Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

Electrochemical Detection of Cortisol by Silver Nanoparticle-Modified Molecularly Imprinted Polymer-Coated Pencil Graphite Electrodes.

The sensitive cortisol detection by an electrochemical sensor based on silver nanoparticle-doped molecularly imprinted polymer was successfully improved. This study describes the method development for cortisol detection in both aqueous solution and biological samples using molecularly imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-(l)-histidine methyl ester)-coated pencil graphite electrodes modified with silver nanoparticles (AgNPs) by differential pulse voltammetry (DPV). The cortisol-imprinted pencil graphite electrode (PGE) has a large surface area because of doped AgNPs with enhanced electroactivity. The prepared molecularly imprinted polymer was characterized by scanning electron microscopy. The DPV response of the synthesized electrode with outstanding electrical conductivity was clarified. Cortisol-imprinted polymer-coated PGEs (MIP), cortisol-imprinted polymer-coated PGEs with AgNPs (MIP@AgNPs), and nonimprinted polymer-coated PGEs with AgNPs (NIP@AgNPs) were evaluated for sensitive and selective detection of cortisol in aqueous solution. Five different cortisol concentrations (0.395, 0.791, 1.32, 2.64, and 3.96 nM) were applied to the MIP@AgNPs, and signal responses were detected by the DPV with a regression coefficient (R2) value of 0.9951. The modified electrode showed good electrocatalytic activity toward cortisol for the linear concentration range from 0.395 to 3.96 nM, and a low limit of detection was recorded as 0.214 nM. The results indicate that the MIP@AgNPs sensor has great potential for sensitive and selective cortisol determination in biological samples.

Simple and Fast Pesticide Nanosensors: Example of Surface Plasmon Resonance Coumaphos Nanosensor.

Here, a molecular imprinting technique was employed to create an SPR-based nanosensor for the selective and sensitive detection of organophosphate-based coumaphos, a toxic insecticide/veterinary drug often used. To achieve this, UV polymerization was used to create polymeric nanofilms using N-methacryloyl-l-cysteine methyl ester, ethylene glycol dimethacrylate, and 2-hydroxyethyl methacrylate, which are functional monomers, cross-linkers, and hydrophilicity enabling agents, respectively. Several methods, including scanning electron microscopy (SEM), atomic force microscopy (AFM), and contact angle (CA) analyses, were used to characterize the nanofilms. Using coumaphos-imprinted SPR (CIP-SPR) and non-imprinted SPR (NIP-SPR) nanosensor chips, the kinetic evaluations of coumaphos sensing were investigated. The created CIP-SPR nanosensor demonstrated high selectivity to the coumaphos molecule compared to similar competitor molecules, including diazinon, pirimiphos-methyl, pyridaphenthion, phosalone, N-2,4(dimethylphenyl) formamide, 2,4-dimethylaniline, dimethoate, and phosmet. Additionally, there is a magnificent linear relationship for the concentration range of 0.1-250 ppb, with a low limit of detection (LOD) and limit of quantification (LOQ) of 0.001 and 0.003 ppb, respectively, and a high imprinting factor (I.F.4.4) for coumaphos. The Langmuir adsorption model is the best appropriate thermodynamic approach for the nanosensor. Intraday trials were performed three times with five repetitions to statistically evaluate the CIP-SPR nanosensor's reusability. Reusability investigations for the two weeks of interday analyses also indicated the three-dimensional stability of the CIP-SPR nanosensor. The remarkable reusability and reproducibility of the procedure are indicated by an RSD% result of less than 1.5. Therefore, it has been determined that the generated CIP-SPR nanosensors are highly selective, rapidly responsive, simple to use, reusable, and sensitive for coumaphos detection in an aqueous solution. An amino acid, which was used to detect coumaphos, included a CIP-SPR nanosensor manufactured without complicated coupling methods and labelling processes. Liquid chromatography with tandem mass spectrometry (LC/MS-MS) studies was performed for the validation studies of the SPR.

Machine learning-based models for the qualitative classification of potassium ferrocyanide using electrochemical methods.

Iron is one of the trace elements that plays a vital role in the human immune system, especially against variants of SARS-CoV-2 virus. Electrochemical methods are convenient for the detection due to the simplicity of instrumentation available for different analyses. The square wave voltammetry (SQWV) and differential pulse voltammetry (DPV) are useful electrochemical voltammetric techniques for diverse types of compounds such as heavy metals. The basic reason is the increased sensitivity by lowering the capacitive current. In this study, machine learning models were improved to classify concentrations of an analyte depending on the voltammograms obtained alone. SQWV and DPV were used to quantify the concentrations of ferrous ions (Fe+2) in potassium ferrocyanide (K4Fe(CN)6), validated by machine learning models for the data classifications. The greatest classifier algorithms models Backpropagation Neural Networks, Gaussian Naive Bayes, Logistic Regression, K-Nearest Neighbors Algorithm, K-Means clustering, and Random Forest were used as data classifiers, based on the data sets obtained from the measured chemical. Once competed to other algorithms models used previously for the data classification, ours get greater accuracy, maximum accuracy of 100% was obtained for each analyte in 25 s for the datasets.

Preparation of Immobilised 17ß-Estradiol-Imprinted Nanoparticles onto Bacterial Cellulose Nanofibres to Use for the Removal of 17ß-Estradiol from Wastewater.

Estradiol, a phenolic steroid oestrogen, is one of the endocrine-disrupting chemicals (EDCs) found in natural and tap waters. The detection and removal of EDCs is attracting attention daily as they negatively affect animals' and humans' endocrine functions and physiological conditions. Therefore, developing a fast and practical method for the selective removal of EDCs from waters is essential. In this study, we prepared 17ß-estradiol (E2)-imprinted HEMA-based nanoparticles onto bacterial cellulose nanofibres (E2-NP/BC-NFs) to use for the removal of E2 from wastewater. FT-IR and NMR confirmed the structure of the functional monomer. The composite system was characterised by BET, SEM, µCT, contact angle, and swelling tests. Additionally, the non-imprinted bacterial cellulose nanofibres (NIP/BC-NFs) were prepared to compare the results of E2-NP/BC-NFs. Adsorption of E2 from aqueous solutions was performed in batch mode and investigated via several parameters for optimisation conditions. The effect of pH studies was examined in the 4.0-8.0 range using acetate and phosphate buffers and a concentration of E2 of 0.5 mg/mL. The maximum E2 adsorption amount was 254 µg/g phosphate buffer at 45 °C. The experimental data show that the Langmuir is a relevant isotherm model for E2 adsorption. Additionally, the relevant kinetic model was the pseudo-second-order kinetic model. It was observed that the adsorption process reached equilibrium in less than 20 min. The E2 adsorption decreased with the increase in salt at varying salt concentrations. The selectivity studies were performed using cholesterol and stigmasterol as competing steroids. The results show that E2 is 46.0 times more selective than cholesterol and 21.0 times more selective than stigmasterol. According to the results, the relative selectivity coefficients for E2/cholesterol and E2/stigmasterol were 8.38 and 86.6 times greater for E2-NP/BC-NFs than for E2-NP/BC-NFs, respectively. The synthesised composite systems were repeated ten times to assess the reusability of E2-NP/BC-NFs.

Development of a Plasmonic Sensor for a Chemotherapeutic Agent Cabazitaxel.

Drug dosage is a crucial subject in both human and animal treatment. Administering less drug dosage may prevent treatment or make it less effective, and high drug dosage may cause a heightened risk of adverse effects, or in some cases, cost a patient's life. Also, even when the dosage is administered carefully, metabolic differences may cause different effects on different patients. Because of these considerations, monitoring drug dosage in the body is a critical and significant requirement in the health industry. Within the scope of this study, a reusable surface plasmon resonance (SPR) chip with fast response, high selectivity, and no pretreatment is produced for the chemotherapeutic agent cabazitaxel. A cabazitaxel-imprinted nanofilm was synthesized on the sensor chip surface and characterized by atomic force microscopy, ellipsometry, and contact angle measurements. Standard cabazitaxel solution and an artificial plasma sample were used for the kinetic analysis. Docetaxel, methylprednisolone, and dexamethasone were analyzed for their selectivity experiment. In addition, the repeatability and storage durability of the sensor were also evaluated. As a result of the adsorption studies, the limit of detection and limit of quantitation values were found to be 0.012 and 0.036 µg/mL, respectively. High-performance liquid chromatography analysis was used to validate the response of the cabazitaxel-imprinted sensor.

Computational Investigation of the Monomer Ratio and Solvent Environment for the Complex Formed between Sulfamethoxazole and Functional Monomer Methacrylic Acid.

In this study, the molecularly imprinted polymers (MIPs) that will be formed by the sulfamethoxazole (SMX) molecule and methacrylic acid (MAA) molecule were examined theoretically. The most stable interaction region between the two molecules was determined in solvent environments (ethanol, acetonitrile, and dimethylsulfoxide), and monomer ratios (SMX/MAA; 1:1, 1:2, and 1:3) were examined to form the most stable geometry. The number and length of the hydrogen bonds formed between the template molecule and the functional monomer and the interaction between the atoms were determined. Geometry optimizations of the molecules were calculated by the DFT method at the M06-2X/ccpVTZ level, and single-point energy calculations were carried out at the B2PLYP-D3/ccpVDZ level. In addition to the theoretical studies, the experimental Fourier-transform infrared spectroscopy (FTIR) spectrum of the complex formed between SMX and MAA was compared with the theoretical FTIR spectrum. As a result of the studies, the monomer ratio and solvent environment in which the stable complex was formed were determined in the MIP studies carried out with the SMX template molecule and MAA monomer. The most stable template molecule-monomer ratio of the complex between SMX and MAA was determined to be 1:3, and the solvent medium in which the most stable geometry was formed was acetonitrile.

Gold-Modified Molecularly Imprinted N-Methacryloyl-(l)-phenylalanine-containing Electrodes for Electrochemical Detection of Dopamine.

A molecularly imprinted polymer-based pencil graphite electrode (MIP PGE) sensor, modified with gold nanoparticles, was utilized for the detection of dopamine in the presence of other biochemical compounds using cyclic voltammetry (CV) and differential pulse voltammetry (DPV), depending on its strong electroactivity function. The pulse voltammetry methods recorded the highest response. In addition to the high oxidation rate of DA and the other biomolecule interferences available in the sample matrix used, which cause overlapping voltammograms, we aimed to differentiate them in a highly sensitive limit of detection range. The calibration curves for DA were obtained using the CV and DPV over the concentration range of 0.395-3.96 nM in 0.1 M phosphate buffer solution (PBS) at pH 7.4 with a correlation coefficient of 0.996 and a detection limit of 0.193 nM. The electrochemical technique was employed to detect DA molecules quantitatively in human blood plasma selected as real samples without applying any pre-treatment processes. MIP electrodes proved their ability to detect DA with high selectivity, even with epinephrine and norepinephrine competitor molecules and interferences, such as ascorbic acid (AA). The high level of recognition achieved by molecularly imprinted polymers (MIPs) is essential for many biological and pharmaceutical studies.

Electrochemical Sensor for Determination of Various Phenolic Compounds in Wine Samples Using Fe3O4 Nanoparticles Modified Carbon Paste Electrode.

Phenolic compounds contain classes of flavonoids and non-flavonoids, which occur naturally as secondary metabolites in plants. These compounds, when consumed in food substances, improve human health because of their antioxidant properties against oxidative damage diseases. In this study, an electrochemical sensor was developed using a carbon paste electrode (CPE) modified with Fe3O4 nanoparticles (MCPE) for the electrosensitive determination of sinapic acid, syringic acid, and rutin. The characterization techniques adapted for CPE, MCPE electrodes, and the solution interface were cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Scan rate and pH were the parameters subjected to optimization studies for the determination of phenolic compounds. The incorporation of Fe3O4 nanoparticles to the CPE as a sensor showed excellent sensitivity, selectivity, repeatability, reproducibility, stability, and low preparation cost. The limits of detection (LOD) obtained were 2.2 × 10-7 M for sinapic acid, 2.6 × 10-7 M for syringic acid, and 0.8 × 10-7 M for rutin, respectively. The fabricated electrochemical sensor was applied to determine phenolic compounds in real samples of red and white wine.

Polymethacryloyl-L-Phenylalanine [PMAPA]-Based Monolithic Column for Capillary Electrochromatography.

The ability to detect catecholamines (CAs) and their metabolites is vital to understand the mechanism behind the neuronal diseases. Neurochemistry aims to provide an improved pharmacological, molecular and physiological understanding of complex brain chemistries by analytical techniques. Capillary electrophoresis (CE) is one such analytical technique that enables the study of various chemical species ranging from amino acids and peptides to natural products and drugs. CE can easily adapt the changes in research focus and in recent years remains an applicable technique for investigating neuroscience and single cell neurobiology. The prepared phenylalanine-based hydrophobic monolithic column, Polymethacryloyl-L-phenylalanine [PMAPA], was used as a stationary phase in capillary electrochromatography to separate CAs that are similar in size and shape to each other including dopamine (DA) and norepinephrine (NE) via hydrophobic interactions. Separation carried out in a short period of 17 min was performed with the electrophoretic mobility of 5.54 × 10-6 m2 V-1 s-1 and 7.60 × 10-6 m2 V-1 s-1 for DA and NE, respectively, at pH 7.0, 65% acetonitrile ratio with 100 mbar applied pressure by the developed hydrophobic monolithic column without needing any extra process such as imprinting or spacer arms to immobilize ligands used in separation.

Dopamine-imprinted monolithic column for capillary electrochromatography.

A dopamine-imprinted monolithic column was prepared and used in capillary electrochromatography as stationary phase for the first time. Dopamine was selectively separated from aqueous solution containing the competitor molecule norepinephrine, which is similar in size and shape to the template molecule. Morphology of the dopamine-imprinted column was observed by scanning electron microscopy. The influence of the organic solvent content of mobile phase, applied pressure and pH of the mobile phase on the recognition of dopamine by the imprinted monolithic column has been evaluated, and the imprinting effect in the dopamine-imprinted monolithic polymer was verified. Developed dopamine-imprinted monolithic column resulted in excellent separation of dopamine from structurally related competitor molecule, norepinephrine. Separation was achieved in a short period of 10 min, with the electrophoretic mobility of 5.81 × 10-5  m2 V-1 s-1 at pH 5.0 and 500 mbar pressure.

Triazine herbicide imprinted monolithic column for capillary electrochromatography.

Trietazine was selectively separated from aqueous solution containing the competitor molecule cyanazine, which is similar in size and shape to the template molecule. Structural features of the molecularly imprinted column were figured out by SEM. The influence of the mobile-phase composition, applied electrical field, and pH of the mobile phase on the recognition of trietazine by the imprinted monolithic polymer has been evaluated, and the imprint effect in the trietazine-imprinted monolithic polymer was demonstrated by an imprinting factor. The optimized monolithic column resulted in separation of trietazine from a structurally related competitor molecule, cyanazine. In addition, fast separation was obtained within 6 min by applying higher electrical field, with the electrophoretic mobility of 2.97 × 10(-8) m(2) V(-1) s(-1) at pH 11.0.

Synthesis of a novel fluorescent amphiphilic chitosan biopolymer: photophysical and electrochemical behavior.

The present investigation describes for the first time, the synthesis and detailed characterization of a novel fluorescent and amphiphilic chitosan polymer (3) containing fluorescent peryleneimide chromophores for biomedical applications. The polymer 3 is moderately soluble in a wide range of organic solvents and aqueous solutions, unlike chitosan (2). The Mw of 3 and 2 determined by GPC were 467 kDa and 460 kDa, respectively. The photophysical and electrochemical properties measured in solution and solid states are engrossing. is soluble in the entire pH range and exhibits excimer emission above pH 5. In solution, 3 is electroactive but 2 is not. Whereas in the solid-state, 3 shows one quasi-reversible oxidation and reversible reduction step and 2 exhibits only one quasi-reversible oxidation step. Our results point out a new class of organic biopolymers that could yield promising potentials in many biomedical applications.