Improving water desalination performance of electrospun carbon nanofibers by supporting with binary metallic carbide nanoparticles
Source abstract
Abstract
Abstract Capacitive deionization (CDI) technology is proposed as an environmentally friendly way to desalinate brackish water samples with outstanding efficiency. Since the nanomaterial composition is the controlling factor in the measured activity of fabricated CDI cells, much efforts are directed to explore highly effective nanocomposites with increased electrosorptive capacity and enhanced regeneration behavior during operation for longer periods. Here, the electrospinning process was devoted to synthesize mixed cobalt and titanium carbides nanoparticles onto carbon nanofibers [Co–TiC@CNFs] using cobalt acetate tetrahydrate (CoAc) and titanium (IV) isopropoxide (TIP) as precursor salts and polyvinylpyrrolidone (PVP) as a carbon source. Electrospun mats were then calcined at 950 °C for 6 h using a tube furnace with passing argon gas. This formed nanoporous material could provide numerous pathways for increased ions adsorption with extraordinary electrical conductivity. This could explain its enhanced electrochemical properties as evidenced by cyclic voltammetry, chronoamperometry and electrochemical impedance spectroscopy (EIS) methods. A specific capacitance value of 979.40 F g−1 was estimated for Co–TiC@CNFs as 7.42 times higher than that for bare CNFs. Furthermore, this nanohybrid material had a salt adsorption capacity of 33.10 mg g−1 in 1.0 M NaCl solution at 1.2 V that highly exceeded the obtained ones for TiC [2.20 mg g−1] and CNFs [8.20 mg g−1]. Accordingly, modifying carbon nanofibers with metallic carbides can create suitable nano-constituents for promising CDI cell output.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Membrane-based Ion Separation Techniques · Membrane Separation Technologies · Advanced battery technologies research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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