Synthesis of nanopowders with low agglomeration by elaborating Φ values for producing Gd2O3-MgO nanocomposites with extremely fine grain sizes and high mid-infrared transparency

Refining ceramic microstructures to the nanometric range to minimize light scattering provides an interesting methodology for developing novel optical ceramic materials. In this work, we reported the fabrication and properties of a new nanocomposite optical ceramic of Gd₂O₃-MgO. The citric acid sol-gel combustion method was adopted to fabricate Gd₂O₃-MgO nanocomposites with fine-grain sizes, dense microstructures and homogeneous phase domains. Nanopowders with low agglomeration and improved sinterability can be obtained by elaborating Φ values. Further refining of the microstructure of the nanocomposites was achieved by elaborating the hot-pressing conditions. The sample sintered at 65 MPa and 1300 °C showed a quite high hardness value of 14.3 ± 0.2 GPa, a high transmittance of 80.3 %–84.7 % over the 3?6 μm wavelength range, due mainly to its extremely fine-grain size of Gd₂O₃ and MgO (93 and 78 nm, respectively) and high density.     

Reducing web latency with coding-based fast multi-path loss recovery

Wireless Networks - TCP latency is critical to the performance of Web services. However, packet loss greatly impairs the TCP performance due to its poor loss recovery mechanisms. Recent work FUSO...     

The origin of interfacial delamination in Au/ceramic cofired LTCC structure

The interfacial delamination of electrode/ceramic multilayer structure will seriously damage the reliability of low temperature co-fired ceramic (LTCC) module in practical applications. In this work, three kinds of glasses employed in Au electrode are designed and prepared to study the abnormal expansion and delamination process in the Au/ceramic LTCC multilayer structure. The interfacial delamination in the co-fired structure is found to be attributed to the abnormal expansion of glass in respect to Au electrode at high temperature, which is originated from the enlarged closed pores during the co-firing process. This conclusion is further confirmed by co-firing the sample in a low-pressure condition. The mechanism and elimination of interfacial delamination here provides a feasible solution for the design of novel glasses in Au electrode for LTCC applications.     

GaN/Surface-modified graphitic carbon nitride heterojunction: Promising photocatalytic hydrogen evolution materials

The coupling of two-dimensional (2D) layered materials is an effective way to realize photocatalytic hydrogen production. Herein, using first-principles calculations, the photocatalytic properties of GaN/CNs heterojunctions formed by two different graphite-like carbon nitride materials and GaN monolayer are discussed in detail. The results show that the GaN/C₂N heterojunction can promote the effective separation of photogenerated electron and hole pairs, which is attributed to its type-II band orientation and high carrier mobility. However, the low overpotential of GaN/C₂N for photocatalytic hydrogen production limits the photocatalytic performance. On this basis, we adjust the CBM position of the GaN/C₂N heterojunction by applying an electric field to enhance its hydrogen evolution capability. In addition, the GaN/g-C₃N₄ is a type-I heterojunction, which is suitable for the field of optoelectronic devices. This work broadens the field of vision for the preparation of highly efficient photocatalysts.     

Effects of rice bran fermented with Lactobacillus plantarum on palatability,volatile profiles,and antioxidant activity of brown rice noodles

Brown rice noodles (RN) are typical whole-grain products with health benefits. This study investigated the effects of rice bran fermented with Lactobacillus plantarum on palatability, volatile profiles, and antioxidant activity of brown RN. Three kinds of RN, including RN, brown RN (BRN), and bran fermented RN (BFRN), were involved in this study. The results indicated that bran fermentation reduced the cooking loss of BFRN and improved the elongation and the sensory attributes of brown RN. After fermentation, aldehydes were significantly decreased while alcohols increased, and more pleasant flavours were shown in BFRN. Fermentation increased the content of free phenolics and decreased the content of bound phenolics. Among the four major phenolics detected in this study, ferulic acid was the most abundant one in all three RN samples. The DPPH radical scavenging activity and the ferric reducing antioxidant power of free phenolics in BFRN increased significantly compared to BRN. This study proved that rice bran fermentation is an effective approach to improve palatability, enrich the flavour as well as enhance the antioxidant activity of brown RN, which may facilitate the development of processing technology for brown RN.     

Additive manufacturing,quasi-static and dynamic compressive behaviours of ceramic lattice structures

Ceramic lattice structures (CLSs) are used for construction in common and extreme environments because of the extraordinary properties of ceramics. In this study, we designed and additively manufactured CLSs with distinct structural parameters to explore their quasi-static and dynamic compressive behaviours in detail. It was demonstrated that both the relative density (?ρ) and inclination angle (ω) had a significant impact on the quasi-static and dynamic mechanical properties of the CLSs. Furthermore, the mathematical relationships between the quasi-static compressive properties, including quasi-static compressive strength (QS), quasi-static Young’s modulus (QY), and quasi-static energy absorption (QE), versus ?ρ and ω obeyed the Gibson–Ashby and Deshpande and Fleck models, respectively. It was revealed by experiment and simulation that as the stiffness increased, the quasi-static failure mode of the CLSs changed from a parallel-vertical-inclined mixed mode to a parallel-vertical mode. In addition, the relationship between the dynamic mechanical properties of the CLSs versus ?ρ and ω also followed the Gibson–Ashby and Deshpande and Fleck models. The exceptional dynamic increase factor indicated that CLSs are highly suitable for extreme environments. These findings will aid in the research and development of customised additively manufactured CLSs.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

How scientific research incorporates policy: an examination using the case of China’s science and technology evaluation system

Scientometrics - Governments typically formulate sets of policies to guide the direction of scientific research. And the possible effects of these policies on scientific research have been...     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Nanograin–glass dual-phasic,elasto-flexible,fatigue-tolerant,and heat-insulating ceramic sponges at large scales

Ceramics are considered intrinsically brittle at room temperature, which is mainly attributed to the limited availability of crystallographic slips and pre-existing geometrical flaws. Moreover, the lack of flexibility has severely hindered many high-end applications of ceramic materials. Here, we produce ceramic sponges that are simultaneously ultra-light, elasto-flexible, thermally insulating, and can fully recover from large deformation with a near-zero Poisson's ratio. These spongy materials also possess superb fatigue resistance without the accumulation of damage or structural collapse for 10,000 large-scale compressive or buckling cycles. We demonstrate the exceptional flexibility is enabled by the elastic distortion of nanograin–glassy dual phase and the fiber bulking in open-cell three-dimensional structure. Moreover, these spongy materials possess superior temperature-invariant superelasticity from deep cryogenic temperatures (−196 °C) to high temperature (1500 °C). Our study not only developed mechanically reliable lightweight ceramics for numerous extreme applications, but also provided new theoretical insights into the origin of flexibility in polycrystalline ceramics.