Good Regression Coefficient Research Articles

Development and Validation of HSPiP- and Optimization-Assisted Method to Analyze Tolterodine Tartrate in Pharmacokinetic Study

A new approach was applied for the development of a precise, simple, and economic analytical process for the accurate analysis of tolterodine tartrate (TOT) in its bulk and tablet using HSPiP- and quality by design (QbD)-assisted methods. The HSPiP program predicted several solvents and their right ratios for the mobile phase, followed by simulating the experimental solubility data in various predicted solvents. QbD was used to identify the impact of the composition and the mobile phase flow rate on the peak area and retention time. TOT was estimated using an Agilent TC C18 column employing an optimized mobile phase. The HSPiP shortened the solvent selection time with high reliability, whereas QbD identified critical factors. The optimized composition and process variables were used to develop an analytical method for TOT estimation. Various analytical validation parameters were estimated with constructed linearity of 5–30 μg/mL and a percent recovery yield value of 100.36%. To ensure the reliability of the optimized method, we estimated validation parameters (linearity, specificity, precision, accuracy, robustness, and ruggedness) to comply with the ICH guidelines. Considering the high recovery yield, good regression coefficient, low detection limit, and low noise ratio, the optimized method was accurate and precise with a high degree of specificity, rapid process, and reproducibility for the quantitative estimation of tolterodine from both oral analytes (I and II). The validated method was implemented for pharmacokinetic study in rats for quantitative estimation of the analytes with high accuracy, sensitivity, and reproducibility.

Electrochemical detection of nalbuphine drug using oval-like ZnO nanostructure-based sensor

Monitoring pharmaceutical drugs in various mediums is crucial to mitigate adverse effects. This study presents a chemical sensor using an oval-like zinc oxide (ZnO) nanostructure for electrochemical detection of nalbuphine. The ZnO nanostructure, produced via an efficient sol-gel technique, was extensively characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), UV–visible spectrophotometry, and fourier transform infrared spectroscopy (FTIR). A slurry of the ZnO nanostructure in a binder was applied to a glassy carbon electrode (GCE). The sensor's responsiveness to nalbuphine was assessed using linear sweep voltammetry (LSV), achieving optimal performance by fine-tuning the pH. The sensor demonstrated a proportional response to nalbuphine concentrations up to 150.0 nM with a good regression coefficient (R2) and a detection limit of 6.20 nM (S/N ratio of 3). Selectivity was validated against various interfering substances, and efficacy was confirmed through real sample analysis, highlighting the sensor's successful application for nalbuphine detection.

Sensing, detoxification and bactericidal applications of nitrogen-doped carbon dots

Here, a one-pot hydrothermal synthesis technique was used to create nitrogen-doped carbon dots (N-CDs). Their structural and optical properties were assessed using XPS, XRD, Raman, UV–Vis, and fluorescence spectrophotometer. The N-CDs demonstrated encouraging emission quenching towards p-NP (p-Nitrophenol) and developed a sensing approach between 0.25 and 125 μM concentrations, from which a linear equation with a 20 nM detection limit was generated. In comparison to river and circulating water samples, the practicality demonstrated positive recovery rates together with impressive accuracy and precision. Additionally, the multi-functionality of N-CDs was investigated further in the catalytic reduction of methyl orange (MO) dye. With a good regression coefficient(0.9904) (R2) and rate constant (Kcat) (0.0208 min−1), N-CDs were able to reduce the azo groups in 8 min completely. Additionally, the bactericidal efficiency of N-CDs against gram-positive (Staphylococcus aureus, Listeria monocytogenes) and gram-negative (Escherichia coli, Salmonella enterica) bacteria was encouraging. The well-diffusion method demonstrated a good zone of inhibition, and the microdilution method demonstrated a promising minimum bactericidal concentration (MBC) of 470–940 μg/mL and minimum inhibitory concentration (MIC) of 240–470 μg/mL. The suggested fluorescent material may prove useful in the future for creating a fluorescence sensor, catalysts to combat water pollution, and antibacterial sprays to combat dangerous microorganisms.

Green Synthesis and Characterization of Silver Nanoparticles (AgNPs) and L-cysteine-capped AgNPs with Foeniculum vulgare seed extract for Colorimetric Hg2+ Detection

In this study, silver nanoparticles (AgNPs) and L-cysteine-capped AgNPs were synthesized separately using Foeniculum vulgare seed extract as the reducing agent and L-cysteine as the capping agent, which were characterized by ultraviolet–visible spectrophotometer (UV–vis), Fourier Transform Infrared spectrophotometer (FT-IR), X-ray diffractometer (XRD), and transmission electron microscopy (TEM). The utilization of the synthesized nanomaterials as colorimetric sensors for the detection of Hg2+ ions was also investigated. In this context, it was determined that L-cysteine-capped AgNPs exhibited better performance in the colorimetric Hg2+ detection in regards to sensitivity, selectivity, and applicability in real samples. It was observed that the colorimetric detection method was based on the disappearance of the brown color of the nanomaterial-contained colloidal solution and thus the decrease in the LSPR peak intensity. The method of the colorimetric Hg2+ detection with L-cysteine-capped AgNPs showed the good regression coefficient with the minimum detection limit of 0.36 μM in the linear Hg2+ concentration range of 1.0-10 μM, which indicated the competitive results compared to the latest reported colorimetric sensors in the literature. According to the obtained results, it has been concluded that the studied method enables to detection of Hg2+ ions colorimetrically via L-cysteine-capped AgNPs in a sensitive, selective, applicable in real samples, cheap, and easy way.

MULTIPLE LINEAR REGRESSION MODELLING FOR THE COMPACTION CHARACTERISTICS OF SEDIMENTARY SOIL MIXED BENTONITE AS COMPACTED LINER

This paper attempts to develop a prediction model for compaction characteristics such as maximum dry density (MDD) and optimum moisture content (OMC) of sedimentary residual soil mixed with bentonite as compacted liner. This prediction model was based on the laboratories testing data such as compaction testing and Atterberg limit testing of residual soil mixed with bentonite. Meanwhile, compaction testing was conducted at the different compaction energies. The Multiple Linear Regression (MLR) analysis method was selected to develop a model in determining the maximum dry density (MDD) and optimum moisture content (OMC). The predicted compaction model developed in this study was validated in accordance with the statistical validation steps and conditions. It was found from the modelling analysis, the significant relationship between the compaction energies (E) and OMC for MDD model. Meanwhile, it shows the significant relationship between liquid limit (LL), plastic limit (PL), percentage bentonite (B) and compaction energies (E) for OMC model. The fitted regression model shows the reasonably good regression coefficient for MDD model is (R2 = 78.5%) and for OMC model is (R2 = 71.9%). The models were validated by comparing between the predicted model with measured model data from published study data. It was found, the determination coefficient and mean square error (MSE) for validated model between the predicted model and the measured models gave a value of R2 = 88.7% with MSE = 0.12% for MDD model and R2 = 88% with MSE = 4.3% for OMC model.

Weaving-based wearable sensing instrument designed for the joint motion monitoring of the elbow and knee flexion angle

This work presents a wearable optical fiber sensing scheme based on an eight-figure macro-bend fiber configuration. The wearable sensor scheme utilizes a single-mode fiber deformed on an eight-figure configuration. The fabricated optical fiber sensor is mounted onto a wearable woven fabric and then garment on the elbow and knee joints of different healthy volunteers’ bodies. The proposed wearable biomechanical sensor shows an excellent sensitivity correlated with the human knee and elbow joints’ range of motion 0°-90° which is about −0.963 nm/°, with good regression coefficients (R2) exceeding 99.6%, for elbow joint flexion and sensitivity of 0.874 nm/° with a high R2 exceeding 99.4% for knee flexion. Besides, this sensor also displays high repeatability and stability and a fast response time of 1.66 ms, combined with a small standard deviation of about 2.321%. So, the planned wearable optical fiber sensor system is a practicable option for monitoring joint motion, human movement analysis, and soft robots.

Quantification of Luteolin, Apigenin and Chrysoeriol in Tecoma stans by RP-HPLC Method.

Tecoma stans (Fam. Bignoniaceae) is also popularly known as yellow bells and yellow trumpet bush in vernacular terminology. Limited and variable data are available from the literature regarding the quantification of luteolin, apigenin and chrysoeriol, which are considered as the most active pharmacological active constituents. High-performance liquid chromatography-photodiode array detection has been developed for the determination of the bioactive flavonoids, luteolin, apigenin and chrysoeriol, from the methanolic extract of the leaves of T. stans. A column packed with a pentafluorophenyl-based stationary phase was used for the separation of the analytes under gradient elution. The detection wavelength was 345nm. The validation of the method as per the International Council on Harmonisation (ICH) guidelines (ICH 2005) for linearity, accuracy and precision was investigated and found within limits specified by the ICH guidelines. The method was linear over with a good regression coefficient of more than 0.99. The limit of detection of the method was 0.68, 2.97 and 1.76μg/mL for luteolin, apigenin and chrysoeriol, respectively. In conclusion, a reliable and reproducible method was devised that can be used for the estimation of the said components from T. stans.

Removal of Remazol brilliant blue r and Remazol brilliant violet 5r dyes from aqueous solution by adsorption using coffee residue

AbstractAs a crucial ingredient for human and animal survival, clean water also contributes significantly to a nation's ever‐expanding economy. However, the availability of clean water has deteriorated as contaminants, such as dyes, have a significant detrimental effect on the pure and clean main waters. Coffee residue with micro‐ and macropores is a potential biosorbent for the removal of synthetic dyes from aqueous solutions. This study aims to determine the efficacy of adsorption of synthetic colors by coffee dregs. Using the Central Composite Design (CCD), the impacts of four adsorption factors (contact time, pH, starting concentration, and adsorbent dosage) were investigated (CCD). For the percent elimination of Remazol brilliant blue R (RBBR) and Remazol brilliant violet 5R, quadratic models were created (RBV‐5R). The ideal adsorption conditions consisted of a pH of 3, a dosage of 5 g of adsorbent, an initial concentration of 10 mg/L, and a contact period of 30 min for RBBR and 60 min for RBV‐5R, resulting in a removal of 49.98% and 93.8%, respectively. The experimental and projected values for RBBR were 96.57% and 95.2% for RBV‐5R, respectively, demonstrating high agreement with minimal errors. The Langmuir isotherm model fit the equilibrium adsorption data of the two selected dyes the best. In addition, the pseudo‐second‐order kinetic model was able to better characterize the experimental data with good respective regression coefficients.

An approach to characterize the compositional, structural and mechanical properties of thermally processed titanium (Ti) alloy simultaneously via novel in-situ laser opto-ultrasonic dual detection (LOUD)

A keen interest is developed in improving heat treatment methods of Ti alloy to meet the desirable mechanical properties due to its enhanced physical and chemical features. Structural and mechanical properties are important parameters as they are significantly affected by heat treatment and process conditions. However, most metal manufacturer relies on separate post-process analysis, which is usually time-consuming, costly, and destructive. Therefore, this study investigated a novel in-situ laser Opto-ultrasonic dual detection (LOUD) approach for real-time analysis of the elemental composition, grain size distribution, and hardness simultaneously of heat-treated Ti6AL4V alloy. The X-ray diffraction (XRD) and scanning electron microscope (SEM) results showed that with the increase in annealing temperature and cooling rate, a pronounced change in microstructure and phase transition occurs, eventually increasing grain size and hardness, respectively. The elemental composition, grain size distribution, and hardness variations were evaluated simultaneously via the processing of ultrasonic and optical signals in in-situ LOUD detection. The obtained results of average grain size from the ultrasonic acoustic attenuation of all the investigated samples are consistent with the results of optical microscope (OM) maps with a good regression coefficient value (R2 ≥0.991). Meanwhile, the optical spectral processing data for hardness analysis exhibited that the calibration curve of the emission intensity ratio (Ti-II/Ti-I) and plasma electron temperature (Te) is linearly dependent on hardness with (R2 ≥0.994). The linear correlation results of ultrasonic acoustic attenuation with grain size, calibration curve, and Te with the hardness investigated via the in-situ LOUD technique shows good approximation, ensuring quality control and reliability.

Research on fiber reinforced concrete and its performance prediction method and mix design method

To better solve the problems of performance prediction and mix design of fiber reinforced concrete (FRC), this paper carried out relevant research based on different kinds of fibers. First, according to the existing results and experimental research, fibers of various scales are selected preliminarily. Then, based on the mechanical property test, the fiber selection, fiber content and fiber hybrid mode are studied, and the fiber action mechanism is revealed through the failure process analysis and microscopic observation. Then, based on the newly constructed fiber reinforcement factor (S), a FRC strength prediction model was established. Finally, by combining S with hybrid fiber effect coefficient (RH) based on comprehensive properties, a method for FRC mix design is formed. The research results and conclusions are as follows: Steel fiber (SF), Short macroscopic polyvinyl alcohol fiber (SMP), fine polyvinyl alcohol fiber (PVA) and Calcium carbonate whisker (CW) have better performance improvement effects in their respective scales, and the content of SF, SMP, PVA and CW with the highest cost performance ratio is 2%, 0.75%, 0.5% and 1% respectively. Micro observation shows that CW not only has the effect of “micro filler” and “microfiber”, but also has the effect of friction resistance at the interface between reinforcement fiber and substrate. According to the mechanical property data and RH value, the best sample group is the sample containing 1.5% SF, 0.5% SMP and 1% CW. In addition, 1.5% SF and 0.5% SMP can also produce good performance when mixed with 1% CW respectively. The regression analysis results show that the FRC strength prediction model established in this paper has good regression coefficients. The mix design method of FRC based on S and RH can provide reference for the research and production of FRC.

Equilibrium and DFT modeling studies for the biosorption of Safranin O dye from water samples using Bacillus subtilis biosorbent

Current study deals with the biosorption of Safranin O dye from water by using high potential Bacillus subtilis biosorbent material. The presence of functional groups and surface morphology of biosorbent was analyzed by using sophisticated analytical techniques such as FT-IR (Fouirer Transform Infrared Spectrophotometer), SEM (Scanning Electron Microscopy), EDX (Energy Dispersive X-Ray Analysis), and TGA (Thermogravimetric Analysis). To check the dye removal potential of Bacillus subtilis sorbent, batch adsorption experiments were performed under the optimized conditions. The biosorption experiments showed that the Bacillus subtilis sorbents remove 80% of safranin O dye from water. The biosorption mechanism is depends upon the solution pH thus the maximum biosorption was observed at (5.5–6.5). Moreover, the biosorbent dose has been optimized and it has observed that the maximum concentration of safranin O dye has been removed using the 50 mg.L−1 of Bacillus subtilis. The biosorption equilibrium data were well fitted by the Langmuir adsorption isotherm due to good regression coefficient value (R2=0.98) and better Langmuir capacity (0.383 mmol.g−1), while the kinetic studies indicated that the biosorption followed the pseudo-second-order model. The thermodynamic parameters values showed that the biosorption is process is endothermic and spontaneous nature. In addition, molecular docking was also performed to examine the interaction between the safranin O dye molecule and the Bacillus subtilis bacterial cell line.

Molecular modelling investigation on 4-aminoquinoline derivatives as potent anti-tubercular agents

The emergency of new Mycobacterium tuberculosis strains leads to multi-drug resistant tuberculosis (MDR) and total drug resistant tuberculosis (TDR) indicating the urgency in the development of newer anti-tubercular agents to be effective against the resistance strains. In the present study pharmacophore modelling and atom-based 3D-QSAR of 4-Aminoquinoline derivatives was carried out to understand the key structural features responsible for anti-tb activity. Phase module of Schrödinger was used to generate a five-point Pharmacophore for 46 molecules belonging to 4-Aminoquinolines. The best scoring hypothesis (ADHHR_1) had one H-bond Acceptor (A1), one H-bond donor (D5), two Hydrophobic regions (H10 and H11) and one Ring (R14) with a good regression coefficient of 0.9604. The validity of the hypothesis was judged by the cross-validation coefficient (Q2= 0.7885) and Pearson- r (0.9158) followed by the external validation which showed high predictive power. Newer molecules were designed and subjected to molecular docking to explore the alleged binding modes followed by molecular dynamic simulation studies. The molecular modelling study revealed the significance of aromatic ring and hydrogen bond responsible for potent anti-tb activity.

Modelling nitrogen transformation in the Lake Bunyonyi ecosystem, South-Western Uganda

Lake Bunyonyi is one of the major resources of social-economic potential in the districts of Rubanda and Kabale, South-Western Uganda. The lake’s sub-catchment faces environmental problems because of intensive agriculture, settlement, business and tourism activities, which consequently cause pollution of water in the lake’s system. This study, therefore, intended to determine the processes that govern nitrogen dynamism using a numerical model that takes into account various processes in the system using STELLA® 8.1.1 software. From the model simulation, it was found that mineralization, microbial uptake and nitrification were the major processes governing nitrogen transformation in the water phase, accounting for 47.8% (0.49 g/d m−2), 44.2% (0.45 g/d m−2), and 7.8% (0.05 g/d m−2), respectively. The developed model predicted reasonably well the behaviour of the lake evidenced by the validation results of observed and simulated data that showed good linear regression coefficients (R2) of organic nitrogen (0.48), ammonia–nitrogen (0.68), and nitrate–nitrogen (0.61). The model has proven suitable for application on lakes with characteristics similar to that of Lake Bunyonyi. The study recommended that a compressive investigation that puts into consideration all the possible sources of nutrient and water inflow into the lake system be done on Lake Bunyonyi.

Niger Seed Oil-Based Biodiesel Production Using Transesterification Process: Experimental Investigation and Optimization for Higher Biodiesel Yield Using Box–Behnken Design and Artificial Intelligence Tools

The present work aims at cost-effective approaches for biodiesel conversion from niger seed (NS) oil by employing the transesterification process, Box–Behnken design (BBD), and artificial intelligence (AI) tools. The performances of biodiesel yield are reliant on transesterification variables (methanol-to-oil molar ratio M:O, reaction time Rt, catalyst concentration CC, and reaction temperature RT). BBD matrices representing the transesterification parameters were utilized for experiment reductions, analyzing factor (individual and interaction) effects, deriving empirical equations, and evaluating prediction accuracy. M:O showed a dominant effect, followed by CC, Rt, and RT, respectively. All two-factor interaction effects are significant, excluding the two interactions (Rt with RT and M:O with RT). The model showed a good correlation or regression coefficient with a value equal to 0.9869. Furthermore, the model produced the best fit, corresponding to the experimental and predicted yield of biodiesel. Three AI algorithms were applied (the big-bang big-crunch algorithm (BB-BC), firefly algorithm (FA), and grey wolf optimization (GWO)) to search for the best transesterification conditions that could maximize biodiesel yield. GWO and FA produced better fitness (biodiesel yield) values compared to BB-BC. GWO and FA experimental conditions resulted in a maximum biodiesel yield equal to 95.3 ± 0.5%. The computation time incurred in optimizing the biodiesel yield was found to be equal to 0.8 s for BB-BC, 1.66 s for GWO, and 15.06 s for FA. GWO determined that the optimized condition is recommended for better solution accuracy with a slight compromise in computation time. The physicochemical properties of the biodiesel yield were tested according to ASTM D6751-15C; the results are in good agreement and the biodiesel yield would be appropriate to use in diesel engines.

EFFECT OF CONCENTRATION AND TEMPERATURE ON THE RHEOLOGICAL BEHAVIOR OF HYDROXYLETHYL CELLULOSE SOLUTIONS

The knowledge of the rheological properties of polymers makes their use interesting in various fields of applications, such as food industry, cosmetics, enhanced oil recovery or construction materials. Whatever the application, the effect of temperature and concentration on these properties is of great importance. This study covered a wide range of concentrations from 0.2 w/% to 1 w/%, and temperatures from 10 °C to 80 °C. The results obtained provide interesting information regarding the effects of the temperature and concentration of the aqueous solutions of the polymer since they reveal that the rheological properties remained practically unchanged in the temperature range considered. The impacts of shear rate, temperature and concentration on the flow behavior were analyzed. Small-amplitude oscillatory shear measurements were performed, and the results obtained show that the apparent viscosity is strongly influenced by the concentration of the aqueous solution of HEC, exhibiting a marked non-Newtonian shear-thinning behavior at different temperatures. The flow behavior is well described by several rheological models. The effect of temperature on the kinematic viscosity was fitted with the Arrhenius model; the behavior of this model in relation to experimental viscosity values was suitable and the linear fit showed good regression coefficients. The dynamic state was well described with the generalized Maxwell model.

Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics

Abstract. Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.

Application of HRP-streptavidin bionanoparticles for potentiometric biotin determination

Biotin is widely used in infant formula to prevent biotin deficiency of newborn babies and in beauty products as nutritional supplements for coenzymatic functions and having strong nails, shiny hair, and skin over the last few years. There is a need for the development of a fast, simple and reusable assay method to perform biotin determination at very low concentrations. Biotin determination has achieved with a prepared potentiometric biotin sensor that has a very wide concentration range (10−15M–10−7M) and a lower detection limit (0.3 10−15M) with a very good regression coefficient (0.9925). A quick response (7 min), good accuracy (recovery 100.4–103.7%), reproducible, reusable (10 times), and long-term stability (3 months) have been obtained using the prepared potentiometric sensor. The obtained results have proved that the prepared potentiometric sensor can be used for biotin determination in real samples.

Green sample pre-treatment technique coupled with UHPLC-MS/MS for the rapid biomonitoring of dietary poly-unsaturated (omega) fatty acids to predict health risks

Polyunsaturated fatty acids (PUFAs) consumption indicates beneficial effects on cardiovascular disease (CVD) and physiological processes in humans. However, the inappropriate ratio of omega-(ω)-PUFA levels in human blood is considered as raising the risk of CVD. Therefore, monitoring dietary ω-FAs in human serum is vital for early diagnosis for individuals to predict CVD risk. This work reports a fast green sample pre-treatment protocol for sensitive and simultaneous monitoring of ω-3-FAs and ω-6-FAs in serum by novel in-syringe-based ultrasonication-assisted alkaline hydrolysis coupled with vortex-induced liquid-liquid microextraction (IS-USAH-VI-LLME) technique connected with UHPLC–MS/MS analysis. Factors affecting extraction recoveries of ten ω-PUFAs by the presented method were well-studied. ω-3 and ω-6 PUFAs demonstrated excellent linearities between the concentrations between 0.1–10,000 ng mL−1 with good regression coefficients between 0.9910–0.9997. The detection and quantification limits were between 0.05–0.35 and 0.16–1.07 ng mL−1, demonstrating that the presented method is highly sensitive and versatile. The precision of the technique was <8.2% that deemed acceptable in clinical analysis. Further, the proposed method was applied for ω-PUFAs analysis in human blood samples, and spiked recoveries showed between 80.32–119.34% with <9.82% precision. Results proved that the developed method is green, sensitive, and reliable to simultaneously determine ten ω-PUFAs in human blood samples for clinical diagnosis applications for predicting health hazards.

Zirconium Nanoparticles Based Vortex Assisted Ligandless Dispersive Solid Phase Extraction for Trace Determination of Lead in Domestic Wastewater using Flame Atomic Absorption Spectrophotometry.

In this paper, a sensitive and simple zirconium nanoparticles (Zr-NPs) based vortex assisted ligandless dispersive solid phase extraction (VA-LDSPE) method was developed for the preconcentration of lead from wastewater samples for the determination by slotted quartz tube-flame atomic absorption spectrometry (SQT-FAAS). Zr-NPs were synthesized using zirconium (IV) chloride salt as a starting material through a simple reduction process with sodium borohydride, and used as selective adsorbent for the extraction of lead ions from aqueous medium. Single variant experiments were carried out for all optimizations of sorption/desorption steps including pH of solution, amount of nanoparticles, mixing type/period and eluent type. An SQT with five round slots was placed onto the burner of FAAS to increase the interaction between lead atoms and light from radiation source to enhance the absorbance signals. Under the determined optimum conditions, analytical figures of merit were evaluated and the limit of detection and quantification (LOD/LOQ) values were calculated as 5.2 and 17.3µg L- 1, respectively. The developed method showed a linear calibration range between 25 and 250µg L- 1 with a good regression coefficient value (0.9995). Recovery studies were also performed with domestic wastewater samples spiked at three concentrations and percent recovery values obtained in the range of 97%-102% validated the developed method's applicability and accuracy.

All-silicon terahertz metamaterials absorber and pesticides sensing

Perfect absorption based on metamaterials at terahertz frequencies range has attracted a great deal of interest in the field of sensing, imaging, bolometers and stealth technology. This review is focused on presenting several recently developed absorbers based on all-silicon metamaterials, such as single-band, dual-band, multi-band and broadband absorbers. The partial physical mechanisms and optical tunability corresponding to the absorption are also reported. Furthermore, the presented absorbers can be used to detect the concentration of trace pesticides, and a good linear regression coefficient was obtained between the absorption amplitude and the concentration. Notably, the presented all-silicon metamaterials perfect absorbers are compatible with COMS processing which is beneficial to promote the development of terahertz functional devices.