The adsorptive properties of UiO-66 towards organic dyes: A record adsorption capacity for the anionic dye Alizarin Red S.

In order to decisively determine the adsorption selectivity of zirconium MOF(UiO-66) towards anionic versus cationic species, the adsorptive removal of the anionic dyes(Alizarin Red S.(ARS), Eosin(E), Fuchsin Acid(FA)and Methyl Orange(MO)) and the cationic dyes(Neutral Red(NR), Fuchsin Basic(FB), Methylene Blue(MB),and Safranine T(ST)) has been evaluated. The results clearly reveal a significant selectivity towards anionic dyes. Such an observation agrees with a plethora of reports of UiO-66 superior affinity towards other anionic species(Floride, PO_4^(3-), Diclofenac sodium, Methylchlorophenoxy-propionic acid, Phenols, CrO_4^(2-), SeO_3^(2-), and AsO_4^-). The adsorption process of ARS as an example has been optimized using the central composite design(CCD). The resultant statistical model indicates a crucial effect of both pH and sorbent mass. The optimum conditions were determined to be initial dye concentration 11.82 mg.L^(-1), adsorbent amount 0.0248 g, shaking time of 36 min and pH 2. The adsorption process proceeds via pseudo-second order kinetics(R^2= 0.999). The equilibrium data were fit to Langmuir and Tempkin models(R^2= 0.999 and 0.997 respectively). The results reveal an exceptional removal for the anionic dye(Alizarin Red S.) with a record adsorption capacity of400 mg·g^(-1). The significantly high adsorption capacity of UiO-66 towards ARS adds further evidence to the recently reported exceptional performance of MOFs in pollutants removal from water.

Ethanol mediated As(Ⅲ) adsorption onto Zn-loaded pinecone biochar：Experimental investigation,modeling,and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters（OMs） and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol（EtO H）-mediated As（Ⅲ） adsorption onto Zn-loaded pinecone（PC） biochar through batch experiments conducted under Box–Behnken design. The effect of EtO H on As（Ⅲ） adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtO H and pH on As（Ⅲ） adsorption,whereas neural network revealed the stronger influence of Et OH（64.5%） followed by pH（20.75%）and As（Ⅲ） concentration（14.75%） on the adsorption phenomena. Besides, the interaction among process variables indicated that Et OH enhances As（Ⅲ） adsorption over a pH range of2 to 7, possibly due to facilitation of ligand–metal（Zn） binding complexation mechanism.Eventually, hybrid response surface model–genetic algorithm（RSM–GA） approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As（Ⅲ）（10.47 μg/g） is facilitated at 30.22 mg C/L of Et OH with initial As（Ⅲ） concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As（Ⅲ） species in the presence of OM.