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Functionalization of metal-chalcogenide clusters by either replacing one or more of the core metal atoms or by choosing the ligand is a powerful technique to tailor their properties and synthesize cluster assembled materials. Central to this approach is to understand the competition between the strength of the metal-ligand and metal-metal interactions within the cluster. We use collision-induced dissociation (CID) of two series of atomically precise metal sulfide nanoclusters including Co5MS8L6+ (L=PEt3, M= Mn, Fe, Co, Ni) and Co5-xFexS8L6+ (x=1-3) to understand the effect of a heteroatom incorporation on the core-ligand interactions and relative stability towards fragmentation. We observe sequential ligand loss as the dominant dissociation pathway for all the clusters, which occurs in competition with the loss of ligand sulfide (LS). Because all the ligands are attached to metal atoms in the precursor cluster cations, LS loss is an unusual dissociation pathway, indicative of a rearrangement of the cluster prior to fragmentation. Collision energy-resolved CID combined with theoretical calculations reveals the reduced stability of Co5MnS8L6+ and Co5FeS8L6+ cations towards the loss of the first ligand in comparison with their Co6S8L6+ and Co5NiS8L6+ counterparts. Moreover, we observe that the relative stability of clusters towards ligand loss as well as the competition between ligand and LS losses are strongly dependent on the composition of the clusters. Theoretical studies also confirm that the first ligand always prefers to detach from the doped-metal atom site to form Co5MS8L5+ cations. This study provides new insight into modulation of the core-ligand interaction of the atomically precise metal chalcogenide clusters suggesting paths to generate reactive species to promote inter-cluster reactions.

A novel SnO2@Cu3(BTC)2 composite was synthesized using a quick and affordable bottom-up approach via impregnation of SnO2 nanoparticles into the porous Cu3(BTC)2 metal-organic framework (MOF). The photocatalytic degradation of the methylene blue (MB) dye has been studied for the first time using this novel recyclable SnO2@Cu3(BTC)2 composite. It was found that SnO2@Cu3(BTC)2 composite photo catalytically degrades methylene blue (MB) dye with a degradation efficiency of 85.12% within 80 min under solar irradiation. The most appropriate benefit of this composite is the easy recyclability up to numerous cycles with retention of its photocatalytic activity. Therefore, this cheaper and greener composite photocatalyst is more suitable for large-scale industrial applications than the traditional photocatalysts employed in the degradation of MB dye. Furthermore, this composite has also been investigated as a fluorescence sensor for the detection of nitroaromatic compounds (NACs). It was observed that the 88.2% quenching of the intense fluorescent signal of this composite happens in the presence of 2,4,6-trinitrophenol (TNP) showing it incredibly selectivity towards TNP with no interference of other NACs. With a detection limit of 2.82 µM, this composite exhibits outstanding sensitivity towards TNP. The Stern-Volmer plot for TNP is linearly fitted displays large quenching coefficient, correlation coefficient, and linear ranges KSV = 1.04x104 M-1, R2 = 0.9901, and 0-10 µM, respectively. This quenching response of this composite towards TNP was well-explained by the two mechanisms: one is photo-induced electron transfer (PET), and the other is fluorescence resonance energy transfer (FRET), in addition to theoretical calculations based on density functional theory (DFT). Our findings imply that the synthetic composite can be used as a superior fluorescence sensor and photocatalyst.

Indian poultry industry has evolved from a simple backyard occupation to a large commercial agri-based enterprise. Chicken dominates poultry production in India, accounting for almost 95% of total egg production. Several factors affect the egg production such as feeding material, drinking water, environmental factors etc. Analyzing the water quality is one of the important tasks. Cauvery River is considered as the study area because of its importance in several states of South India which have significant contribution in poultry farming. The aim of the proposed study is to develop an automated approach of water quality analysis and present a novel machine learning approach which considers an improved feature ranking method and ensemble tree classifier with majority voting. The experimental result shows that proposed approach performs better with an accuracy of 95.12%.

Read Full License spectral overlap, respectively. Hence, synthesized composite is veri ed as multi-component system to act as excellent photocatalyst as well as uorescent sensor.