Dynamics of laser-induced cavitation bubble during expansion over sharp-edge geometry submerged in liquid – an inside view by diffuse illumination

Laser ablation in liquids is growing in popularity for various applications including nanoparticle production, breakdown spectroscopy, and surface functionalization. When laser pulse ablates the solid target submerged in liquid, a cavitation bubble develops. In case of “finite” geometries of ablated solids, liquid dynamical phenomena can occur inside the bubble when the bubble overflows the surface edge. To observe this dynamics, we use diffuse illumination of a flashlamp in combination with a high-speed videography by exposure times down to 250 ns. The developed theoretical modelling and its comparison with the experimental observations clearly prove that this approach widens the observable area inside the bubble. We thereby use it to study the dynamics of laser-induced cavitation bubble during its expansion over a sharp-edge (“cliff-like” 90°) geometry submerged in water, ethanol, and polyethylene glycol 300. The samples are 17 mm wide stainless steel plates with thickness in the range of 0.025–2 mm. Bubbles are induced on the samples by 1064-nm laser pulses with pulse durations of 7–60 ns and pulse energies of 10–55 mJ. We observe formation of a fixed-type secondary cavity behind the edge where low-pressure area develops due to bubble-driven flow of the liquid. This occurs when the velocity of liquid overflow exceeds ~20 m s⁻¹. A re-entrant liquid injection with up to ~40 m s⁻¹ velocity may occur inside the bubble when the bubble overflows the edge of the sample. Formation and characteristics of the jet evidently depend on the relation between the breakdown-edge offset and the bubble energy, as well as the properties of the surrounding liquid. Higher viscosity of the liquid prevents the generation of the jet.     

Localization and quantification of radical production in cavitating flows with luminol chemiluminescent reactions

Hydrodynamic cavitation experiments in microfluidic systems have been performed with an aqueous solution of luminol as the working fluid. In order to identify where and how much reactive radical species are formed by the violent bubble collapse, the resulting chemiluminescent oxidation reaction of luminol was scrutinized downstream of a constriction in the microchannel. An original method was developed in order to map the intensity of chemiluminescence emitted from the micro-flow, allowing us to localize the region where radicals are produced. Time averaged void fraction measurements performed by laser induced fluorescence experiments were also used to determine the cavitation cloud position. The combination void fraction and chemiluminescence two-dimensional mapping demonstrated that the maximum chemiluminescent intensity area was found just downstream of the cavitation clouds. Furthermore, the radical yield can be obtained with our single photon counting technique. The maximum radical production rates of 1.2*10⁷ OH/s and radical production per processed liquid volume of 2.15*10¹⁰ HO/l were observed. The proposed technique allows for two-dimensional characterisation of radical production in the microfluidic flow and could be a quick, non-intrusive way to optimise hydrodynamic cavitation reactor design and operating parameters, leading to enhancements in wastewater treatments and other process intensifications.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Two-dimensional Covalent Organic Frameworks: Intrinsic Synergy Promoting Photocatalytic Hydrogen Evolution

Covalent organic frameworks(COFs) are emerging photocatalysts for hydrogen evolution in water splitting in recent years. They offer a pre-designable platform to design tailor-made structures and chemically adjustable functionality in terms of photocatalysis. In this review, we summarize the recent striking progress of COF-based photocatalysts in design and synthesis. Firstly, different approaches to functionalizing building blocks, diversifying linkages, extending π-conjugation and establishing D-A conjugation are illustrated for enhancing photocatalytic activity. Next, post-modification of backbones and pores is detailed for emphasizing the synergistic catalytic uniqueness of COFs. Besides, the strategy of preparing COF-related composites with various semiconductors is outlined for optimizing the electronic properties. Finally, we conclude with the current challenges and promising opportunities for the exploration of new COF-based photocatalysts.     

DFT Study of Methanol Adsorption on Defect-Free CeO2 Low-Index Surfaces

Methanol decomposition is a promising method for hydrogen production. However, the performance of current catalysts for this process is not sufficient for commercial applications. In this work, methanol adsorption on the CeO₂ low-index surfaces is studied by density functional theory (DFT). The results show that methanol always dissociates spontaneously on the (100) surface, whereas dissociation on the (110) surface is site-selective; dissociation does not occur at all on the (111) surface, where only weak physisorption is found. The results confirm that surfaces with higher energies are more catalytically active. Analysis of the surface geometries shows that the dominant factors for the dissociation of methanol are the degree of undercoordination and the charges of the surface ions. The adsorption energy of each methanol molecule decreases with increasing coverage and there is a transition threshold between dissociative and associative adsorption. The present work indicates that a strategy to design catalysts with high activity is to maximize exposure of surfaces on which the ions have a high degree of undercoordination and a strong tendency to donate/accept electrons. The results demonstrate the importance of appropriately selecting and controlling exposed facets and particle morphology for optimizing catalyst performance.     

水流损失和热补偿共同作用对增强型地热系统(EGS)产能影响的研究

基于青海共和盆地-3705m地热田实测数据,结合流固耦合传热理论并运用Comsol软件,建立了离散型裂隙岩体流体传热模型。考虑水流损失和热补偿共同作用,模拟得到了开采过程中上、下岩层(盖层和垫层)为绝热不渗透、传热不渗透、渗透传热时,储层(上、下岩层和压裂层)温度场的变化特征,分析了产出流量、水流损失、产出温度、产热速率的变化规律。研究结果表明:采热过程中产出流量始终小于注入流量;产出流量增幅速率先增大后减小,最后趋于稳定,前3a产出流量增幅超过总增幅量的3/4;忽略水流损失,将高估产热速率,采热初期甚至达到考虑水流损失时产热速率的3倍以上;考虑水流损失,产热速率呈先快速上升再趋于稳定后逐渐下降的趋势,最优开采时间为3a^11a;研究上、下岩层对产出温度的影响,仅考虑传热,采热寿命延长5.43%,同时考虑渗流传热时,采热寿命延长2.71%;采热前9a,水流损失占主导作用,即流入上、下岩层水流损失对产热速率的影响高于热补偿效应,开采10a后,热补偿效应占主导作用;同时考虑水流损失和热补偿效应得到的产热速率变化规律与实际工程更为符合,建议选择低渗透能力的上、下岩层延长增强型地热系统(EGS)运行时间。     

Relationship between the radial dynamics and the chemical production of a harmonically driven spherical bubble

The sonochemical activity and the radial dynamics of a harmonically excited spherical bubble are investigated numerically. A detailed model is employed capable to calculate the chemical production inside the bubble placed in water that is saturated with oxygen. Parameter studies are performed with the control parameters of the pressure amplitude, the forcing frequency and the bubble size. Three different definitions of collapse strengths (extracted from the radius vs. time curves) are examined and compared with the chemical output of various species. A mathematical formula is established to estimate the chemical output as a function of the collapse strength; thus, the chemical activity can be predicted without taking into account the chemical kinetics into the bubble model. The calculations are carried out by an in-house code exploiting the high processing power of professional graphics cards (GPUs).The results shown that chemical activity can be approximated qualitatively from the values of relative expansion. This could be helpful in order to optimise chemical output of sonochemical reactors either from measurement data or simulations as well.     

三缺位硅钨酸盐纳米材料的调控形貌的合成及功能化

以三缺位Keggin型硅钨酸盐为前驱体,采用化学沉淀法成功合成出2例新型纳米材料M_3SiW_9(M=Cu.,Co.)。其中,Cu_3SiW_9表现为新颖的纳米晶须形貌,Co_3SiW_9表现为尺寸均一的球形纳米颗粒。经过一系列控制实验证明,通过掺杂不同类型的金属离子可以调控M_3SiW_9的形貌。我们对产生该现象的机理进行了合理推测。此外,使用荧光素钠作为掺杂剂可使Cu_3SiW_9纳米晶须具有优良的光致发光性能,测试表明,多酸并未淬灭荧光素钠的发光,复合材料在510 nm具有明显的发射波长。最后,我们对Cu_3SiW_9纳米晶须负载CdS量子点后的光催化产氢性质进行了研究。结果表明,当Cu_3SiW_9与CdS的质量比为1∶1时,复合材料的产氢效率约为纯CdS量子点的10倍。这也证实多金属氧酸盐的存在可以有效抑制CdS量子点光生电子与空穴的复合,从而大幅提高其光解水产氢的效率。     

Influence of bidentate phosphine ligands on the chemistry of [FeFe]-hydrogenase model: insight into molecular structures and electrochemical characteristics

Substitution of carbonyl ligands of the hydrogenase model complex [Fe₂(μ-SeCH₂CH(Me)CH₂Se-μ)(CO)₆] ( A ), by 1,1′-bis (diphenylphosphino)ferrocene (dppf), 1,2-bis (diphenylphosphino)benzene (dppbz) or 1,2-bis (diphenylphosphino)acetylene (dppac) is investigated. It is found that the reaction product depends on the diphosphine used. In the case of dppf, the product is an intramolecular bridged disubstituted complex [Fe₂{μ-SeCH₂CH(Me)CH₂Se-μ}(CO)₄{μ,κ¹,κ¹(P,P)-dppf}] ( 1 ), while the dppac-reaction produces an intermolecular bridged tetra-iron model [Fe₂{μ-SeCH₂CH(Me)CH₂Se-μ}(CO)₅]₂{μ,κ¹,κ¹(P,P)-dppac} ( 2 ). However, the dppbz-reaction gives [Fe₂{μ-SeCH₂CH(Me)CH₂Se-μ}(CO)₄{κ²(P,P)-dppbz}] ( 3 ) in which the dppbz ligand is bonded to one Fe atom in a chelated manner. The newly prepared complexes ( 1 – 3 ) have been characterized by elemental analysis, IR, ¹H-, ¹³C{H}-, ³¹P{H}-, ⁷⁷Se{H}-NMR spectroscopy and X-ray structure determination. The electrochemical behavior of 2 and 3 , in absence and presence of acid, is described by cyclic voltammetric measurements in CH₂Cl₂.     

基于串联两阶段网络DEA模型的供应链规模收益分析

数据包络分析(DEA)是评价供应链系统(Supply chain system)间相对有效性的一种重要的工具,但是传统的DEA不考虑供应链的内部结构,对系统效率评价偏高;而本文所研究两阶段串联供应链系统,考虑把部分中间产品作为最终产品输出,增加额外中间投入的情形.基于所提出的供应链系统结构,本文建立相应的串联结构下的网络DEA模型,并针对所建立模型进行相关理论的研究,给出了串联结构下的生产可能集和规模收益情况判定方法.最后,进行数值实验,以验证我们提出的结论.