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The development of portable and efficient nanoprobes to realize the quantitative/qualitative onsite determination of food pollutants is of immense importance for safeguarding human health and food safety. With the advent of the smartphone, the digital imaging property causes it to be an ideal diagnostic substrate to point-of-care analysis probes. Besides, merging the versatility of carbon dots nanostructures and bioreceptor abilities has opened an innovative assortment of construction blocks to design advanced nanoprobes or improving those existing ones. On this ground, massive endeavors have been made to combine mobile phones with smart nanomaterials to produce portable (bio)sensors in a reliable, low cost, rapid, and even facile-to-implement area with inadequate resources. Herein, this work outlines the latest advancement of carbon dots nanostructures on smartphone for onsite detecting of agri-food pollutants. Particularly, we afford a summary of numerous approaches applied for target molecule diagnosis (pesticides, mycotoxins, pathogens, antibiotics, and metal ions), for instance microscopic imaging, fluorescence, colorimetric, and electrochemical techniques. Authors tried to list those scaffolds that are well-recognized in complex media or those using novel constructions/techniques. Lastly, we also point out some challenges and appealing prospects related to the enhancement of high-efficiency smartphone based carbon dots systems. [ABSTRACT FROM AUTHOR]

An asymmetric Mn/TEMPO-Salen complex was utilized as a reducing agent and template for the synthesis of Mn/TEMPO-doped Fe3O4 NPs in situ. The subsequent thermal decomposition of Mn/TEMPO-doped Fe3O4 NPs, as a precursor, provides the highly porous (641 m2 g–1) Mn2O3-doped Fe3O4 NPs. The highly porous NPs was characterized by FTIR, BET, TGA, VSM, ICP, TEM, XRD, UV–Vis, and XPS analyses. High stability and catalytic activity was found for the NPs in the synthesis of α-aminonitriles via the Strecker synthesis from primary and secondary alcohols under mild conditions. TAIm[CN] ionic liquid (IL) was used as an efficient solvent and nitrile-required reagent to synthesis of α-aminonitriles. TAIm[CN] IL could be recycled for several times by a simple recovery and subsequent treatment. Also, the catalytic activity of the NPs was evaluated toward the selective alcohol oxidation to carbonyl compounds and the direct transformation of imines from alcohols and aryl amines. Mn2O3-doped Fe3O4 NPs could be recovered and reused for several consecutive cycles without any considerable reactivity loss. Highly porous MnO-doped Fe3O4 NPs were synthesized via thermal decomposition of Mn/TEMPO-doped Fe3O4 NPs as a precursor, and used as an efficient recyclable catalyst for the Strecker synthesis from alcohols in TAIm[CN] ionic liquid [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) without any hole transport layer (HTL) offer cost-effective photovoltaics for the world's energy demands. The current study focuses on improving the photovoltaic performance and stability of this kind of PSC by employing an interface modification of the tin(iv) oxide (SnO2) electron transport layer (ETL). For this mission, hydrofluoric acid (HF) with different concentrations was used as a modifier and spin-coated over the SnO2 ETL. Results showed that HF-based treatment increases charge transfer at the ETL/perovskite interface by reducing charge traps in the interface, leading to a champion efficiency of 14.65% for treated PSCs, higher than the 12.92% recorded for the control PSCs. In addition, HF modification improves the wettability of SnO2 for perovskite precursors and forms a smoother and more compact perovskite layer, which boosts stability behavior in treated PSCs. [ABSTRACT FROM AUTHOR]

To find a potential sensor for the carbamazepine (CBZ) drug, pure and doped Zn12O12 nanoclusters were utilized to adsorption CBZ using the density functional theory (DFT) calculation. After adsorption of CBZ on the Zn12O12, AlZn11O12, and GaZn11O12 nanoclusters, the adsorption energies were calculated at −104.31, −142.51, and −94.89 kJ mol−1, respectively. After the adsorption process, electrical conductivity changed to −4.36%, −50.30%, and −53.71% in the Zn12O12, AlZn11O12, and GaZn11O12, respectively. Thus, it is clear that the AlZn11O12 and GaZn11O12 nanoclusters indicated a considerable change in the electrical conductivity, and these changes could be considered the signal for the detection of CBZ. Although the sensitivity of AlZn11O12 and GaZn11O12 were high, only GaZn11O12 showed an ideal recovery time for CBZ desorption from nanoclusters. Furthermore, solvent calculations indicated that the nanoclusters also could be used in biological samples. UV–Vis calculation indicated after the interaction of CBZ with GaZn11O12 spectrum shift significantly to the higher wavelength region (red shift). Thus, it can be concluded that the GaZn11O12 nanocluster is an appropriate candidate for CBZ detection. [ABSTRACT FROM AUTHOR]

In this study, Nano AA1100/SiC composites have been fabricated via accumulative press bonding (APB) on the AA1100/SiC composited which have been stir casted. Then, the effect of silicon carbide particles on the mechanical properties, wear resistance and microstructural evolution of these composites have been investigated. To study the effect of Nano SiC Wt.% and as its novelty, all the stir casted samples went under cumulative press bonding process after four cycles to guarantee a uniform distribution of reinforcement particles through the aluminum matrix. The experimental results showed that the strength of these composites were enhanced and the elongation of samples decreased by higher SiC Wt.% contents. Also, there is a significant growing in wear resistance by increasing of SiC content into the Al matrix through stir casting process. [ABSTRACT FROM AUTHOR]

In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram Model with Negative Sampling (ISGNS) based word embedding, bidirectional long short-term memory-based classification and bird swarm optimization based hyperparameter tuning. The SGNS training desires to go over the complete training data to pre-compute the noise distribution before performing Stochastic Gradient Descent (SGD) and the ISGNS technique is derived for the word embedding process. Besides, the cosine similarity is computed from the word embedding matrix to generate a feature vector which is fed as input into the Bidirectional Long Short-Term Memory (BiLSTM) for the prediction of website relevance. Finally, the Birds Swarm Optimization-Bidirectional Long Short-Term Memory (BSO-BiLSTM) based classification model is used to classify the webpages and the BSO algorithm is employed to determine the hyperparameters of the BiLSTM model so that the overall crawling performance can be considerably enhanced. For validating the enhanced outcome of the presented model, a comprehensive set of simulations are carried out and the results are examined in terms of different measures. The Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) technique has attained a higher harvest rate of 85% when compared with the other techniques. The experimental results highlight the enhanced web crawling performance of the proposed model over the recent state of art web crawlers. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) becomes a hot research area owing to an extensive set of applications. In order to accomplish energy efficiency in WSN, most of the earlier works have focused on the clustering process which enables to elect CHs and organize unequal clusters. However, the clustering process results in hot spot problem and can be addressed by the use of unequal clustering techniques, which enables to construct of clusters of unequal sizes to equalize the energy dissipation in the WSN. Unequal clustering can be formulated as an NP-hard issue and can be solved by metaheuristic optimization algorithms. With this motivation, this paper presents a novel seagull optimization (SGO) based unequal clustering (SGOBUC) model to attain energy efficiency in WSN. The SGO algorithm is mainly inspired by the migrating and attacking behaviour of seagulls. They are formulated in a mathematical way and designed to highlight exploration as well as exploitation in a provided searching area. The SGOBUC technique derives a fitness involving different parameters in such a way that energy efficiency can be accomplished. A comprehensive simulation analysis takes place to showcase the enhanced outcomes of the SGOBUC technique. The simulation outcomes highlighted the betterment of the SGOBUC technique over the recent techniques interms of different dimensions. [ABSTRACT FROM AUTHOR]