Modified hydrothermal method for synthesizing titanium dioxide-decorated multiwalled carbon nanotube nanocomposites for the solar-driven photocatalytic degradation of dyes.

This study aimed to address the issue of rapid electron-hole recombination in photocatalysis by exploiting multi-phase TiO decorated on multiwalled carbon nanotubes (MWCNTs) to improve the photocatalytic degradation of dyes. A simple and eco-friendly one-pot method was utilized to create the TiO/MWCNT nanostructure using glucose as both a structure-directing agent and a carbon source without requiring any prior covalent or non-covalent functionalization of the MWCNTs at 160 °C. Furthermore, it was found that the average width of the nanocomposites changed from 20 ± 1 and 42 ± 2 nm to 56 ± 3 nm, corresponding to MWCNT contents of 1.

Gold nanoparticle-decorated amorphous carbon for oxidative cyclization of anthranilamide and aryl alcohols.

Herein, gold nanoparticle-decorated amorphous carbon (Au/AC) was prepared by reducing chloroauric acid and layering it on amorphous carbon. The characteristics of Au/AC were examined using FT-IR, XRD, TGA, SEM, EDS, and XPS. To assess its catalytic activity, Au/AC was tested in a reaction involving anthranilamide and benzyl alcohol at 110 °C for 24 hours.

Deep latent variable joint cognitive modeling of neural signals and human behavior.

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data.

Binding Mechanism and Surface-Enhanced Raman Scattering of the Antimicrobial Sulfathiazole on Gold Nanoparticles.

A combined study using the surface-enhanced Raman scattering (SERS) technique and quantum chemical calculations was carried out to elucidate the adsorption behavior of sulfathiazole, an antibiotic drug, on gold nanoparticles. The tetrahedral Au cluster was used as a simple model to mimic a nanostructured gold surface. Computations using density functional theory with the PBE functional were performed in both the gas phase and aqueous medium using a continuum model.

Differences between surfactant-free Au@Ag and CTAB-stabilized Au@Ag star-like nanoparticles in the preparation of nanoarrays to improve their surface-enhanced Raman scattering (SERS) performance.

In this study, we assessed the controlled synthesis and efficacy of surface-enhanced Raman scattering (SERS) on two distinct types of star-like Au@Ag core-shell nanoarrays. These nanoarrays were designed based on gold nanostars (AuNSs), which were synthesized with and without CTAB surfactant (AuNSs-CTAB and AuNSs-FS, respectively). The AuNS-FS nanoparticles were synthesized a novel modification process, which helped overcome the previous limitations in the free-surfactant preparation of AuNSs by significantly increasing the number of branches, increasing the sharpness of the branches and minimizing the adsorption of the surfactant on the surface of AuNSs.

Single-step controlled synthesis of flower-like gold nanoparticles stabilized by chitosan for sensitive detection of heparin using a surface-enhanced Raman scattering method.

A novel single-step and template-free procedure, including controlled synthesis of gold flowers (AuNFs), conjugation to a 4-MBA reporter, and stabilization with chitosan, is proposed to develop the SERS tags-based nanoparticles for trace detection of heparin. This SERS detection assay is based on the aggregation/non-aggregation balance of AuNFs-4-MBA@chitosan nanoparticles, which was induced by adding a very low concentration of heparin in the as-synthesized colloidal solutions. SERS-tag colloids are prepared by mixing chitosan with HAuCl and 4-mercapto benzoic acid before being reduced with ascorbic acid under appropriate pH conditions.

Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System.

Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.

Facile tuning of tip sharpness on gold nanostars by the controlled seed-growth method and coating with a silver shell for detection of thiram using surface enhanced Raman spectroscopy (SERS).

Developing SERS substrates based on individual gold and silver metals, either with rough surfaces or bare nanoparticles, has certain limitations in practical analysis applications. In order to improve the range of applications of the noble metallic substrates, a comprehensive approach has been proposed for preparing non-traditional SERS nano-substrates by combining tip-enhanced gold nanostars and Raman signal amplification of the silver layer. This preparation process is conducted in two steps, including tuning the sharpness and length of tips by a modified seed growth method followed by coating the silver layer on the formed star-shaped nanoparticles.

A novel wireless ECG system for prolonged monitoring of multiple zebrafish for heart disease and drug screening studies.

Zebrafish and their mutant lines have been extensively used in cardiovascular studies. In the current study, the novel system, Zebra II, is presented for prolonged electrocardiogram (ECG) acquisition and analysis for multiple zebrafish within controllable working environments. The Zebra II is composed of a perfusion system, apparatuses, sensors, and an in-house electronic system.

Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.

Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantifying and monitoring cardiac functions mainly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos.

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram.

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother.