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.     

一类分数阶微积分的中值定理及应用

Riemann-Liouville分数阶微积分算子是一类带有一个函数的分数阶微积分算子的特殊情形，以Riemann-Liouville分数阶微积分算子的积分中值定理和微分中值定理为基础，我们得到了一类带有一个函数的分数阶微积分算子的积分中值定理和微分中值定理，并给出其在计算方面的一些应用。     

Prelabeling Expansion Single-Molecule Localization Microscopy with Minimal Linkage Error

Expansion microscopy combined with single-molecule localization microscopy (ExSMLM) has a potential for approaching molecular resolution. However, ExSMLM faces multiple challenges such as loss of fluorophores and proteins during polymerization, digestion or denaturation, and an increase in linkage error arising from the distance between the fluorophore and the target molecule. Here, we introduce a trifunctional streptavidin to link the target, fluorophore and gel matrix via a biotinylizable peptide tag. The resultant ExSMLM images of vimentin filaments demonstrated high labeling efficiency and a minimal linkage error of ∼5 nm. Our ExSMLM provides a simple and practical means for fluorescence imaging with molecular resolution.     

磨矿动力学研究现状及应用

磨矿动力学是描述被磨物料的磨碎速率与磨矿时间之间关系规律的一种数学模型，对分析物料在磨矿过程中的粒级及能量变化具有重要作用。为充分发挥磨矿动力学在磨矿过程中的作用，论文在分析国内外研究现状的基础上，系统介绍了两种典型的磨矿动力学模型：m阶磨矿动力学模型和磨矿总体平衡动力学模型，分析了模型中各参数的含义；以磨矿总体平衡动力学模型为重点，分析了破碎速率函数和破碎分布函数的求解方式，包括零阶产出率法、奥-勒理论简算法、卡普尔G-H算法以及经验公式法等；从物料性质、磨矿介质及配比、磨矿方式及参数、化学添加剂等几个方面分析了影响磨矿动力学模型的因素；指出了磨矿动力学模型在矿物加工工程领域的应用现状并对其未来的研究方向提出展望。研究表明磨矿动力学在矿物加工领域具有广泛而重要的应用，为进一步改善磨矿工艺提供了理论依据。     

Anisotropic thermal expansion and ionic conductivity of a crystal-oriented,Mg2+-conducting NASICON-type solid electrolyte

Multivalent ion-conducting ceramics are required for the manufacture of high-safety, high-capacity rechargeable batteries. However, the low ionic conductivity of solid electrolytes and discrepancies in the thermal expansion between the battery components limit their widespread application. Furthermore, anisotropic thermal expansion in crystals during battery manufacturing and the charge-discharge cycles causes the formation of microcracks, which degrade the battery performance. The physical properties of ceramic materials with anisotropic crystal structures can be modified by varying the crystallographic orientation of their grains. In this study, a co-precipitation approach was used to synthesize an Mg²⁺-conducting (Mg_0.1Hf_0.9)_4/3.8Nb(PO₄)₃ solid electrolyte, and the grain orientation in the bulk sample was controlled using strong magnetic fields during the slip casting process. The results showed that inducing an orientation along the c-axis enhanced the apparent ionic conductivity of the bulk sample. It was also observed that (Mg_0.1Hf_0.9)_4/3.8Nb(PO₄)₃ crystal has a negative volumetric thermal expansion despite a positive linear thermal expansion along its c-axis. By adjusting the c-axis orientation of the grains, (Mg_0.1Hf_0.9)_4/3.8Nb(PO₄)₃ electrolytes with negative or positive linear thermal expansion coefficient have been produced. The findings of this study suggest that solid-electrolytes with negative, positive, or zero linear thermal expansion can be produced to create more compatible and higher-performance solid-state devices.     

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.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

煤峪口矿81010工作面煤层底板裂隙发育特征及防治

针对煤炭开采过程中出现的突水事故,采用RFPA数值模拟软件建立采动模型,对底板裂隙破断过程和声发射进行模拟,研究煤层底板采动裂隙扩展突水通道,结果表明:离断层越近,断层内水压导升高度越高,断层出现活化,裂隙扩展发育,最终贯通形成导水通道,在进行注浆改造后,单个钻孔的最大涌水量为8 m³/h,说明注浆加固防治水效果较好,能确保工作面的安全回采。