جستجوی مقالات مرتبط با کلیدواژه « neural network model » در نشریات گروه « شیمی »

Tartrazine color is a synthetic organic food dye that can be found in common food products such as bakery products, dairy products, candies, and beverages, the presence and content of tartrazine color must be controlled in food products due to their potential harmfulness to human beings. Although the liquid chromatographic, and other methods for tartrazine color has advantages such as excellent accuracy and reproducibility, it has limitations such as long-time measurement, and high equipment costs. In this study, to determine tartrazine color in the solution we used a prepared from Mandarin Leaves-capped AuNPs sensor and kinetic spectrophotometric method. The calibration curve was linear in the range of (0.02 to 12.0 µg/L). The standard deviation of (3.0%), and detection limit of the method (0.02 µg/L in time 7 min, 385 nm) were obtained for sensor level response Mandarin Leaves-capped AuNPs with (95%) confidence evaluated. The observed outcomes confirmed the very low detection limit for measuring the tartrazine color in food samples. The artificial neural network model was used as a tool very low for determining mean square error (MSE = 0.515)  for tartrazine color by Mandarin Leaves-capped AuNPs sensor. The chemical Mandarin Leaves-capped AuNPs sensor made it possible as an excellent sensor with reproducibility.

The applicability of the synthesized Ricinus Communis-capeed Fe3O4NPs as a novel adsorbent for eliminating Methyl Paraben (MP) from aqueous media was investigated. Various techniques including Brunauer Emmett Teller theory (BET), Fourier Transform InfraRed (FT-IR) spectroscopy, X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray (EDX) were used to characterize this novel adsorbent. The maximum adsorption efficiency of (MP) dye onto Ricinus Communis-capeed Fe3O4 NPs was 98.6% at an optimum pH value of 7.0, the adsorbent dosage of 0.01 g, (MP) dye concentration of 15 mg/L, and contact time of 12 min were considered as the ideal values for (MP) dye. The adsorption data fitted well with the Langmuir isotherm model with a correlation coefficient (R2 > 0.97), whereas the adsorption kinetics followed the pseudo-second-order kinetics. The use of an artificial neural network model in predicting data with the Levenberg–Marquardt algorithm, purlin, or a linear transfer function at the output layer, and training was helpful. ANN model as a tool (mean square error) MSEANN = 0.0034, MSEFL = 0.023, and MSEANFIS = 0.0020 for removal of the (MP) dye onto Ricinus Communis-capeed Fe3O4 NPs synthesis. Thermodynamic parameters of free energy (ΔG0), enthalpy (ΔH0), and entropy (ΔS0) of adsorption were determined using isotherms. ∆H0=59.58 kJ/mol, ∆G0= -2.8324 kJ/mol and ∆S0=221.15 kJ/mol. K. The value of (ΔGo, ΔHo, and ΔSo) confirmed the sorption process was endothermic reflecting the affinity of Ricinus Communis-capeed Fe3O4NPs for removing (MP) dye onto Ricinus Communis-capeed Fe3O4NPs process requires heat. The maximum monolayer capacity (qmax) was observed to be 195.0 mg/g for (MP) dye at desired conditions.

The environmental pollution caused by drug antibiotic waste presents a foremost concern in the ecosystem, as  high  levels  of  these  antibiotic  drugs  after  consumption  when  released  into  the  ecosystem,  biological samples  are  accumulated  and  are  producing  overall  contamination.  Consequently,  the  need  for  selective, sensitive, fast, easy-to-handle, and low-cost early monitoring detection systems is growing. In this study, we used a prepared Albizia Lebbeck Leaves-capped AgNPs sensor to illustrate examples of friendly biosensors with  their  real  application  fields for  the  sensitive  detection  of the  metronidazole  drug  in  various  matrices such as human fluids by kinetic spectrophotometric method. The calibration curve was linear in the range of (0.02 to 10.0 µg L−1). The standard deviation of less than (3%), and detection limits (3S/m) of the method (0.02 µg L−1in time 8 min, 367 nm) were obtained for sensor level response Albizia Lebbeck Leaves-capped AgNPs with (95%) confidence evaluated. The artificial neural network model was used as a tool very low for determining  mean  square  error  (MSE  0.061)  for  metronidazole  drug  by Albizia  Lebbeck  Leaves-capped AgNPs sensor. The observed outcomes confirmed the suitability of recovery and a very low detection limit for  measuring  the  metronidazole  drug.  The  method  introduced  to  measure  metronidazole  drugs  in  real samples  such  as  urine  and  blood  was  used  and  can  be  used  for  other  drugs  environmental  pollution  and hospital samples.