A novel ratiometric fluorescent approach for the modulation of the dynamic range of lateral flow immunoassays

The majority of lateral flow assays (LFAs) use single-color optical labels to provide a qualitative naked-eye detection, however this detection method displays two important limitations. First, the use of a single-color label makes the LFA prone to results misinterpretation. Second, it does not allow the precise modulation of the sensitivity and dynamic range of the test. To overcome these limitations, a ratiometric approach is developed. In particular, using anti-HIgG functionalized red-fluorescent quantum dots on the conjugate pad (as target dependent labels) and blue-fluorescent nanoparticles fixed on the test line (as target independent reporters), it is possible to generate a wide color palette (blue, purple, pink, red) on the test line. It is believed that this strategy will facilitate the development of LFAs by easily adjusting their analytical properties to the needs required by the specific application

Contamination assessment of heavy metals in the soils around Khouzestan Steel Company (KSC) (Ni, Mn, Pb, Fe, Zn, Cr)

Introduction Soil plays a vital role in human life as the very survival of mankind is tied to the preservation of soil productivity (Kabata- Pendies and Mukherjee, 2007). The purpose of this study is the assessment of heavy metal contamination (Zn, Mn, Pb, Fe, Ni, Cr) of the soil around the Khuzestan Steel Complex. Materials and methods For this purpose, 13 surface soil samples (0-10 cm) were taken. Also a control sample was taken from an area away from the steel complex. The coordinates of each point were recorded by Global Positioning System (GPS). The samples were transferred to the laboratory and then were air dried at room temperature for 72 hours. Then they were sieved through a 2mm sieve for determining physical and chemical parameters (soil texture, pH, OC), and a 63-micron sieve for measurement of heavy metal concentration. pH was measured using a calibrated pH meter at a 2: 1 mixture (soil: water), and soil texture was determined using a hydrometer. The amount of organic matter was measured using the Valkey black method (Chopin and Alloway, 2007). After preparation of the samples in the laboratory, the samples were analyzed using the ICP-OES method to assess concentration of heavy metals. Measurement of heavy metals concentration was carried out at the Zar azma laboratory in Tehran. To ensure the accuracy of the analysis of soil samples, replicate samples were also sent to the laboratory. In order to assess the heavy metal pollution in the soil samples, different indices including contamination factor (CF), contamination degree (Cd), anthropogenic enrichment percent (An%), and saturation degree of metals (SDM) were calculated. Discussion In addition, the mean concentrations of heavy metals in soil samples were compared to the concentration of these metals in Control Sample and unpolluted soil standard. Measurement of soil pH showed that the soil has a tendency to alkalinity. Also, soil texture is sandy loam (Moyes, 2011). The results showed that the mean Organic Carbon in the soil sample is 1.03%, the higher amount of OC is related to soil sample numbers 7 and 11. The mean concentrations of Ni, Pb, Zn, Mn, Fe and Cr in soil samples were 61.42, 19.90, 156.63, 443.63, 38762.63 and 127.58 (mg/kg), respectively. The highest concentrations of manganese, chromium, zinc and lead were found in soil samples number 4 and 12. This is in agreement with the results of the saturation degree of metals so that, the highest values of saturation degree of metals were found for soil samples close to the factory, (i.e. 4 and 12). The SDM values decreased with distance to the factory. The highest contamination factor was obtained for soil samples which were taken near the steel factory (4 and 12). Also, the highest contamination degrees were found for soil samples 4 (23.7) and 12 (14.1) while, the lowest values were obtained for soil samples 6 (6.35) and 10 (6.07). The results of the contamination degree calculation, anthropogenic enrichment percent, as well as statistical analysis are consistent. It can be said that the origin of iron in study areas is related to anthropogenic and geogenic activites. The results of anthropogenic enrichment percent, indicated that Lead, Manganese, and Zinc in the soil samples which were taken around the steel factory have an anthropogenic source. Moreover, the source of Chromium and Nickel is mainly geogenic. The results showed that all variables are normally distributed. Three components originate with a cumulative variance of 79.55% for soil samples. PC1 which explains 41.47% of the total variance can be defined as an anthropogenic component since Mn, Pb and Zn soil samples have the highest loading on PC1. As previously indicated, the concentration of these elements in the study area is mainly influenced by the steel industrial complex. The PC2 represents 22.26% of the total variance, and is strongly associated with Ni, Fe can be defined as geogenic and anthropogenic component, as the variability of the elements seems to be controlled by parent rocks and human activities. Cr was individually included in the PC3 whith 15.82% of the total variance. The distribution of Cr in the studied soils confirmed that it was derived from the parent materials of soil. Results The highest concentrations were found at soil samples 4 and 12. Comparison of heavy metals concentration with unpolluted soil standard indicated that, concentrations of Cr, Zn, Fe, Ni and Pb is higher than that of unpolluted soil standard. In general, Manganese, Chromium, Zinc and Lead are the most important elements that are found in emissions of steel plants. The soil samples near the steel plant and downwind direction have much higher pollution level. The results showed that Mn, Pb and Zn is related to human activity and Cr have geogenic source and Fe and Ni have both geogenic and anthropogenic source in the study area in the city of Ahwaz. Acknowledgements The authors would like to thank the vice-chancellor for research and technology of Shahid Chamran University of Ahwaz for financial support. References Chopin, E.I.B. and Alloway, B.j., 2007. Distribution and Mobility of Trace Element in Soil and Vegetation Around the Mining and Smelting Areas Of Tharsis, Riotinto and Hulelva, Iberian Pyrite Belt, SW Spain. Water Air Soil pollution, 182(1-4): 245-261. Kabata-Pendias, A. and Mukherjee, A.B., 2007. Trace Elements from soil to Human. Springer, Berlin, 550 pp. Moyes, J., 2011. The soil texture wizard: In: J. Lemon and B. Bolker (Editors), R functions for plotting, classifying, transforming and exploring soil texture data. Swedish University of Agricultural Sciences, Sweden, pp. 11-47

The use of chitin shrimp shells for the biosorption of zinc from aqueous solutions

The removal of zinc ions from aqueous solutions on the chitin extracted of shrimp shells has been studied by using batch adsorption method. Experiments were performed as a function of initial pH (3-7), initial metal concentration (50-500mg/l), and adsorbent dose (0/5-10 gr) at 25°C. .The equilibrium metal uptake was increased and percentage biosorption was decreased with an increase in the initial concentration. So, the data showed that optimum pH for efficient biosorption of zinc by chitin was 7. The pseudo first order and pseudo second order kinetic models were used to describe the kinetic data. The dynamic data fitted with the pseudo second order kinetic model for zinc. So, Experimental data obtained were tested with the adsorption models like Langmuir and Freundlich models and Biosorption isothermal data were well correspond by Freundlich model. So that chitin extracted of shrimp shells relatively high sorption capacity, when comparing with other sorbents that was evaluated as 270.270 mg/g

Influence of the nozzle type, air-assisted velocity, and wind velocity on the measurements of spray drift potential of boom sprayers

Spraying is a major operation to control the bio-elements that are harmful to farming products. Reduction in nozzle drift can be considered as one of the main factors in preventing the risk of environmental pollution because of using pesticides. For this aim, three types of air-assisted nozzles, namely, air-liquid-air(ALA), liquid-air(LA), liquid-air-liquid(LAL), and at four levels of air-assisted velocity (0, 2, 4, and 7.5 m/s-1)were used;  They were also examined at four levels of wind velocity (0, 2, 3, and 4 m/s-1).A spectrophotometry device, MATLAB, SAS 9.1, and IBM SPSS statistical software were used for measurement. The results showed that the effects of the nozzle type, air-assisted velocity, and wind velocity on the drift, deposition, unified spraying, volume median Diameters of 50% and 90%, and spraying quality indicators were significant (a<=0.01 ). Also in regression studies, among the three effective factors (nozzle type, air-assisted velocity, wind velocity), the nozzle type with the highest coefficient of 0.62, 0.74, 0.277, and 0.144, respectively, the most effective factor on the drift, the amount of the deposition and volume 50 and 90 percent media diameter were identified

Electrochromism: An emerging and promising approach in (bio)sensing technology

Electrochromism (EC) is a unique property of certain materials that undergo an electrochemical-induced change in colouration. During the last decades, electrochromic materials (ECMs) have been applied in a variety of technologies ranging from smart windows to information displays and energy storage devices. More recently, ECMs have attracted the attention of the (bio)sensing community thanks to their ability to combine the sensitivity of electrochemical methods with the intuitive readout of optical sensors. Although still a nascent technology, EC-based sensors are on the rise with several targets (e.g. cancer biomarkers, bacteria, metabolites and pesticides), which have already been detected by (bio)sensors using ECMs as transducers. In this review, we provide the reader with all the information to understand EC and its use in the development of EC-based biosensors.We acknowledge the MICROB-PREDICT project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825694. Financial support from the EU Graphene Flagship Core 2 Project (No. 785219) is also acknowledged. This article reflects only the author’s view, and the European Commission is not responsible for any use that may be made of the information it contains. ICN2 is funded by the CERCA programme, Generalitat de Catalunya. The ICN2 is supported by the Severo Ochoa Centres of Excellence programme, funded by the Spanish Research Agency (AEI, grant no. SEV-2017-0706). E.P.N. acknowledges funding through the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 754510