Microstructure and Mechanical Properties of Magnetron Sputtering TiN-Ni Nanocrystalline Composite Films

In this paper, TiN-Ni nanostructured composite films with different Ni contents are prepared using the magnetron sputtering method. The composition, microstructure, and mechanical properties of composite films are analyzed using an X-ray energy spectrometer (EDS), a scanning electron microscope (SEM), X-ray diffraction technology (XRD), a transmission electron microscope (TEM), and nanoindentation. All the films grow in a columnar crystal structure. There are only TiN diffraction peaks in the XRD spectrum, and no diffraction peaks of Ni and its compounds are observed. The addition of the Ni element disrupts the integrity of TiN lattice growth, resulting in a decrease in the grain size from 60 nm in TiN to 25 nm at 20.6% Ni. The film with a Ni content of 12.4 at.% forms a nanocomposite structure in which the nanocrystalline TiN phase (nc-TiN) is surrounded by the amorphous Ni (a-Ni) phase. The formation of nc-TiN/a-Ni nanocomposite structures relies on the good wettability of Ni on TiN ceramics. The hardness and elastic modulus of the film gradually decrease with the increase in Ni content, but the toughness is improved. The hardness and elastic modulus decrease from 19.9 GPa and 239.5 GPa for TiN film to 15.4 GPa and 223 GPa at 20.6 at.% Ni film, respectively, while the fracture toughness increases from 1.5 MPa m1/2 to 2.0 MPa m1/2. The soft and ductile Ni phase enriched at the TiN grain boundaries hinders the propagation of cracks in the TiN phase, resulting in a significant increase in the film’s toughness. The research results of this paper provide support for the design of TiN-Ni films with high strength and toughness and the understanding of the formation mechanism of nanocomposite structures.info:eu-repo/semantics/publishedVersio

Designing forest biodiversity experiments : general considerations illustrated by a new large experiment in subtropical China

Funded by German Research Foundation. Grant Number: DFG FOR 891/1 and 2 National Natural Science Foundation of China. Grant Numbers: NSFC 30710103907, 30930005, 31170457 , 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in BeijingPeer reviewedPublisher PD

Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China


 Biodiversity-ecosystem functioning (BEF) experiments address ecosystem-level consequences of species loss by comparing communities of high species richness with communities from which species have been gradually eliminated. BEF experiments originally started with microcosms in the laboratory and with grassland ecosystems. A new frontier in experimental BEF research is manipulating tree diversity in forest ecosystems, compelling researchers to think big and comprehensively.
 We present and discuss some of the major issues to be considered in the design of BEF experiments with trees and illustrate these with a new forest biodiversity experiment established in subtropical China (Xingangshan, Jiangxi Province) in 2009/2010. Using a pool of 40 tree species, extinction scenarios were simulated with tree richness levels of 1, 2, 4, 8 and 16 species on a total of 566 plots of 25.8 × 25.8 m each.
 The goal of this experiment is to estimate effects of tree and shrub species richness on carbon storage and soil erosion; therefore, the experiment was established on sloped terrain. The following important design choices were made: (i) establishing many small rather than fewer larger plots, (ii) using high planting density and random mixing of species rather than lower planting density and patchwise mixing of species, (iii) establishing a map of the initial 'ecoscape' to characterize site heterogeneity before the onset of biodiversity effects and (iv) manipulating tree species richness not only in random but also in trait-oriented extinction scenarios.
 Data management and analysis are particularly challenging in BEF experiments with their hierarchical designs nesting individuals within-species populations within plots within-species compositions. Statistical analysis best proceeds by partitioning these random terms into fixed-term contrasts, for example, species composition into contrasts for species richness and the presence of particular functional groups, which can then be tested against the remaining random variation among compositions.
 We conclude that forest BEF experiments provide exciting and timely research options. They especially require careful thinking to allow multiple disciplines to measure and analyse data jointly and effectively. Achieving specific research goals and synergy with previous experiments involves trade-offs between different designs and requires manifold design decisions.&#13

Constitutive Models for the Strain Strengthening of Austenitic Stainless Steels at Cryogenic Temperatures with a Literature Review

Austenitic stainless steels are widely used in cryogenic pressure vessels, liquefied natural gas pipelines, and offshore transportation liquefied petroleum gas storage tanks due to their excellent mechanical properties at cryogenic temperatures. To meet the lightweight and economical requirements, pre-strain of austenitic stainless steels was conducted to improve the strength at cryogenic temperatures. The essence of being strengthened by strain (strain strengthening) and the phase-transformation mechanism of austenitic stainless steels at cryogenic temperatures are reviewed in this work. The mechanical properties and microstructure evolution of austenitic stainless steels under different temperatures, types, and strain rates are compared. The phase-transformation mechanism of austenitic stainless steels during strain at cryogenic temperatures and its influence on strength and microstructure evolution are summarized. The constitutive models of strain strengthening at cryogenic temperatures were set to calculate the volume fraction of strain-induced martensite and to predict the mechanical properties of austenitic stainless steels

Green technology advancement, energy input share and carbon emission trend studies

Abstract In order to study the theoretical mechanism of the impact of green technology progress on carbon emissions, this article constructs a theoretical mechanism of the impact of green technology progress on carbon emission growth. Explore the conditions for achieving carbon peak and carbon reduction. Based on the Cobb Douglas production function, construct a three sector model that includes capital, labor, and energy. Empirical methods were used to analyze the quantitative impact of green technology progress on carbon emission growth and the moderating effect of energy input share. This study mainly used provincial panel data from 1995 to 2020. Calculate carbon dioxide emissions based on energy consumption and carbon dioxide emission coefficients of various energy sources in different regions. Using the perpetual inventory method to calculate capital growth rate, green computing progress rate, etc., to provide data support for the green technology carbon reduction model. Empirical analysis of the impact of green technology progress on carbon emissions using the FGLS panel model. Theoretical and empirical analyses show that green technological progress promotes an increase in the carbon emission growth rate through the scale effect, with an impact coefficient of 0.607; it promotes a decrease in the carbon emission growth rate through the technological effect, with an impact coefficient of − 0.667; the combined effect promotes a decrease in growth rate of carbon emissions, with an impact coefficient of − 0.06. The share of energy inputs has a positive regulating effect on the scale effect

Prediction model for the water jet falling point in fire extinguishing based on a GA-BP neural network.

Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters

Effects of Residue Returning on Soil Organic Carbon Storage and Sequestration Rate in China’s Croplands: A Meta-Analysis

Crop residue returning (RR) is a promising option to increase soil organic carbon (SOC) storage, which is linked to crop yield promotion, ecologically sustainable agriculture, and climate change mitigation. Thus, the objectives of this study were to identify the responses of SOC storage and sequestration rates to RR in China’s croplands. Based on a national meta-analysis of 365 comparisons from 99 publications, the results indicated that RR increased SOC storage by 11.3% compared to residue removal (p < 0.05). Theoretically, when combined with low nitrogen fertilizer input rates (0–120 kg N ha−1), single cropping system, paddy-upland rotation, lower mean annual precipitation (0–500 mm), alkaline soils (pH 7.5–8.5), other methods of RR (including residue chopping, evenly incorporating, and burying) or long-term use (>10 yrs), an increase in SOC storage under RR by 11.6–15.5% could be obtained. The SOC sequestration rate of RR varied from 0.48 (Central China) to 1.61 (Southwest China) Mg C ha−1 yr−1, with a national average value of 0.93 Mg C ha−1 yr−1. Higher SOC sequestration rates enhanced crop production. However, decreases in SOC sequestration rate were observed with increases in experimental durations. The phenomenon of “C saturation” occurred after 23 yrs of RR. Overall, RR can be used as an efficient and environmentally friendly and climate-smart management practice for long-term use

Multi-objective optimization design of gas turbine exhaust ejection device based on NSGA-II algorithm

ObjectiveThe exhaust ejection device is an important part of a ship's gas turbine intake and exhaust system. It is very important to ensure the exhaust and ejection performance of the exhaust ejection device for the normal operation of the ship. MethodsAccording to the characteristics of this type of gas turbine exhaust ejection device, this paper selects eight relevant structural parameters and two objective functions in order to analyze the parameter correlation, and determines the two design variables that have the greatest correlation with the objective function. On this basis, the design parameters are optimized based on the multi-objective optimization genetic algorithm NSGA-II, and the selected optimization scheme is applied to a gas turbine intake and exhaust system assembly model for verification. Results The results show that, compared with the original scheme, the optimized exhaust ejection device has a significantly improved ejection flow rate of the gas turbine casing, meeting the design requirements of the exhaust ejection amount, but this also leads to an increase in exhaust back pressure. ConclusionThe research approaches and results obtained in this paper have certain reference value for the design and optimization of gas turbine exhaust ejection devices

Numerical modeling and simulation of the electric breakdown of rocks immersed in water using high voltage pulses

Selective breakdown of mineralized particles by using high-voltage pulses (HVP) has been reported, yet its mechanisms are not fully understood, and the HVP setting factors affecting its efficacy in ore pre-concentration for the mining industry are not established. This study investigates the electro-dynamic mechanisms of electric breakdown by using the time-transient dielectric breakdown model and the finite-difference numerical method. Monte-Carlo method with random sampling is applied to calculate breakdown probabilities. The model and the selected parameters have been validated by the published experimental data of the electric breakdown of mineralized synthetic particles. The simulations of pulse rising time from 150\ua0ns to 1\ua0μs showed that the HVP breakdown threshold of rock particles gradually increased as the pulse rising time decreased. This suggests that to minimize the mis-breakdown of barren rocks in the HVP-enabled ore pre-concentration application, it is important to use a generator with a short pulse rising time. Shorter pulses also led to a higher probability of the internal breakdown of the mineralized particles. The simulations indicate that inhomogeneity of conductivity in an ore particle caused the streamers to bend toward the area of inclusion with high conductivity in a host rock matrix, which increased the probabilities of the breakdown of this mineralized particle. This phenomenon was more pronounced as conductivity rose. High-conductivity inclusions can reduce the minimum voltages required for the breakdown of the mineralized particles

Air-Flow Impacting for Continuous, Highly Efficient, Large-Scale Mechanochemical Synthesis: A Proof-of-Concept Study

We report a novel air-flow impacting method for mechanochemical synthesis. It is an alternative approach for conventional mechanochemical synthesis. The feasibility of this method was demonstrated via the preparation of three Schiff bases containing N,N′-bis­(m-nitrobenzylidene)-p-phenylenediamine (compound 1), N,N′-bis­(2-hydroxy-1-naphthylmethylene)-p-phenylenediamine (compound 2), and polymeric Schiff base as the model compounds. The as-prepared Schiff bases were characterized by Fourier transform infrared spectroscopy, powder X-ray diffraction, differential thermal analysis, nuclear magnetic resonance, ultraviolet–visible (UV–vis) spectroscopy, and single crystal X-ray diffraction. All the results indicated that two bis-schiff bases were successfully synthesized after 3 min at the rate of 1.5 kg min^–1. In addition, kinetic analysis was carried out to study the reaction mechanisms by detecting the UV–vis spectra of the products at different reaction times. It was found that the preparation of compound 1 belonged to the two-dimensional diffusion-controlled model, while the synthesis of compound 2 is the two-dimensional diffusion-controlled product growth following deceleratory nucleation