these defects could cause improvements in performance of the graphene composites with re-designed methods, the main focus of this thesis. Before loading into NR matrix, the defect information of graphene oxide (GO) prepared using Hummers method was examined through positron testing, which is known to be highly effective in the study of the defects in graphite and its derivatives. The different types of defects were detectable, which revealed that the vacancy clusters and vacancy-oxygen group complexes were present on the GO sheets. No large open-volume hole was detected in GO. The reduction of GO by potassium carbonate (K2CO3) as a green noble preparation approach was developed, and the oxygen groups dispersion status in the GO sheet was further investigated. K2CO3 was used as a reusable reduction agent to convert GO to reduced graphene oxide (RGO) in two steps, based on the conversion of the different types of oxygen groups detected. Carbon dioxide was the only by-product of this process, which was absorbed by K2CO3. In addition, the study further elucidates the structure of GO sheets. The oxygen groups on the GO sheets not only aligned but also randomly dispersed in different areas. Antistatic NR nanocomposites with partly interconnected graphene architectures offer significant enhancement in various properties. RGO/NR composites were prepared using latex mixing and in-situ reduction process. The oxygen groups on the GO played a key role in attaching GO sheets to the surface of NR particles. Segregated current transfer routes were partly constructed in an NR matrix with an electrical conductivity of 0.1 S/m and reinforcing the tensile strength and elongation-at-break as well. Silver nanoparticles (AgNPs) were used to decorate GO, which further increased the electrical conductivity of NR nanocomposites. Electrically conductive AgNPs/RGO filled NR with well-organized three-dimensional (3D) microstructures were prepared through electrostatic self-assembly integrated latex mixing. The oxygen groups in GO acted as an anchor for AgNPs growth, resulting in the electrical conductivity of 31000 S/m for the AgNPs/RGO. A honeycomb-like AgNPs/RGO 3D network was constructed in the NR matrix after freeze-drying and hot compression moulding. The AgNPs/RGO/NR nanocomposites show a percolation threshold of 0.63 vol.% and electrical conductivity of 196 S/m at AgNPs/RGO content of 4.03 vol.%. The oxygen groups can not only be used to improve the electrical conductivity of NR but also used to reinforce mechanical properties. The effect of functionalized GO on the mechanical properties of NR was investigated through two strategies. In the first strategy, one layer of silica particles were attached to the GO surface through hydrogen bonds. The strength were reinforced because of well-dispersed SiO2/GO in the NR matrix. GO acted as a surfactant dispersed by silica into the NR matrix to reinforce the mechanical properties using latex mixing. Oxygen groups on the graphene sheets banded with silica to achieve the target. In the second strategy, the strength reinforcement of NR nanocomposites was achieved by construction of an interpenetrating network between the NR molecules and porous graphene. In this project, porous graphene loaded NR nanocomposites were prepared through an ultrasonically assisted latex mixing and in-situ reduction process. The oxygen groups showed chemo-selectivity etched by potassium permanganate (KMnO4), forming pores possessing suitable dimensions in graphene sheets. Porous graphene/NR nanocomposites show strong interactions between the NR molecules and porous graphene than RGO/NR, which contributed to an increase in tensile strength compared to the RGO/NR nanocomposites. Furthermore, the scorch time compared to RGO/NR was decreased, and density of cross-linking was increased, which demonstrate the pores on the graphene sheets formed a mass transfer route, indicating an interpenetrating network was constructed

An amperometric glucose biosensor based on a MnO2/graphene composite modified electrode

In this paper, a novel composite of graphene/MnO2 (GR/MnO2) was successfully synthesized by a simple one-step hydrothermal method. The as-synthesized MnO2 and the composite were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The results showed that MnO2 was nanorods and the two materials were perfectly composited. The composite was decorated on a glassy carbon electrode (GCE) and used for the entrapment of glucose oxidase (GOD). Electrochemical results showed that the composite modified electrode showed a pair of well-defined redox peaks, and the direct electron transfer between GOD and the electrode surface was accelerated. The sensor fabricated by the composite modified electrode showed an excellent response to the oxidation of glucose with a wide linear range (0.04 to 2 mM), low detection limit (10 mM), and high sensitivity (3.3 mA mM-1 cm-2). The sensor also exhibited excellent reproducibility, stability and selectivity, and it can be used in the determination of glucose in real samples

In situ synthesis of natural rubber latex-supported gold nanoparticles for flexible SERS substrates

Natural rubber latex (NRL) from Hevea brasiliensis was used as a matrix to synthesize gold nanoparticles (AuNPs), leading to an organic-inorganic hybrid latex of NRL-supported AuNPs (AuNPs@NRL). The in situ and environmentally friendly preparation of AuNPs in an NRL matrix was developed by thermal treatment without using any other reducing agents or stabilizers because natural rubber particles and non-rubber components present in serum can serve as supporters for the synthesized AuNPs. As a result, the nanosized and well-dispersed AuNPs not only are decorated on the surface of natural rubber particles, but also can be found in the serum of NRL. The size of the AuNPs presented in NRL matrix can be controlled by adjusting the concentration of NRL. Furthermore, the flexible surface-enhanced Raman scattering (SERS) substrates made from the AuNPs@NRL through vacuum filtration presented good enhancement of the Raman probe molecule of 4-mercaptopyridine and outstanding SERS reproducibility. The capability of synthesizing the bio-supported nanohybrid latex provides a novel green and simple approach for the fabrication of flexible and effective SERS substrates

Super-compact universal quantum logic gates with inversedesigned elements

Integrated quantum photonic circuit is a promising platform for the realization of quantum information processing in the future. To achieve the largescale quantum photonic circuits, the applied quantum logic gates should be as small as possible for the high-density integration on chips. Here, we report the implementation of super-compact universal quantum logic gates on silicon chips by the method of inverse design. In particular, the fabricated controlled-NOT gate and Hadamard gate are both nearly a vacuum wavelength, being the smallest optical quantum gates reported up to now. We further design the quantum circuit by cascading these fundamental gates to perform arbitrary quantum processing, where the corresponding size is about several orders smaller than that of previous quantum photonic circuits. Our study paves the way for the realization of largescale quantum photonic chips with integrated sources, and can possess important applications in the field of quantum information processes

The relationship between labial soft tissue changes and jumping spaces after immediate implant placement and restoration in the anterior maxilla: A prospective study

Oral implants have been increasingly used in the treatment of edentulous patients or those with dentition defects due to reliable treatment procedure and favorable long-term prognosis. We investigated the changes of labial soft tissue contours with different jumping spaces after immediate implant placement and restoration (IIPR) in the maxillary esthetic area and also provided a long-term stability measurement for the changing trend of soft tissue contour. All patients had been separated into three groups based on the jumping space: group A (horizontal defect dimension [HDD] 2 mm), group B (2 mm 3 mm) and the digital impressions were obtained in the first, third, and sixth month after the operation. The changes of gingival mucosa levels, the average thickness of soft tissue contour volume, and the linear change of submarginal level decreased gradually across the three groups, with the largest change of submarginal level being at 5mm. The size of the jumping space was moderately negatively correlated with the level and average thickness of gingival mucosa and the linear changes of 3 mm and 5 mm under gingival margin, while there was no significant correlation with pink esthetic score (PES) and the linear change of the 1 mm under the gingival margin. Generally, IIPR of upper anterior teeth can achieve esthetic satisfaction, and the level of soft tissue around the implant can be well preserved

212962^{1296} Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method

For decades, people are developing efficient numerical methods for solving the challenging quantum many-body problem, whose Hilbert space grows exponentially with the size of the problem. However, this journey is far from over, as previous methods all have serious limitations. The recently developed deep learning methods provide a very promising new route to solve the long-standing quantum many-body problems. We report that a deep learning based simulation protocol can achieve the solution with state-of-the-art precision in the Hilbert space as large as $2^{1296}$ for spin system and $3^{144}$ for fermion system , using a HPC-AI hybrid framework on the new Sunway supercomputer. With highly scalability up to 40 million heterogeneous cores, our applications have measured 94% weak scaling efficiency and 72% strong scaling efficiency. The accomplishment of this work opens the door to simulate spin models and Fermion models on unprecedented lattice size with extreme high precision.Comment: Massive ground state optimizations of CNN-based wave-functions for $J1$-$J2$ model and $t$-$J$ model carried out on a heterogeneous cores supercompute

ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits

We introduce an online for public quantum computation platform, named as ScQ, based on a 1D array of 10-qubit superconducting processor. Single qubit rotation gates can be performed on each qubit. Controlled-NOT (CNOT) gates between nearest-neighbor sites on the 1D array of 10 qubits are available. We show online preparation and verification of Greenberger-Horne-Zeilinger (GHZ) states of up to 10 qubits by this platform, for all possible blocks of qubits in the chain. Both graphic interface and the quantum assembly language methods are presented to achieve the above tasks, which rely on a parameter scanning feature implemented on ScQ. Performance of this quantum computation platform, such as fidelities of the logic gates and details of the superconducting device, are presented

Compatibility-tuned distribution of nanoparticles in co-continuous rubber structures toward microwave absorption enhancement

Development of novel and versatile approaches to engineer composites with light density, broad effective bandwidth and high microwave absorption (MA) capacity is of great importance. Here, co-continuous natural rubber/epoxidized natural rubber (NR/ENR) blends with a selective distribution of conductive carbon black nanoparticles (CCBs), have been fabricated by tow-roll mixing. ENR with abundant epoxide groups shows inferior wettability to CCB than NR, which is responsible for the preferential location of CCB in the NR/ENR blend. Increasing the epoxidation level of ENR promotes the preferential location of CCB and creates stronger dielectric loss, thus enhancing the MA properties of CCB/NR/ENR composites. When the epoxidation level increases to 40 mol%, the MA capacity of the composite has been significantly enhanced by 40%. Meanwhile, the qualified frequency bandwidth (RL < −10 dB) of composites with ENR is 85% broader than that of CCB/NR composites. Such a novel approach of compatibility-tuned nanoparticles distribution in co-continuous rubber blends will significantly promote the multi-functional use of rubber and carbonaceous resources