N-TiO₂ Photonic and Quantum Photocatalytic Efficiency Determined by Monte Carlo Simulation

Abstract

Nitrogen-modified titanium dioxide (N-TiO₂) is proposed as an alternative to improve solar light absorption in photocatalytic applications. Due to its high chemical stability and low toxicity, various synthesis methods have been developed, yielding materials with different properties. Evaluating its performance compared to other photocatalysts requires calculating the quantum efficiency, which involves appropriate mathematical models to interpret experimental data. This study used a Monte Carlo approach to determine the local volumetric rate of photon absorption (LVRPA). TiO₂ and N-TiO₂ were synthesized via the sol-gel method using urea as the nitrogen source, and commercial TiO₂ P-25 was used as a reference. Formic acid and salicylic acid were chosen as model pollutants due to their differing adsorption behavior on TiO₂. Three light sources were used: UVA, white, and blue light. Nitrogen doping increased quantum efficiency for formic acid degradation under UVA from 2.4 to 3.5 (46% increase) and salicylic acid from 1.0 to 2.1 (110% increase). P-25 showed the highest efficiencies under UVA, with 6.2 for formic acid and 5.2 for salicylic acid. Under white light, salicylic acid degradation efficiency doubled from 0.4 to 0.8 after nitrogen doping. No activity was observed for formic acid with undoped TiO₂ under white light, but N-TiO₂ achieved 1.1. Under blue light, no activity was detected for formic acid, while salicylic acid degradation showed efficiencies of 0.3 (N-TiO₂) and 0.2 (P-25). Quantum efficiency was highest under UVA, indicating that nitrogen doping improves visible light response but does not surpass UVA performance.

Keywords

N-TiO₂ / Photocatalysis / Quantum-efficiency / Photonic-efficiency / Monte Carlo simulation

Supplementary Materials

The following supporting information can be found at: https://www.sciepublish.com/article/pii/727, Figure S1: Emission spectra of the different lamps; Calculations and theory: Implementation of Monte Carlo simulations to calculate photon absorption efficiency; Figure S2: Transmittance spectrum of methacrylate cylindrical filter; Figure S3: SEM images an XRD pattern of TiO₂ P-25; Figure S4: Flowchart of the Monte Carlo algorithm used for the simulation of the reactor, where n is the number of photons extinguished and n_T is the total number of photons at the beginning of the simulation; Figure S5: Coefficients of extinction (a) and absorption (b) as a function of concentration for three different wavelengths, for the N-TiO₂ photocatalyst. Also included are linear regressions for each dataset with their respective values for the coefficient of determination R²; Figure S6: temporal evolution of salicylic and formic acid during adsorption; Figure S7: Effect of UVA, WL and BL irradiation on aqueous solutions of formic or salicylic acid; Figure S8: Absorption spectra of salicylic acid after different photocatalytic treatment times. Photocatalyst: N-TiO₂. Light source: UVA, white light (WL), filtered white light (FWL), and blue light (BL); Figure S9: Absorption spectra of salicylic acid after different photocatalytic treatment times. Photocatalyst: TiO₂. Light source: UVA, white light (WL), filtered white light (FWL), and blue light (BL); Figure S10: Absorption spectra of salicylic acid after different photocatalytic treatment times. Photocatalyst: TiO₂. Light source: UVA, white light (WL), filtered white light (FWL), and blue light (BL); Figure S11: Contaminant degradation as a function of time using the N-TiO₂ photocatalyst for formic acid and white light (a), formic acid and UVA radiation (b), salicylic acid and white light (c), salicylic acid and UVA radiation (d). Data fits are included in the figures, using the pseudo-order zero (Ord 0), pseudo-order 1 (Ord 1), and Langmuir-Hinshelwood (L-H) models; Table S1: Sum of squared residuals (SSR) of the fits of the concentration vs. time curves for the different photocatalysts. The considered models were the pseudo-order zero (Ord 0), pseudo-order 1 (Ord 1), and Langmuir-Hinshelwood (L-H) (for some example plots, see Figure S11). The fitted values of the initial rate of reaction v0 {v}_{0} v0​ for the Ord 0 model are also included.

Acknowledgments

The authors gratefully acknowledge the support given by Universidad Nacional de San Martín, Universidad Nacional del Litoral and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). OMA, PFFC and RJC are members of CONICET.

Author Contributions

Conceptualization, O.M.A., R.J.C. and P.F.F.C.; Methodology, E.B.B. and P.F.F.C.; Software, P.F.F.C.; Validation, O.M.A., R.J.C. and E.B.B.; Formal Analysis, E.B.B. and P.F.F.C.; Investigation, E.B.B., P.F.F.C. and R.J.C.; Resources, R.J.C.; Data Curation, O.M.A.; Writing—Original Draft Preparation, R.J.C. and P.F.F.C.; Writing—Review & Editing, R.J.C. and O.M.A.; Project Administration, R.J.C.; Funding Acquisition, R.J.C. and P.F.F.C.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be available under requirement.

Funding

This research was funded by Agencia Nacional de PromociónCientífica y Tecnológica, PICT 2019-3263, PICT 2014-750 and PICT 2014-2386 (Argentina).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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