Dithiophosphonate-Protected Eight-Electron Superatomic Ag21 Nanocluster: Synthesis, Isomerism, Luminescence, and Catalytic Activity.

The structure-property relationship considering isomerism-tuned photoluminescence and efficient catalytic activity of silver nanoclusters (NCs) is exclusive. Asymmetrical dithiophosphonate NH4[S2P(OR)(p-C6H4OCH3)] ligated first atomically precise silver NCs [Ag21{S2P(OR)(p-C6H4OCH3)}12]PF6 {where, R = nPr (1), Et (2)} were established by single-crystal X-ray diffraction and characterized by electrospray ionization mass spectrometry, NMR (31P, 1H, 2H), X-ray photoelectron spectroscopy, UV-visible, energy-dispersive X-ray spectroscopy, Fourier transforms infrared, thermogravimetric analysis, etc. NCs 1 and 2 consist of eight silver atoms in a cubic framework and enclose an Ag@Ag12-centered icosahedron to constitute an Ag21 core of Th symmetry, which is concentrically inscribed within the S24 snub-cube, P12 cuboctahedron, and the O12 truncated tetrahedron formed by 12 dithiophosphonate ligands. These NCs facilitate to be an eight-electron superatom (1S21P6), in which eight capping Ag atoms exhibit structural isomerism with documented isoelectronic [Ag21{S2P(OiPr)2}12]PF6, 3. In contrast to 3, the stapling of dithiophosphonates in 1 and 2 triggered bluish emission within the 400 to 500 nm region at room temperature. The density functional theory study rationalized isomerization and optical properties of 1, 2, and 3. Both (1, and 2) clusters catalyzed a decarboxylative acylarylation reaction for rapid oxindole synthesis in 99% yield under ambient conditions and proposed a multistep reaction pathway. Ultimately, this study links nanostructures to their physical and catalytic properties.

Ag-S Type Quantum Dots versus Superatom Nanocatalyst: A Single Sulfur Atom Modulated Decarboxylative Radical Cascade Reaction.

The preparation of high-nuclearity silver nanoclusters in quantitative yield remains exclusive and their potential applications in the catalysis of organic reactions are still undeveloped. Here, we have synthesized a quantum dot (QD)-based catalyst, [Ag62S13(SBut)32](PF6)4 (denoted as Ag62S12-S) in excellent yield that enables the direct synthesis of pharmaceutically precious 3,4-dihydroquinolinone in 92% via a decarboxylative radical cascade reaction of cinnamamide with α-oxocarboxylic acid under mild reaction conditions. In comparison, a superatom [Ag62S12(SBut)32](PF6)2 (denoted as Ag62S12) with identical surface anatomy and size, but without a central S2- atom in the core, gives an improved yield (95%) in a short time and exhibits higher reactivity. Multiple characterization techniques (single-crystal X-ray diffraction, nuclear magnetic resonance (1H and 31P), electrospray ionization mass spectrometry, energy dispersive X-ray spectroscopy, Brunauer-Emmett-Teller (BET), Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis) confirm the formation of Ag62S12-S. The BET results expose the total active surface area in supporting a single e- transfer reaction mechanism. Density functional theory reveals that leaving the central S atom of Ag62S12-S leads to higher charge transfer from Ag62S12 to the reactant, accelerates the decarboxylation process, and correlates the catalytic properties with the structure of the nanocatalyst.

Dithiophosphonate Anchored Heterometallic (Ag(I)/Fe(II)) Molecular Catalysts for Electrochemical Hydrogen Evolution Reaction.

The dichalcogenide ligated molecules in catalysis to produce molecular hydrogen through electroreduction of water are rarely explored. Here, a series of heterometallic [Ag4(S2PFc(OR)4] [where Fc = Fe(η5-C5H4)(η5-C5H5), R = Me, 1; Et, 2; nPr, 3; isoAmyl, 4] clusters were synthesized and characterized by IR, absorption spectroscopy, NMR (1H, 31P), and electrospray ionization mass spectrometry. The molecular structures of 1, 2, and 3 clusters were established by single-crystal X-ray crystallographic analysis. The structural elucidation shows that each triangular face of a tetrahedral silver(I) core is capped by a ferrocenyl dithiophosphonate ligand in a trimetallic triconnective (η3; µ2, µ1) pattern. A comparative electrocatalytic hydrogen evolution reaction of 1-5 (R = iPr, 5) was studied in order to demonstrate the potential of these clusters in water splitting activity. The experimental results reveal that catalytic performance decreases with increases in the length of the carbon chain and branching within the alkoxy (-OR) group of these clusters. Catalytic durability was found effective even after 8 h of a chronoamperometric stability test along with 1500 cycles of linear sweep voltammetry performance, and only 15 mV overpotential was increased at 5 mA/cm2 current density for cluster 1. A catalytic mechanism was proposed by applying density functional theory (DFT) on clusters 1 and 2 as a representative. Here, a µ1 coordinated S-site between Ag4 core and ligand was found a reaction center. The experimental results are also in good accordance with the DFT analysis.

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.