Effectiveness of a new green technology for metal removal from contaminated water

Design and implementation of humidity sensor based on carbon nitride modified with graphene quantum dots

Relative humidity (RH) is one of the most important factors that deserve intensive study because of its impact on many aspects of life. In this work humidity sensor based on carbon nitride / graphene quantum dots (g-C3N4/GQDs) nanocomposites have been developed. The structure, morphology and composition properties of the g-C3N4/GQDs were investigated and analyzed by XRD, HR-TEM, FTIR, UV-Vis, Raman, XPS and BET surface area. The average particle size of GQDs was estimated from XRD to be 5 nm and confirmed using HRTEM. The HRTEM images prove that the GQDs are attached to the external surface of the g-C3N4. The measured BET surface area was found to be 216 m²/g, 313 m²/g, and 545 m²/g for GQDs, g-C3N4, and g-C3N4/GQDs respectively. The d-spacing and crystallite size were estimated from XRD and HRTEM and found in a good matching. The humidity sensing behavior of g-C3N4/GQDs was measured in a wide span of humidity from 7% up to 97% RH under different testing frequencies. The obtained results demonstrate good reversibility and fast response/recovery time. The implemented sensor exhibits a great application prospect in humidity alarm devices, automatic diaper alarms, and breath analysis, which have advantages such as strong anti-interference capability, low cost, and easy to use.

Ecofriendly remediation technologies for wastewater contaminated with heavy metals with special focus on using water hyacinth and black tea wastes: a review

Treatment of water contaminated with heavy metals is challenging. Heavy metals are non-degradable, persistent in the environment, have a high dispersion capacity by water, can bioaccumulate, and represent risks to human and environmental health. Conventional treatment methods have disadvantages; however, adsorption in biomass is a highly promising method with high efficiency and low cost that avoids many of the disadvantages of conventional methods. Black tea (BT) wastes and water hyacinth (WH) have attracted attention for their ability to remove heavy metals from wastewater. Utilizing these approaches can remove contaminants and effectively manage problematic invasive species and wastes. The conventional uses of BT and WH were efficient for removing heavy metals from wastewater. Due to the unique and distinct properties and advantages of biochar and nano-forms of biosorbents, the use of BT and WH in these forms is promising to achieve sustainable heavy metals removal from wastewater. However, more study is needed to confirm preliminary results.

The influence of the correlation-covariance structure of measurement errors over uncertainties propagation in online monitoring: application to environmental indicators in SUDS

This paper presents a methodology to assess the influence of the correlation-covariance structure of measurement errors in online monitoring over the propagation of uncertainties, applied to wet-weather environmental indicators in sustainable urban drainage systems (SUDSs). The effect of auto-correlated and heteroskedastic errors in measured time-series over the estimated probability density function (PDF) of different environmental indicators is analyzed for a wide variety of possible error structures in the data. For this purpose, multiple correlation-covariance structures are randomly generated from exploring the parametric space of a linear exponent autoregressive (LEAR) model, employing a Bayesian-based Markov Chain Monte Carlo sampling technique. Significant differences tests are proposed to identify the most correlated parameters of the correlation-covariance error model with statistics of the environmental indicator PDFs. The method is applied to total suspended solids (TSS) and chemical oxygen demand (COD) time-series recorded during 13 rainfall events at the inlet and outlet of a SUDS train (stormwater settling tank-horizontal constructed wetland). In this case, results showed that the total error in the estimation of the analyzed environmental indicators is mostly explained by standard uncertainties (flattening of the PDFs) rather than bias contributions (displacement of the PDFs). The correlation-covariance model parameters related to the temporal delimitation of hydrographs/pollutographs and the intensity of the autocorrelation showed to have the strongest influence in the propagation of measurement errors (flattening/displacement of the PDFs).

Molecular modeling analyses for graphene functionalized with Fe3O4 and NiO

Graphene has attracted great concern in recent years as one of the potential 2D materials in various applications. This work is devoted for assessing the feasibility of functionalizing 2D graphene sheets with ferromagnetic and antiferromagnetic metal oxides namely magnetite (Fe₃O₄) and nickel oxide (NiO). Molecular models of the proposed candidates are exposed to energy calculations at DFT level, in addition to geometry optimization processes at PM6 method. HOMO/LUMO orbitals, MESP maps and QSAR descriptors are calculated. Results ensure that graphene doped with NiO has the highest reactivity since it possesses the largest TDM and the smallest HOMO/LUMO band gap. MESP maps illustrate that the benzene rings of graphene are most probable to undergo nucleophilic interactions. Addition of Fe₃O₄ creates new negatively charged active sites that are ready for nucleophilic interactions. The calculated QSAR parameters demonstrate a hydrophobic nature for pure and modified graphene suggesting that they need further modification with further groups for usage in biological applications.