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.     

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.     

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.     

Determination of gold in water samples using electromembrane extraction

In this work, an electromembrane extraction (EME) technique was used for the extraction and determination of gold from water samples prior to UV-Vis spectrophotometry. An artificial neural network (ANN) combined with imperialist competitive algorithm (ICA) has been applied to optimize the EME. The effective parameters including pH of acceptor phase, extraction time (t), volume of sample solution (V), stirring rate (S), and voltage (E) were chosen as input variables and the extraction recovery of gold was considered as output variable. The mean of squared error (i.e., 0.0009) and determination coefficient (i.e., 0.9821) were applied to estimate the performance of the ANN model. The limit of detection was 4.5?µg L^?1 (S/N?=?3) on the optimized variables. The intra- and interday precisions (%) were found to be 6.7% and 2.6%, respectively. This technique was then applied for analysis of gold from environment water samples.     

Artificial Intelligence Computation to Establish Relationships Between APS Process Parameters and Alumina–Titania Coating Properties

Modeling the behavior of air plasma spray (APS) process, one of the challenges nowadays is to identify the parameter interdependencies, correlations and individual effects on coating properties, characteristics and influences on the in-service properties. APS modeling requires a global approach which considers the relationships between coating characteristics/ in-service properties and process parameters. Such an approach permits to reduce the development costs. This is why a robust methodology is needed to study these interrelated effects. Artificial intelligence based on fuzzy logic and artificial neural network concepts offers the possibility to develop a global approach to predict the coating characteristics so as to reach the required operating parameters. The model considered coating properties (porosity) and established the relationships with power process parameters (arc current intensity, total plasma gas flow rate, hydrogen content) on the basis of artificial intelligence rules. Consequently, the role and the effects of each power process parameter were discriminated. The specific case of the deposition of alumina–titania (Al₂O₃–TiO₂, 13% by weight) by APS was considered.     

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.     

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.     

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.     

The Story of the Little Blue Box: A Tribute to Siegfried Hünig

The tetracationic cyclophane, cyclobis(paraquat-p-phenylene), also known as the little blue box, constitutes a modular receptor that has facilitated the discovery of many host–guest complexes and mechanically interlocked molecules during the past 35 years. Its versatility in binding small π-donors in its tetracationic state, as well as forming trisradical tricationic complexes with viologen radical cations in its doubly reduced bisradical dicationic state, renders it valuable for the construction of various stimuli-responsive materials. Since the first reports in 1988, the little blue box has been featured in over 500 publications in the literature. All this research activity would not have been possible without the seminal contributions carried out by Siegfried Hünig, who not only pioneered the syntheses of viologen-containing cyclophanes, but also revealed their rich redox chemistry in addition to their ability to undergo intramolecular π-dimerization. This Review describes how his pioneering research led to the design and synthesis of the little blue box, and how this redox-active host evolved into the key component of molecular shuttles, switches, and machines.