This paper reports the development of a method to control the flow rate of fluids within paper-based microfluidic analytical devices. We demonstrate that by simply sandwiching paper channels between two flexible films, it is possible to accelerate the flow of water through paper by over 10-fold. The dynamics of this process are such that the height of the liquid is dependent on time to the power of 1/3. This dependence was validated using three different flexible films (with markedly different contact angles) and three different fluids (water and two silicon oils with different viscosities). These covered channels provide a low-cost method for controlling the flow rate of fluid in paper channels, and can be added following printing of reagents to control fluid flow in selected fluidic channels. Using this method, we redesigned a previously published bidirectional lateral flow pesticide sensor to allow more rapid detection of pesticides while eliminating the need to run the assay in two stages. The sensor is fabricated with sol–gel entrapped reagents (indoxyl acetate in a substrate zone and acetylcholinesterase, AChE, in a sensing zone) present in an uncovered “slow” flow channel, with a second, covered “fast” channel used to transport pesticide samples to the sensing region through a simple paper-flap valve. In this manner, pesticides reach the sensing region first to allow preincubation, followed by delivery of the substrate to generate a colorimetric signal. This format results in a uni-directional device that detects the presence of pesticides two times faster than the original bidirectional sensors.

This study investigates the use of microalgae as a biosorbent to eliminate heavy metals ions from wastewater. TheChlorella kesslerimicroalgae species was employed to biosorb heavy metals from synthetic wastewater specimens. FTIR, and SEM/XRD analyses were utilized to characterize the microalgal biomass (the adsorbent). The experiments were conducted with several process parameters, including initial solution pH, temperature, and microalgae biomass dose. In order to secure the best experimental conditions, the optimum parameters were estimated using an integrated response surface methodology (RSM), desirability function (DF), and crow search algorithm (CSA) modeling approach. A maximum lead(II) removal efficiency of 99.54% was identified by the RSM–DF platform with the following optimal set of parameters: pH of 6.34, temperature of 27.71 °C, and biomass dosage of 1.5 g L⁻¹. The hybrid RSM–CSA approach provided a globally optimal solution that was similar to the results obtained by the RSM–DF approach. The consistency of the model-predicted optimum conditions was confirmed by conducting experiments under those conditions. It was found that the experimental removal efficiency (97.1%) under optimum conditions was very close (less than a 5% error) to the model-predicted value. The lead(II) biosorption process was better demonstrated by the pseudo-second order kinetic model. Finally, simultaneous removal of metals from wastewater samples containing a mixture of multiple heavy metals was investigated. The removal efficiency of each heavy metal was found to be in the following order: Pb(II) > Co(II) > Cu(II) > Cd(II) > Cr(II).