Fast, Selective and Sensitive Fluorescence Detection of Levofloxacin, Fe3+ and Cu2+ Ions in 100% Aqueous Solution Via Their Reciprocal Recognition.

The fluorescence detection of ions and pharmaceutical effluents by using organic chemosensors is a valuable surrogate to the currently existing expensive analytical methods. In this regard, the design of multi-functional chemosensors to recognize desirable guests is of utmost importance. In this study, we first show that levofloxacin (LVO) is able to use as a fluorescent chemosensor for the detection of biologically important Cu2+ (turn-off) and Fe3+ (turn-on) ions via independent signal outputs in 100% aqueous buffer solutions. Next, using the reciprocal recognition of LVO and Fe3+ provides a unique emission pattern for the detection of LVO. This approach exhibited a high specificity to LVO among various pharmaceutical samples, namely acetaminophen (AC), azithromycin (AZ), gemifloxacin (GEM) and ciprofloxacin (CIP) and also showed great anti-interference property in urine. The attractive features of this sensing system are availability, easy-to-use, high sensitivity (limit of detection = 18 nM for Cu2+, 22 nM for Fe3+ and 0.12 nM for LVO), rapid response (5 s) with an excellent selectivity. Levofloxacin (LVO) is able to use as a fluorescent chemosensor for the detection of Cu2+ (turn-off) and Fe3+ (turn-on) ions via independent signal outputs. Moreover, using the reciprocal recognition of LVO and Fe3+ a unique emission pattern for the detection of LVO was achieved which is applicable for biological samples. The attractive features of this sensing system are availability, easy-to-use, high sensitivity, rapid response (5 s) with an excellent selectivity.

Genetic algorithm-assisted artificial neural network modelling for remediation and recovery of Pb (II) and Cr(VI) by manganese and cobalt spinel ferrite super nanoadsorbent.

MnFe2O4 and CoFe2O4 nanoparticles were hydrothermally synthesized to examine their capability in adsorption of Pb (II) and Cr (VI). The adsorbents exhibited a high rate of adsorption, reaching 90% of their adsorption capacity in less than 30 min. Furthermore, the adsorption capability of the Magnetic Nanoparticles (MNPs) was noticeably greater at initial pollutant concentrations smaller than 40 mg/L. Maximum adsorption capacity on MnFe2O4 and CoFe2O4 nanoparticles were 40 and 25.38 mg/g for Cr (VI) and 523.32 and 476.19 mg/g for Pb (II), respectively. A data-driven model of Artificial Neural Network was used for prediction of adsorption capacity at both equilibrium and non-equilibrium condition. The model parameters including the numbers of neuron (n = 7) and data portioning for training (49.5%), validation (40.5%), and testing (10%) were obtained using Genetic Algorithm. The results indicated that the model could predict the data with high accuracy (R2 = 0.998). The input parameters were initial concentration, time, pH, temperature, adsorbent dosage, and other parameters that is dependent to the physico-chemical properties of ions and adsorbents' surface (ε, α1, α2). The mechanism involved in Cr(VI) and Pb(II) adsorption are electrostatic physisorption and a combination of ion exchange chemisorption and electrostatic physisorption, respectively. Desorption capability and adsorbent reuse capability were also examined.

Superadsorbent Fe3O4-coated carbon black nanocomposite for separation of light rare earth elements from aqueous solution: GMDH-based Neural Network and sensitivity analysis.

A series of nanocomposites adsorbents with different magnetite/carbon black ratios were synthesized by using the co-precipitation method and used for separation of LREEs (Ce, La, and Nd) from aqueous solution. The adsorption efficiency of nanocomposites is strongly dependent on both pH and the loading carbon on nanocomposite. The maximum adsorption capacity (370 mg/g) was reported by nanocomposite with 20% Fe3O4 and 80% carbon in pH 7 for LREE initial concentration of 250 ppm. Results revealed that the LREEs adsorption behavior of the optimal adsorbent fits well with Langmuir isotherm and pseudo-first-order kinetic model. Moreover, the average values of thermodynamic parameters suggest the endothermic and irreversible chemisorption mechanism. An empirical correlation was obtained by using GMDH (Group Method of Data Handling)-based Neural Network to predict the adsorption kinetics of LREEs as a function of ion's electronegativity, molecular weight, and initial concentration. The results showed that the correlation can predict the experimental data mostly lower than 12.5% and it can predict the results of other researches with similar conditions with up to 25% from the experimental values. Finally, the results of sensitivity analysis revealed that the adsorption of LREEs is more sensitive to ions electronegativity and molecular weight at equilibrium conditions.

A highly sensitive "ON-OFF" optical sensor for the selective detection of cyanide ions in 100% aqueous solutions based on hydrogen bonding and water assisted aggregation induced emission.

Nanoparticles N,N'-(pyridine-2,6-diyl)bis(2-(2,4-dichlorophenoxy)acetamide) (1) exhibited an "on-off" emission response toward cyanide (CN-) ions in 100% aqueous solutions based on AIE features. AIEgen 1 is an easy-to-use probe that exhibits rapid response (5 s), extremely high sensitivity (limit of detection = 8.2 nM) and excellent selectivity. The sensing performance of CN- through a test kit and bitter seed solutions was good. The experimental results show that compound 1 is planar and can self-assemble into a supramolecular system and show blue emission. Then, CN- destroys both hydrogen bonds and the aggregates of 1 and quenches the emission. This process is reversible upon the addition of HCl solution.