Global NEST Journal

In this study, a batch adsorption process was conducted utilizing Ascophyllum Nodosum biosorbent as an organic adsorbent used for the elimination of heavy metal ions (specifically chromium, nickel, and zinc) from wastewater. To examine the surface morphology and functional groups, the biosorbent underwent analysis using FTIR, SEM & EDX technologies. To understand the nature of the adsorption process, several isothermal studies were conducted. These studies aimed to determine whether the adsorption process was homogeneous or heterogeneous in nature. Furthermore, the physical or chemical nature of the metal ion adsorption on the adsorbent was investigated through kinetic studies. Thermodynamic investigations were also carried out to assess whether the adsorption of metal ions was exothermic or endothermic, as well as to conclude the spontaneity of the process. Notably, the findings indicated that the inclusion of 0.3N HCl resulted in the highest rate of desorption, indicating its effectiveness in releasing the adsorbed metal ions. The AN – Brown Algae adsorbs targeted metal ions (Cr(VI), Ni(II), Zn(II)) at percentage of 93.55%, 87.56% and 83.27% respectively from the synthetic solution under the optimum conditions in batch study.

Recently, the environmental pollution is becoming a challenging issue due to the lacking of awareness about the importance of the environment and the growth of transportation facility and the various factories. The Internet of Things (IoT) technology is useful today for collecting and updating the data dynamically from anywhere anytime in less expense. Air quality of different cities is predicted by considering and maintaining the above listed technique which is useful for identifying and predicting the air quality and measure the air pollution level as well. This paper proposes a new IoT aware air quality prediction system that incorporates the newly proposed classifier with fuzzy temporal correlation and the auto-encoded Bidirectional Long Short-Term Memory (Bi-LSTM) to predict the diseases such as heart, cancer and diabetes. Moreover, we propose a new optimization algorithm called Spotted Hyena and Personalized best Cuckoo Search Optimization Algorithm (SHPCSA) for selecting and optimizing the most contributed features that are helpful for enhancing the prediction accuracy with short span of time. The air quality data is collected from various cities like Chennai, Bong Kong, etc. to conduct experiments. The proposed model is proved better than standard classifier with respect to precision, recall, F1-score and accuracy by conducting experiments

In the present study, nanocellulose was successfully extracted from durian husk via chemical treatment and then characterized by several analytical techniques, involving X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDX) and transmission electron microscopy (TEM). Nanocellulose was obtained with a crystallinity index of 77% and in the form of cellulose nanofibrils (30 nm in width and micrometers in length). Using nanocellulose as a biosorbent in solid-phase extraction technique, the inorganic arsenic ions were eluted using 0.1 M HNO3 and its content in the eluate solution was quantified by hydride generation-flame atomic absorption spectrometry (HG-FAAS), in which the recovery of arsenic ions using nanocellulose were assessed via contact time, pH and dosage of sorbent, reaching the equilibrium after 30 minutes. With the limit of detection (LOD) of 0.41 ng mL-1, the limit of quantification (LOQ) of 1.38 ng mL-1 and the relative standard deviation of repeatability (%RSD) of 3.44% (with ten replicates) for arsenic determination, the present method was practiced with the trace inorganic arsenic quantification in real samples (fish sauce with various protein levels) with reliable spike recoveries.

Chromium (VI), a ubiquitous toxin, has been associated with several human cancer types as well as immunologic, cardiovascular, developmental, neurological, and endocrine disorders. The present study investigated the selective adsorption capacity and chromium (VI) removal ability of jackfruit leaf powder (JLP, Artocarpus heterophyllus Lam.) in landfill leachate collected from eastern region of southern India. The efficiency of prepared JLP in removing chromium (VI) from landfill leachate was examined using a number of variables such as adsorbent dose, pH, and reaction time by employing the batch adsorption process. The maximum chromium removal efficiency was observed by increasing the adsorbent dose, pH, and reaction time, while the optimal dose of JLP, pH, and adsorption reaction time were found to be 0.5 g/L, pH 8, and 120 min, respectively. Batch adsorption process under optimal conditions showed adsorption capacity value of 0.19 mg/g and the chromium (VI) removal efficiency of 95%. The data were examined using kinetic and equilibrium models. The experimental data and the Freundlich isotherm and pseudo-second order kinetics models were well-matched. The SEM and FT-IR of fresh and recovered JLP revealed similar surface morphology and functional characteristics, representing the active site present in the adsorbent (JLP) showing no significant change. We performed experiments on chromium (VI) recovery from the adsorbent that resulted in the higher recovery % of chromium (VI) with 0.5 M HCl (90%). The results suggest that JLP may be employed as a cost-effective bio-adsorbent for the removal of chromium (VI) from contaminated soil and water resources.