Non-magnetic and magnetic low-cost biochar (BC) from date pits (DP) were applied to remove tigecycline (TIGC) from TIGC-artificially contaminated water samples. Pristine biochar from DP (BCDP) and magnetite-decorated biochar (MBC-DP) were therefore prepared. Morphologies and surface chemistries of BCDP and MBC-DP were explored using FT-IR, Raman, SEM, EDX, TEM, and BET analyses. The obtained IR and Raman spectra confirmed the presence of magnetite on the surface of the MBC-DP. SEM results showed mesoporous surface for both adsorbents. BET analysis indicated higher amount of mesopores in MBC-DP. Box–Behnken (BB) design was utilized to optimize the treatment variables (pH, dose of the adsorbent (AD), concentration of TIGC [TIGC], and the contact time (CT)) and maximize the adsorptive power of both adsorbents. Higher % removal (%R), hitting 99.91%, was observed using MBC-DP compared to BCDP (77.31%). Maximum removal of TIGC (99.91%) was obtained using 120 mg/15 mL of MBC-DP for 10 min at pH 10. Equilibrium studies showed that Langmuir and Freundlich isotherms could best describe the adsorption of TIGC onto BCDP and MBC-DP, respectively, with a maximum adsorption capacity (q_max) of 57.14 mg/g using MBC-DP. Kinetics investigation showed that adsorption of TIGC onto both adsorbents could be best-fitted to a pseudo-second-order (PSO) model.

Besides the high-current density, lower onset potential of urea electrooxidation is key parameter which influences the direct urea fuel cell performance. In the present article, low-onset potential has been reported for nickel-free (NF) electrocatalyst in urea electrooxidation. The nickel-free electrocatalyst: Co₃O₄–SrCO₃ heterostructure nanorods-incorporated carbon nanofibers (CNFs) were synthesized by electrospinning technique, followed by calcination of electrospun mat composed of strontium acetate, cobalt acetate, and poly(vinyl alcohol) sol–gel in inert environment at 750 °C. Physiochemical characterizations confirmed the formation of Co₃O₄–SrCO₃ heterostructure nanorods-incorporated CNFs. The electrochemical activity of resultant nickel-free electrocatalyst toward the electrooxidation of urea in alkaline medium is evaluated using cyclic voltammetry measurements (CV). Co₃O₄–SrCO₃ heterostructure nanorods-incorporated CNFs reveals high-current density of 21.33 mA/cm² at low-fuel concentration. Notably, the low-onset potential has been observed, showing a good application prospect in direct urea fuel cells.

NiFe nanoparticles-decorated & N-doped graphene is introduced as an effective and stable non-precious electrocatalyst for ORR in the acid medium. Compared to conventional Pt/C electrodes under the same conditions, the proposed nanocatalyst shows closer onset potential and current density. Typically, the observed onset potentials and current densities for the synthesized and Pt/C electrodes are 825 and 910 mV (vs. NHE) and −3.65 and −4.31 mA.cm⁻² (at 5 mV.s⁻¹), respectively. However, the most important advantage of the introduced metallic alloy-decorated graphene is its distinct stability in acid medium; the retention in the electrocatalytic performance after 1,000 successive cycles is approximately 98%. This finding is attributed to the high corrosion resistance of the NiFe alloy. The kinetic study indicates that the number of the transferred electrons is 3.46 and 3.89 for the introduced and Pt/C (20 wt%) electrodes, respectively which concludes a high activity for the proposed nanocomposite. The suggested decorated graphene can be synthesized using a multi-thermal method. Typically, nickel acetate, iron acetate, graphene oxide and urea are subjected to MW heating. Then, sintering with melamine in an Argon atmosphere at 750 °C is required to produce the final electrocatalyst. Overall, the introduced NiFe@ N-doped Gr nanocomposite shows remarkable electrochemical activity in the acid medium with long-term stability.

The internal resistances of fuel cells strongly affect the generated power. Basically, in the fuel cell, the anode can be prepared by deposition of a film from the functional electrocatalyst on a proper gas diffusion layer. Accordingly, an interfacial resistance for the electron transport is created between the two layers. Electrocatalyst-functionalized gas diffusion layer (GDL) can distinctly reduce the interfacial resistance between the catalyst layer and the GDL. In this study, NiMn nanoparticles-decorated carbon felt is introduced as functionalized GDL to be exploited as a ready-made anode in a direct urea fuel cell. The proposed treated GDL was prepared by calcination of nickel acetate/manganese acetate-loaded carbon felt under an argon atmosphere at 850 °C. The physiochemical characterizations confirmed complete reduction for the utilized precursors and deposition of pristine NiMn nanoparticles on the carbon felt fiber. In passive direct urea fuel cells, investigation the performance of the functionalized GDLs indicated that the composition of the metal nanoparticles has to be optimized as the GDL obtained from 40 wt % manganese acetate reveals the maximum generated power density; 36 mW/m² at room temperature and 0.5 M urea solution. Moreover, the electrochemical measurements proved that low urea solution concentration is preferred as utilizing 0.5 M solution resulted into generating higher power compared to 1.0 and 2.0 M solution. Overall, this study opens a new avenue toward functionalization of the GDL as a novel strategy to overcome the interfacial resistance between the electrocatalyst and the GDL.

In the current study, eco-structured and efficient removal of the veterinary fluoroquinolone antibiotic sarafloxacin (SARA) from wastewater has been explored. The adsorptive power of four agro-wastes (AWs) derived from pistachio nutshells (PNS) and Aloe vera leaves (AV) as well as the multi-walled carbon nanotubes (MWCNTs) has been assessed. Adsorbent derived from raw pistachio nutshells (RPNS) was the most efficient among the four tested AWs (%removal ‘%R’ = 82.39%), while MWCNTs showed the best adsorptive power amongst the five adsorbents (%R = 96.20%). Plackett-Burman design (PBD) was used to optimize the adsorption process. Two responses (‘%R’ and adsorption capacity ‘q_e’) were optimized as a function of four variables (pH, adsorbent dose ‘AD’ (dose of RPNS and MWCNTs), adsorbate concentration [SARA] and contact time ‘CT’). The effect of pH was similar for both RPNS and MWCNTs. Morphological and textural characterization of the tested adsorbents was carried out using FT-IR spectroscopy, SEM and BET analyses. Conversion of waste-derived materials into carbonaceous material was investigated by Raman spectroscopy. Equilibrium studies showed that Freundlich isotherm is the most suitable isotherm to describe the adsorption of SARA onto RPNS. Kinetics’ investigation shows that the adsorption of SARA onto RPNS follows a pseudo-second order (PSO) model.

The widespread use of antimicrobials within either a therapeutic or a veterinary rehearsal has resulted in a crisis on the long run. New strains of antimicrobial-resistant microorganisms have appeared. Contamination of water with pharmaceutically active materials is becoming a fact! and efficacy of wastewater treatment plants is a question. Adsorption is a promising technique for wastewater treatment. Carbon-based materials are among the most commonly used adsorbents for remediation purposes. Food production and commercialization are posing rigorous regulations. In this concern, almost all authoritarian societies are setting up standards for the maximum residue levels permissible in raw and processed food. Among these products is infant foods. The current trend is to use carbon-based and recycled from agricultural wastes, which can selectively remove target antimicrobials. Nanoparticles are among the most commonly used materials. With the enormous amount of data generated from an analytical process, there is a need for a powerful data processing technique. Factorial designs play an important role in not only minimalizing the number of experimental runs, and hence saving chemicals, resources, and reducing waste but also, they serve to improve the sensitivity and selectivity, the most important analytical outcomes.