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Inelastic electron tunneling spectroscopy (IETS) of an alkanedithiol self-assembled monolayer (SAM) is investigated using a nanometer-scale device. The IETS spectrum of the octanedithiol device clearly shows vibrational signatures of an octanedithiolate, −SC 8 H 16 S−, bonded to gold electrodes. The pronounced IETS peaks correspond to vibrational modes perpendicular to the junction interface, which include the stretching modes of Au−S (at 33 mV) and C−C (at 133 mV) and the wagging mode of CH 2 (at 158 mV). The observed peak intensities and peak widths are in good agreement with theoretical predictions.

Presented here are several convergent synthetic routes to conjugated oligo(phenylene ethynylene)s. Some of these oligomers are free of functional groups, while others possess donor groups, acceptor groups, porphyrin interiors, and other heterocyclic interiors for various potential transmission and digital device applications. The syntheses of oligo(phenylene ethynylene)s with a variety of end groups for attachment to numerous metal probes and surfaces are presented. Some of the functionalized molecular systems showed linear, wire-like, current versus voltage (I(V)) responses, while others exhibited nonlinear I(V) curves for negative differential resistance (NDR) and molecular random access memory effects. Finally, the syntheses of functionalized oligomers are described that can form self-assembled monolayers on metallic electrodes that reduce the Schottky barriers. Information from the Schottky barrier studies can provide useful insight into molecular alligator clip optimizations for molecular electronics.

A review of the mechanisms and characterization methods of molecular electronic transport is presented. Using self-assembled monolayers (SAMs) of alkanethiols in a nanometer-scale device structure, tunneling is unambiguously demonstrated to be the main conduction mechanism for large band gap SAMs exhibiting well-known temperature and length dependencies. Inelastic electron tunneling spectroscopy exhibits clear vibrational modes of the molecules in the device, presenting the first direct evidence of the presence of molecules in a molecular transport device and confirming the tunneling transport mechanism in alkane self-assembled monolayers.

A review on the mechanisms and characterization methods of electronic transport through self-assembled monolayers (SAMs) is presented. Using SAMs of alkanethiols in a nanometre scale device structure, tunnelling is unambiguously demonstrated as the main intrinsic conduction mechanism for defect-free large bandgap SAMs, exhibiting well-known temperature and length dependences. Inelastic electron tunnelling spectroscopy exhibits clear vibrational modes of the molecules in the device, presenting direct evidence of the presence of molecules in the device.

Room-temperature charge transport is investigated for various-length alkanethiol self-assembled monolayers using three different characterization methods, in which lateral areas span from the nanometer to the micrometer scale. In each method, the measured current-voltage characteristics are analyzed with metal-insulatormetal tunneling models. Transport parameters are determined where possible and compared across methods, as well as to previously reported values. Advantages and limitations of each method for characterizing molecular junctions are highlighted.

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

In this work dye-sensitized solar cells (DSSCs) based on Zn 2 SnO 4 nanowires were fabricated for the first time. High yield growth of Zn 2 SnO 4 nanowires were achieved through the optimization of the growth conditions. The adsorption of the N719 dye on Zn 2 SnO 4 nanowires and its effect on cell performance were inspected. From a comparison study of Zn 2 SnO 4 nanowire-and nanoparticle-DSSCs, a more than 0.1 V improvement on open-circuit voltage was observed when Zn 2 SnO 4 nanowires were used as the photoanode. The relation between open-circuit voltage and back electron transfer reaction was examined. Due to the advantages of ternary metal oxides and single crystal nanowires, such nanostructures could lead to a more versatile selection for photoanode materials and improved performance of sensitized solar cells.