Tin(II)-Based Metal–Organic Frameworks Enabling Efficient,Selective Reduction of CO2 to Formate under Visible Light

Certain metal complexes are known as high-performance CO₂ reduction photocatalysts driven by visible light. However, most of them rely on rare, precious metals as principal components, and integrating the functions of light absorption and catalysis into a single molecular unit based on abundant metals remains a challenge. Metal-organic frameworks (MOFs), which can be regarded as intermediate compounds between molecules and inorganic solids, are potential platforms for the construction of a simple photocatalytic system composed only of Earth-abundant nontoxic elements. In this work, we report that a tin-based MOF enables the conversion of CO₂ into formic acid with a record high apparent quantum yield (9.8 % at 400 nm) and >99 % selectivity without the need for any additional photosensitizer or catalyst. This work highlights a new MOF with strong potential for photocatalytic CO₂ reduction driven by solar energy.     

In-Situ Constructing A Heterogeneous Layer on Lithium Metal Anodes for Dendrite-Free Lithium Deposition and High Li-ion Flux

Constructing efficient artificial solid electrolyte interface (SEI) film is extremely vital for the practical application of lithium metal batteries. Herein, a dense artificial SEI film, in which lithiophilic Zn/Li_xZn_y are uniformly but nonconsecutively dispersed in the consecutive Li⁺-conductors of Li_xSiO_y, Li₂O and LiOH, is constructed via the in situ reaction of layered zinc silicate nanosheets and Li. The consecutive Li⁺-conductors can promote the desolvation process of solvated-Li⁺ and regulate the transfer of lithium ions. The nonconsecutive lithiophilic metals are polarized by the internal electric field to boost the transfer of lithium ions, and lower the nucleation barrier. Therefore, a low polarization of ≈50 mV for 750 h at 2.0 mA cm⁻² in symmetric cells, and a high capacity retention of 99.2 % in full cells with a high lithium iron phosphate areal loading of ≈13 mg cm⁻² are achieved. This work offers new sights to develop advanced alkali metal anodes for efficient energy storage.     

Highly Reproducible Flexible Ion-selective Electrodes for the Detection of Sodium and Potassium in Artificial Sweat

It is well known that potentiometric sensors provide a versatile, cost-effective, and efficient platform for wearable applications. Unfortunately, mass production and commercialization of such devices is often constrained by the requirement of a calibration step, which is due to the poor sensor-to-sensor reproducibility and the need of conditioning the electrodes in the analyte before use. Herein, we fabricated calibration-free flexible sensors including ion-selective electrode and reference electrode by integrating single-walled carbon nanotubes (SWCNTs) with poly(3-octylthiophene) (POT) and applying on polyethylene terephthalate (PET) substrate. The developed sodium and potassium ion-selective electrodes (ISEs) display excellent repeatability, selectivity, stability as well as high sensor-to-sensor reproducibility, with a standard deviation of as low as 1.0 mV in artificial sweat microliter samples volume.     

Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

Development of a chemical vapor sensor using piezoelectric quartz crystals with coated unusual lipids

Piezoelectric quartz crystal sensors were developed using lipids with various properties for highly sensitive detection of chemical vapors. Lipids with varying lengths of alkyl chains were coated onto 10 MHz AT-cut quartz crystal resonators and the response of these modified crystals to chemical vapors were measured. It was shown that hydrophilic compounds, such as ethanol and methanol, could be recognized efficiently by lipids having shorter alkyl chains, whereas lipids with longer alkyl chains showed affinity to more hydrophobic vapors, such as toluene, hexane and cyclohexane. Frequency changes caused by adsorption of alcohols could be enhanced when cholesterol was co-immobilized in the lipid layer. To confirm the assumption that the sensor-response might be affected by the properties of lipids derived from acyl chains, we have examined the effects of two types of newly synthesized unusual lipids on sensor response. When lipids having one triple bond each at different positions on their alkyl chains were coated onto quartz crystals separately, lower responses were observed compared to responses obtained for a sensor with immobilized, saturated phosphatidylcholine. Lipids containing -branched acyl chains, however, showed good affinity for organic vapors, and sensor responses improved 4–5-fold. Moreover, these sensors were shown to have sensitivity of the same order as the humans' sense of smell (10⁻⁵–10⁻⁶ w/w in liquid paraffin) when measured using standard odorants (isovaleric acid, skatole, etc.) for an olfactometry established in Japan.     

神经网络法计算镉（Ⅱ）,羟基及碳酸根三元体系的形态分布

采用前馈线笥网络ＢＰ算法，计算了Ｃｄ６２＋－ＯＨ＾－ＣＯ＾２－３三元体系的累积稳定常数。用Ｈｏｐｆｉｅｌｄ反馈网络研究了体系中络合物的形态分布。溶液中溶解的ＣＯ２对ｌｇβ１的计算结果有重要影响，对ｌｇβ２，ｌｇβ３，ｌｇβ４的结果影响不大。     

基于模糊传感器的机器人动态障碍环境中的运动控制

对自主式机械手在动态和部分已知且存在运动障碍环境中的运动规划和控制进行了研究,解决了自由碰撞运动控制中具有普遍意义的问题。利用人工势能场的机器人导航控制技术由模糊控制实现,系统的稳定性由李雅普诺夫原理保证。模糊控制器为机器人伺服提供控制指令,使机器人在不可预知的环境中能实时地、自主地选择到达目标的路径和方向。在动态环境的实时控制中,基于传感器的运动控制是处理未知模型和障碍物的重要控制方式。     

Non-Parametric Regression Methods

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).     

Laminar flamelet modeling of a turbulent CH4/H2/N2 jet diffusion flame using artificial neural networks

The laminar flamelet concept is used in the prediction of mean reactive scalars in a non-premixed turbulent CH₄/H₂/N₂ flame. First, a databank for temperature and species concentrations is developed from the solutions of counter-flow diffusion flames. The effects of flow field on flamelets are considered by using mixture fraction and scalar dissipation rate. Turbulence-chemistry interactions are taken into account by integrating different quantities based on a presumed probability density function (PDF), to calculate the Favre-averaged values of scalars. Flamelet library is then generated. To interpolate in the generated library, one artificial neural network (ANN) is trained where the mean and variance of mixture fraction and the scalar dissipation rate are used as inputs, and species mean mass fractions and temperature are selected as outputs. The weights and biases of this ANN are implemented in a CFD flow solver code, to estimate mean values of the scalars. Results reveal that ANN yields good predictions and the computational time has decreased as compared to numerical integration for the estimation of mean thermo-chemical variables in the CFD code. Predicted thermo-chemical quantities are close to those from experimental measurements but some discrepancies exist, which are mainly due to the assumption of non-unity Lewis number in the calculations.