Magnetic nanomaterials as an effective absorbent material for removal of fluoride concentration in water: a review.

The rapid increases in industrialization and populations are significant sources of water contamination. The speed with which contamination of groundwater and surface water occurs is becoming a serious problem and poses a significant obstacle for water stakeholders. Heavy metals, organic, and inorganic contaminants in the form of suspended and dissolved materials are just a few of the contaminants that can be found in drinking water. One of the most common contaminants in the water is fluoride, which is responsible for numerous toxic diseases. Different traditional techniques, for example, coagulation, ion exchange, absorption, and membrane filtration are being used to dispose of fluoride from water. However, nanomaterials such as magnetic nanoparticles (NPs) are very efficient, reliable, cost-effective, and stable materials to replace traditional water treatment techniques. There has been an increase in interest in the application of nanomaterials to the purification of drinking water over the past few decades. The use of magnetic NPs, such as metal and metal oxide NPs, to remove fluoride ions and organic matter from water is highlighted in this review article. Also, this section also discusses the properties, benefits and drawbacks, and difficulties of utilizing magnetic NPs in the process of purifying drinking water.

Enhanced cellulose nanofiber mechanical stability through ionic crosslinking and interpretation of adsorption data using machine learning.

Herein we report the fabrication of cationic functionalized cellulose nanofibers (c-CNF) having 0.13 mmol.g-1 ammonium content and its ionic crosslinking via the pad-batch process. The overall chemical modifications were justified through infrared spectroscopy. It is revealed that the tensile strength of ionic crosslinked c-CNF (zc-CNF) improved from 3.8 MPa to 5.4 MPa over c-CNF. The adsorption capacity of zc--CNF was found to be 158 mg.g-1 followed by the Thomas model. Further, the experimental data were used to train and test a series of machine learning (ML) models. A total of 23 various classical ML models (as a benchmark) were compared simultaneously using Pycaret which helped reduce the programming complexity. However, shallow, and deep neural networks are used that outperformed the classic machine learning models. The best classical-tuned ML model using Random Forests regression had an accuracy of 92.6 %. The deep neural network made effective by early stopping and dropout regularization techniques, with 20 × 6 (Neurons x Layers) configuration, showed an appreciable prediction accuracy of 96 %.

Adsorption of Indigo Carmine dye onto the surface-modified adsorbent prepared from municipal waste and simulation using deep neural network.

A new adsorbent was prepared from municipal wastes (a mixture of Corn Stover, Paper Waste, and Yard Waste) by cationization with 3 ̶ Chloro ̶ 2 ̶ Hydroxypropyl Trimethylammonium Chloride. The FTIR spectrum confirmed the quaternary ammonium group's presence on the adsorbent surface (1450 cm-1). The maximum adsorption capacity (148 mg/g) was higher than the earlier reported values. Liu isotherm described well the adsorption process, with a high R2adj value (0.997). The pseudo-first-order equation fits well for kinetic data, and thermodynamic experiments demonstrated the endothermic nature of the adsorption. The deep neural network (DNN) is applied to simulate the adsorption process, which outperformed the classical machine learning and shallow neural network models. The DNN model predicted accurately the adsorption process with the lowest deviation from the actual values with Mean Absolute Error (MAE = 3.2), Root Mean Squared Error (RMSE = 4.89), and the highest performance accuracy of R2 (0.96) as compared to various classical ML algorithms such as Linear Regressions (MAE = 12.53, RMSE = 18.01, R2 = 0.42), Random Forest (MAE = 5.81, RMSE = 10.05, R2 = 0.82), and Extra Trees (MAE = 4.35, RMSE = 8.22, R2 = 0.88). The utilized DNN model can be used for predicting the removal efficiency of dyes for various combinations of input parameters without going through laboratory experiments.

Insight into cellulose-based-nanomaterials - A pursuit of environmental remedies.

Nature has given several unique features to one of the most abundant and inexhaustible biopolymers on the earth, i.e., cellulose. Besides, biodegradability, and cost-effectiveness, cellulose possesses attractive properties such as the ability to undergo chemical and structural modification, plus its light weight and thermal and mechanical stability. Cellulose originates from natural sources, including being significant components of plants (ca. 33%), wood (ca. 50%), and cotton (ca. 90%). It can also be synthesized and modified further into a variety of functionalized nanomaterials for diversified sectors, such as bio-medical, food, customer care, and environmental services. Considering the significant growth in product development and interdisciplinary cellulose-based research, the proposed chapter will let the reader gain knowledge about in-vitro extraction, synthesis of nanomaterials, and applications to resolve ongoing environmental challenges.

Ionic cross-linking of cellulose nanofibers: an approach to enhance mechanical stability for dynamic adsorption.

Herein, we attempt to improve the mechanical stability of anionic functionalized cellulose nanofibers (a-CNF) having 1.25 mmol of carboxymethyl groups per gram of cellulose nanofibers (CNF). The a-CNF and cross-linked a-CNF (za-CNF) then used for water desalination in the continuous mode using a tubular adsorption column. It is worth mentioning that the za-CNF possess 40% degree of cross-linking provided better mechanical stability as the tensile strength improved from 3.2 to 5.2 MPa over a-CNF. The IR spectroscopy was used to confirm the success of chemical modifications. Upon ionic cross-linking, the BET surface area reduced from 13.53 to 7.54 m2·g-1 corresponds to a-CNF and za-CNF, respectively. Moreover, this research was extended to determine the dynamic adsorption capacities for a-CNF and za-CNF, which were found to be 21 and 10 mg·g-1 respectively at a flow rate of 5-mL·min-1 explained by Thomas model.

Removal of lead from aqueous solution using polyacrylonitrile/magnetite nanofibers.

Lead is known for its toxic and non-biodegradable behavior. The consumption of lead-contaminated water is one of the major threat the world is facing nowadays. In this study, polyacrylonitrile (PAN) and magnetite (Fe3O4) composite nanofiber adsorbent was developed for Pb2+ removal in batch mode. The synthesis was done by a simple and scalable process of electrospinning followed by chemical precipitation of Fe3O4. The nanofibers thus obtained were characterized through FTIR, zeta potential analyzer, and scanning electron microscope (SEM) and were analyzed for their adsorption capability for Pb2+ ions. The amount of metal ion adsorbed was influenced by the initial metal ion concentration, the time the adsorbent was in contact, the amount of nanofiber, and the pH of the solution. The experimental data fitted well with pseudo 2nd-order and Langmuir adsorption isotherm model. The nanofibers showed high adsorption capability and could be recommended for Pb2+ removal successfully.