Efficient Bi-TiO2 photocatalytic materials for asphalt applications: Synthesis, characterization, and NO degradation performance

Rapid urbanization and increasing vehicular emissions have intensified nitrogen oxide (NO) pollution, posing serious environmental and health risks. Photocatalytic materials, such as TiO<inf>2</inf>, offer a sustainable solution for pollutant degradation; however, their practical efficiency is constrained by a wide band gap (∼3.2 eV) and high electron-hole recombination rates, limiting visible-light activity. This study addresses these challenges by synthesizing Bi-doped TiO<inf>2</inf> nanoparticles via sol-gel and hydrothermal methods, incorporating them into asphalt, and evaluating their photocatalytic performance in degrading both gaseous (nitric oxide, NO) and aqueous (methyl orange, MO) pollutants under UV and visible light. Comprehensive material characterization (XRD, SEM, TEM, XPS, UV–vis DRS, VB-XPS, BET) revealed that hydrothermally synthesized TiO<inf>2</inf> exhibited nearly twice the BET surface area (∼194 vs. 107 m²/g), larger crystallite size (∼14 vs. 10 nm), and smaller particle size (∼20–30 nm vs. 25–45 nm) compared to sol-gel TiO<inf>2</inf>, leading to enhanced photocatalytic activity. Optimal Bi doping (3–4 %) effectively reduced the TiO<inf>2</inf> band gap from ∼2.8–2.85 eV to ∼2.3–2.5 eV, improving visible-light absorption. Density Functional Theory (DFT) calculations confirmed that Bi doping introduced impurity states that facilitated band gap narrowing, while excessive oxygen vacancies diminished visible-light activity by suppressing these states. Photocatalytic evaluations demonstrated that Bi-doped TiO<inf>2</inf> achieved up to 77.6 % under visible light, while also significantly enhancing MO degradation efficiency compared to undoped TiO<inf>2</inf>. These findings highlight the potential of Bi-doped TiO<inf>2</inf>-modified asphalt as a multifunctional material for sustainable urban air pollution mitigation with enhanced photocatalytic stability and efficiency under real-world conditions.