Design consideration and modelling studies of ultrasound and ultraviolet combined approach for shelf-life enhancement of pine apple juice

Although both ultraviolet (UV) radiation and ultrasound (US) treatment have their capabilities in microbial inactivation, applying any one method alone may require a high dose for complete inactivation, which may affect the sensory and nutritional properties of pineapple juice. Hence, this study was intended to analyse and optimise the effect of combined US and UV treatments on microbial inactivation without affecting the selected quality parameters of pineapple juice. US treatment (33 kHz) was done at three different time intervals, viz. 10 min, 20 min and 30 min., after which, juice samples were subjected to UV treatment for 10 min at three UV dosage levels, viz. 1 J/cm², 1.3 J/cm², and 1.6 J/cm². The samples were evaluated for total colour difference, pH, total soluble solids (TSS), titrable acidity (TA), and ascorbic acid content; total bacterial count and total yeast count; and the standardization of process parameters was done using Response Surface Methodology and Artificial Neural Network. The results showed that the individual, as well as combined treatments, did not significantly impact the physicochemical properties while retaining the quality characteristics. It was observed that combined treatment resulted in 5 log cycle reduction in bacterial and yeast populations while the individual treatment failed. From the optimization studies, it was found that combined US and UV treatments with 22.95 min and1.577 J/cm² ensured a microbiologically safe product while retaining organoleptic quality close to that of fresh juice.     

Visible-Light Augmented Lithium Storage Capacity in a Ruthenium(II) Photosensitizer Conjugated with a Dione-Catechol Redox Couple

Controlling redox activity of judiciously appended redox units on a photo-sensitive molecular core is an effective strategy for visible light energy harvesting and storage. The first example of a photosensitizer - electron donor coordination compound in which the photoinduced electron transfer step is used for light to electrical energy conversion and storage is reported. A photo-responsive Ru-diimine module conjugated with redox-active catechol groups in [Ru(II)(phenanthroline-5,6-diolate)₃]⁴⁻ photosensitizer can mediate photoinduced catechol to dione oxidation in the presence of a sacrificial electron acceptor or at the surface of an electrode. Under potentiostatic condition, visible light triggered current density enhancement confirmed the light harvesting ability of this photosensitizer. Upon implementation in galvanostatic charge-discharge of a Li battery configuration, the storage capacity was found to be increased by 100 %, under 470 nm illumination with output power of 4.0 mW/cm⁻². This proof-of-concept molecular system marks an important milestone towards a new generation of molecular photo-rechargeable materials.     

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.     

半导体纳米复合材料的制备及其光电催化CO2还原

本实验通过模拟植物光合作用,设计制备了新颖的光电联合催化池3D-ZnO/Ni BiVO4/FTO,用电化学沉积法制备了泡沫镍负载的ZnO纳米棒光电阴极和BiVO4光电阳极,以0.1 mol·L^−1 KHCO3水溶液作为电解质,1 mmol·L^−1曙红Y为光敏剂,在−0.6 V硅太阳电池的电压下光电催化还原CO2得到了乙醇、乙酸和甲醇,总产率22.5μmol·L^−1·h^−1·cm^−2。实现了将太阳能贮存为化学能并减少了空气中的CO2,加深了学生对绿色化学和植物Calvin循环机理的理解。     

基于ABC-SV R算法的拉曼光谱检测混合油脂肪酸含量

提出了一种基于激光拉曼光谱和人工蜂群智能优化支持向量回归机(ABC-SVR)算法的快速定量检测三组分混和油中3种脂肪酸含量的方法。该方法针对光谱数据信息与样本之间非线性、高维度的关系，建立了预测精度及建模效率均高于同类对比算法的数学模型，同时避免了气相色谱法、液相色谱法等对混合油脂肪酸含量的检测方式，根据纯种油中3种脂肪酸含量的国际标准，由油品配置体积得到脂肪酸质量，有效降低了检测成本与实验复杂程度，提高了检测工作的实用价值。首先根据一定梯度配置66组混合油检测样品，使用便携式拉曼光谱仪采集样本的拉曼光谱信息，扣除背景噪声；观察多组样本的拉曼光谱图可知，由于官能团浓度的差异，食用油的拉曼特征峰位移基本相同，特征峰的峰值明显不同，因此基于特征峰信息可以区分食用调和油的不同混合物；其次对拉曼光谱做背景扣除、光谱平滑、最大值谱线归一化三步预处理，以降低实验中不可控的外界因素及背景荧光的影响，准确提取光谱特征峰强度信息；然后根据纯种油中3种脂肪酸的国际标准含量，结合国家食品法典委员会标准CODEX STAN210-1999《指定的植物油法典标准》中规定的纯种油密度中值，由油品体积得到脂肪酸质量数；随机选取56组样本数据作为训练集，剩余10组样本数据作为预测集；以训练集光谱特征峰强度和脂肪酸质量分别作为回归模型的输入及输出值，建立SVR和PSO-SVR，ABC-SVR三种混合优化算法对比的定量分析模型，对测试集的3种脂肪酸含量分别进行预测；最后通过均方误差(MSE)、相关系数(r)及建模时间(Elapsed time)分别进行对比，建立数据表对模型精准度进行检验。实验结果表明，通过ABC-SVR定量分析模型效果最佳，3种脂肪酸含量预测值与真实值的均方差分别为0.88×10-4，16×10-4和8×10-4，均低于0.002；相关系数分别为93.43%，99.65%和99.43%，均高于93%；预测时间(Elapsed time)分别为1.26，2.42和2.14 s。因此，所提出的检测方法，具备较高的精确度、较快的建模时间，且在理论上的类似条件下可适用于其他样品检测工作，可为振动光谱学对食用油掺伪分析的进一步工作提供可行的理论依据。     

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

光催化还原二氧化碳全反应的研究进展

通过光催化将二氧化碳(CO₂)还原为可持续的绿色太阳能燃料是同时解决环境问题和能源危机的极具前景的方案.尽管迄今为止已经进行了广泛的研究,但实现高转化率、高选择性和高稳定性的光催化二氧化碳还原仍有许多障碍.如将水作为电子供体而非牺牲试剂,能够使反应的吉布斯自由能变ΔG>0,这对于真正实现理想化的人工光合作用至关重要,但同时也会为光催化还原CO₂体系带来更多的挑战.我们首先简要介绍了光催化还原CO₂的机理与挑战,而后根据目前光催化还原CO₂在无牺牲剂体系中出现的问题总结了对应的策略以及最新的研究进展,包括能带结构的调整、助催化剂的负载、异质结的构建、 MOFs与COFs材料的设计等方面,最后对目前仍未解决的问题以及未来实现工业化应用的阻碍进行了总结.     

Highly Selective Transmembrane Transport of Exogenous Lithium Ions through Rationally Designed Supramolecular Channels

Lithium ions have been applied in the clinic in the treatment of psychiatric disorders. In this work, we report artificial supramolecular lithium channels composed of pore-containing small aromatic molecules. By adjusting the lumen size and coordination numbers, we found that one of the supramolecular channels developed shows unprecedented transmembrane transport of exogenous lithium ions with a Li⁺/Na⁺ selectivity ratio of 23.0, which is in the same level of that of natural Na⁺ channels. Furthermore, four coordination sites inside channels are found to be the basic requirement for ion transport function. Importantly, this artificial lithium channel displays very low transport of physiological Na⁺, K⁺, Mg²⁺, and Ca²⁺ ions. This highly selective Li⁺ channel may become an important tool for studying the physiological role of intracellular lithium ions, especially in the treatment of psychiatric disorders.     

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.