This research explores the preparation of polypyrrole (PPy) using chemical oxidation and its enhancement with graphene oxide (GO) for optical sensor applications. PPy was synthesized by polymerizing pyrrole monomers with ferric chloride (Fe2Cl3) as the oxidant. The resulting PPy was then combined with GO to form a composite material, aiming to improve its electrical and optical properties. Polypyrrole nanofibers were obtained and after adding graphene oxide, the sensitivity increased. Characterization techniques including UV-Vis spectroscopy, DC conductivity measurements, Field Emission Scanning Electron Microscopy (FESEM) and response of photocurrent analysis were employed. The incorporation of GO into PPy resulted in a significant reducti

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In this work, chemical oxidation was used to polymerize conjugated polymer "Polypyrrole" at room temperature Graphene nanoparticles were added by in situ-polymerization to get (PPY-GN) nano. Optical and Electrical properties were studied for the nanocomposites. optical properties of the nanocomposites were studied by UV-Vis spectroscopy at wavelength range (200 -800 nm). The result showed optical absorption spectra were normally determined and the result showed that the maximum absorbance wave length at 280nm and 590nm. The optical energy gap has been evaluated by direct transition and the value has decreased from (2.1 eV for pure PPy) to (1.3 eV for 5 %wt. of GN). The optical constants such as the band tail width ΔE was evaluated, the

Photonic Crystal Fiber Interferometers (PCFIs) are greatly used
for sensing applications. This work presents the fabrication and
characterization of a relative humidity sensor based on Mach-
Zehnder Interferometer (MZI), which operates in reflection mode.
The humidity sensor operation based on the adsorption and
desorption of water vapour at the silica-air interface within the PCF.
The fabrication of this sensor is simple, it only includes splicing and
cleaving the PCF with SMF.PCF (LMA-10) with a certain length
spliced to SMF (Corning-28).
The spectrum of PCFI exhibits good sensitivity to humidity
variations. The PCFI response is observed for a range of humidity
values from (27% RH to 85% RH), the positi

In this work the structural, electrical and optical Properties of CuO semiconductor films had been studied, which prepared at three thickness (100, 200 and 500 nm) by spray pyrolysis method at 573K substrate temperatures on glass substrates from 0.2M CuCl2•2H2O dissolved in alcohol. Structural Properties shows that the films have only a polycrystalline CuO phase with preferential orientation in the (111) direction, the dc conductivity shows that all films have two activation energies, Ea1 (0.45-0.66 eV) and Ea2 (0.055-.0185 eV), CuO films have CBH (Correlated Barrier Hopping) mechanism for ac-conductivity. The energy gap between (1.5-1.85 eV).

The study effect Graphene on optical and electrical properties of glass prepared on glass substrates using sol–gel dip-coating technique. The deposited film of about (60-100±5%) nm thick. Optical and electrical properties of the films were studied under different preparation conditions, such as graphene concentration of 2, 4, 6 and 8 wt%. The results show that the optical band gap for glass-graphene films decreasing after adding the graphene. Calculated optical constants, such as transmittance, extinction coefficient are changing after adding graphene. The structural morphology and composition of elements for the samples have been demonstrated using SEM and EDX. The electrical properties of films include DC electrical conductivity; we

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to