Nano-fibrillated cellulose (NFC) was extracted by a chemical method involving alkali and acid hydrolysis. The characterisation of the citrus sinensis fruit peel bran and nano-fibrillated cellulose was performed by XRD, FTIR, TEM, and FESEM. XRD confirmed the phase of NFC which showed monoclinic crystal with spherical to rod shape morphology with a size of 44–50 nm. The crystallinity index of treated NFC increased from 39% to 75%. FTIR showed the removal of lignin and hemicellulose from waste peels due to the alkaline treatment. Silver nanoparticles were also synthesised by utilizing extract of citrus sinensis skins as a reducing agent. Pharmaceutical effluent samples from an industrial area were tested by Atomic Absorption Spectrometry. Out of the four metals obtained, cadmium and chromium were remediated by silver nanoparticles with nano-fibrillated cellulose via simulated method in 100 mg/L metal-salt concentrations over a time period of 160 min. The highest removal efficiency was found for cadmium, i.e., 83%, by using silver and NFC together as adsorbents. The second highest was for chromium, i.e., 47%, but by using only NFC. The Langmuir and Freundlich isotherms were well fitted for the sorption of Cd (II) and Cr (II) with suitable high R² values during kinetic simulation. Thus, the isolation of NFC and synthesis of silver nanoparticles proved efficient for heavy metal sorption by the reuse of waste skins.

Coal fly ash is found to be one of the key pollutants worldwide due to its toxic heavy metal content. However, due to advancements in technology, coal fly ash has gained importance in various emerging fields. They are rich sources of carbonaceous particles which remain unburnt during burning of various coals in thermal power plants (TPPs). Various carbonaceous nanoparticles in the form of fullerenes, soot, and carbon nanotubes could be recovered from coal fly ash by applying trending techniques. Moreover, coal fly ash is comprised of rich sources of organic carbons such as polycyclic and polyaromatic hydrocarbons that are used in various industries for the development of carbon-derived value-added materials and nanocomposites. Here, we focus on all the types of carbon nanominerals from coal fly ash with the latest techniques applied. Moreover, we also emphasize the recovery of organic carbons in polyaromatic (PAHs) and polycyclic hydrocarbons (PCHs) from coal fly ash (CFA). Finally, we try to elucidate the latest applications of such carbon particle in the industry.

In this study, polyacrylonitrile (PAN_P) nanofibers (NFs) were fabricated by electrospinning. The PAN_P NFs membrane was functionalized with diethylenetriamine to prepare a functionalized polyacrylonitrile (PAN_F) NFs membrane. TiO₂ nanoparticles (NPs) synthesized in the laboratory were anchored to the surface of the PAN_F NFs membrane by electrospray to prepare a TiO₂ NPs coated NFs membrane (PAN_Coa). A second TiO₂/PAN_P composite membrane (PAN_Co) was prepared by embedding TiO₂ NPs into the PAN_P NFs by electrospinning. The membranes were characterized by microscopic, spectroscopic and X-ray techniques. Scanning electron micrographs (SEM) revealed smooth morphologies for PAN_P and PAN_F NFs membranes and a dense cloud of TiO₂ NPs on the surface of PAN_Coa NFs membrane. The attenuated total reflectance in the infrared (ATR-IR) proved the addition of the new amine functionality to the chemical structure of PAN. Transmission electron microscope images (TEM) revealed spherical TiO₂ NPs with sizes between 18 and 32 nm. X-ray powder diffraction (XRD) patterns and energy dispersive X-ray spectroscopy (EDX) confirmed the existence of the anatase phase of TiO₂. Surface profilometry da-ta showed increased surface roughness for the PAN_F and PAN_Coa NFs membranes. The adsorption-desorption isotherms and hysteresis loops for all NFs membranes followed the IV -isotherm and the H3 -hysteresis loop, corresponding to mesoporous and slit pores, respectively. The photocatalytic activities of PAN_Coa and PAN_Co NFs membranes against methyl orange dye degradation were evaluated and compared with those of bare TiO₂ NPs.The higher photocatalytic activity of PAN_Coa membrane (92%, 20 ppm) compared to (PAN_Co) NFs membrane (41.64%, 20 ppm) and bare TiO₂ (49.60%, 20 ppm) was attributed to the synergy between adsorption, lower band gap, high surface roughness and surface area.

This work focuses on developing a novel method to optimize the fabrication conditions of polyamide (PA) thin film composite (TFC) membranes using the multi-objective genetic algorithm II (MOGA-II) method. We used different fabrication conditions for formation of polyamide layer—trimesoyl chloride (TMC) concentration, reaction time (t), and curing temperature (Tc)—at different levels, and designed the experiment using the factorial design method. Three functions (polynomial, neural network, and radial basis) were used to generate the response surface model (RSM). The results showed that the radial basis predicted good results (R² = 1) and was selected to generate the RSM that was used as the solver for MOGA-II. The experimental results indicate that TMC concentration and t have the highest influence on water flux, while NaCl rejection is mainly affected by the TMC concentration, t, and Tc. Moreover, the TMC concentration controls the density of the PA, whereas t confers the PA layer thickness. In the optimization run, MOGA-II was used to determine optimal parametric conditions for maximizing water flux and NaCl rejection with constraints on the maximum acceptable levels of Na₂SO₄, MgSO₄, and MgCl₂ rejections. The optimized solutions were obtained for longer t, higher Tc, and different TMC concentration levels.

We fabricated a nanofiltration membrane consisting of a polyaniline (PANI) film on a polyphenylsulfone (PPSU) substrate membrane. The PANI film acted as a potent separation enhancer and antimicrobial coating. The membrane was analyzed via scanning electron microscopy and atomic force microscopy to examine its morphology, topography, contact angle, and zeta potential. We aimed to investigate the impact of the PANI film on the surface properties of the membrane. Membrane performance was then evaluated in terms of water permeation and rejection of methylene blue (MB), an organic dye. Coating the PPSU membrane with a PANI film imparted significant advantages, including finely tuned nanometer-scale membrane pores and tailored surface properties, including increased hydrophilicity and zeta potential. The PANI film also significantly enhanced separation of the MB dye. The PANI-coated membrane rejected over 90% of MB with little compromise in membrane permeability. The PANI film also enhanced the antimicrobial activity of the membrane. The bacteriostasis (B_R) values of PANI-coated PPSU membranes after six and sixteen hours of incubation with Escherichia coli were 63.5% and 95.2%, respectively. The B_R values of PANI-coated PPSU membranes after six and sixteen hours of incubation with Staphylococcus aureus were 70.6% and 88.0%, respectively.

For proton exchange membrane fuel cells (PEMFCs), the parameter extraction issue is among the most widely studied problems in the field of energy storage systems, since the precise identification of such parameters plays an important role in increasing the PEMFC performance and life span. The optimization process is intended to adjust the performance of PEMFCs by appraising the optimal parameters that produce a good estimation of the current–voltage (I–V) curve. In order to build an accurate equivalent circuit model for PEMFCs, a reliable and effective parameter extraction algorithm, termed a supply–demand-based optimization (SDO) algorithm, is proposed in this paper. Nine parameters (${\xi }_1, {\xi }_2, {\xi }_3, {\xi }_4, R_c, \beta , \lambda , l, and J_{max}$) are evaluated, to minimize the sum squared deviation (SSE) between the experimental and simulated I–V curves. To validate the feasibility and effectiveness of the SDO algorithm, four sets of experimental data with diverse characteristics and two well-known PEMFC stacks (BSC500W and 500W Horizon) are employed. Comparison of the simulated and experimental results clearly demonstrates the superiority/competitiveness of the SDO algorithm over five well-established parameter extraction algorithms, i.e., the whale optimization algorithm (WOA), grey wolf optimization (GWO), Harris hawks optimization (HHO), and genetic algorithm (GA). Several evaluation criteria, including best SSE, worst SSE, mean SSE, and standard deviation, show that the SDO algorithm has merits in terms of PEMFC modeling.