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Bridging the Gap in Understanding the Mechanism of NH3 Formation During NO Reduction by CO in the Presence of H2O Over Rh/Al2O3 Catalysts: DFT Study
(2026-02-01)
Wangphon, Chanthip
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Khajondetchairit, Patcharaporn
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Rittiruam, Meena
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Ektarawong, Annop
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Alling, Björn
Ammonia (NH<inf>3</inf>) formation from nitric oxide (NO) and carbon monoxide (CO) in the presence of water is a promising pathway for low-temperature NO<inf>x</inf> reduction. In this work, density functional theory (DFT) calculations were used to investigate the mechanism of NH<inf>3</inf> formation on a Rh(111)/γ-Al<inf>2</inf>O<inf>3</inf>(110), in which the Rh(111) and γ-Al<inf>2</inf>O<inf>3</inf>(110) were considered separately. NO readily dissociates on the Rh site, forming N*, which reacts with CO to produce the key intermediate of NCO*. Meanwhile, water dissociates more easily on γ-Al<inf>2</inf>O<inf>3</inf>(110), providing H* species. The hydrolysis of NCO* can proceed via two pathways: on Rh(111), it forms HNCO, while on γ-Al<inf>2</inf>O<inf>3</inf>(110), it forms HNCOH*. Both intermediates subsequently decompose into NH* species, which are then hydrogenated to produce NH<inf>3</inf>. The Rh pathway is more favorable in both kinetics and thermodynamics, while the support mainly supplies hydrogen through water activation. These results suggest a dual-site mechanism in which Rh drives the main transformation, and γ-Al<inf>2</inf>O<inf>3</inf>(110) assists via hydrogen transfer. This work presents the individual roles of Rh and Al<inf>2</inf>O<inf>3</inf> surfaces in NO reduction with CO, producing NH<inf>3</inf>, which is relevant to catalytic behavior under humid conditions.
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Theoretical investigation on the role of external oxygen facilitating oxidative dehydrogenation of hydrogen sulfide on Fe-based oxide catalysts
(2025-11-15)
Saelee, Tinnakorn
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Setasuban, Sorawee
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Noppakhun, Jakapob
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Rittiruam, Meena
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Khajondetchairit, Patcharaporn
The oxidative dehydrogenation (ODH) of hydrogen sulfide (H<inf>2</inf>S) is a critical process in biogas purification, enabling cleaner and more efficient utilization of renewable energy sources. This study employs density functional theory (DFT) and microkinetic modeling to investigate the role of external oxygen (O<inf>ext</inf>) in enhancing the catalytic performance of O<inf>2</inf>/α-Fe<inf>2</inf>O<inf>3</inf>(0 0 0 1) surfaces. The findings reveal that O<inf>ext</inf> significantly lowers the activation energy of key reaction steps, promotes more favorable reaction pathways, and mitigates sulfur poisoning by stabilizing lattice oxygen and suppressing the formation of oxygen vacancies. The unique electronic and structural properties of the O<inf>2</inf>/α-Fe<inf>2</inf>O<inf>3</inf>(0 0 0 1) surface facilitate improved catalyst activity and extended operational stability, addressing key challenges in sustainable energy technologies. The existence of the O<inf>ext</inf> can decrease the optimal temperature of ODH of H<inf>2</inf>S from >1150 K on the pristine α-Fe<inf>2</inf>O<inf>3</inf>(0 0 0 1) surface to 850 K on the O<inf>2</inf>/α-Fe<inf>2</inf>O<inf>3</inf>(0 0 0 1) surface. Furthermore, undergoing the ODH reaction to the steady state is 1.25 × 10<sup>3</sup> times faster than that of a lean α-Fe<inf>2</inf>O<inf>3</inf>(0 0 0 1) surface. Also, the amount of poisoned species of S* on the O<inf>2</inf>/α-Fe<inf>2</inf>O<inf>3</inf>(0 0 0 1) surface is lowered significantly. This work advances the design of robust catalysts for efficient H<inf>2</inf>S removal by providing a deeper understanding of catalytic behavior and deactivation mechanisms. The insights presented here contribute to cleaner energy production and environmental protection, bridging fundamental knowledge and practical applications in catalysis.
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Machine learning-accelerated density functional theory optimization of PtPd-based high-entropy alloys for hydrogen evolution catalysis
(2025-11-01)
Khajondetchairit, Patcharaporn
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Somdee, Siriwimol
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Saelee, Tinnakorn
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Ektarawong, Annop
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Alling, Björn
High-entropy alloys (HEAs) have emerged as promising catalysts for the hydrogen evolution reaction (HER) due to their compositional diversity and synergistic effects. In this study, machine learning-accelerated density functional theory (DFT) calculations were employed to assess the catalytic performance of PtPd-based HEAs with the formula PtPdXYZ (X, Y, Z = Fe, Co, Ni, Cu, Ru, Rh, Ag, Au; X ≠ Y ≠ Z). Among 56 screened HEA(111) surfaces, PtPdRuCoNi(111) was identified as the most promising, with adsorption energies (E<inf>ads</inf>) between −0.50 and −0.60 eV and high d-band center of −1.85 eV, indicating enhanced activity. This surface showed the hydrogen adsorption free energy (ΔG<inf>H*</inf>) of −0.03 eV for hydrogen adsorption, outperforming Pt(111) by achieving a better balance between adsorption and desorption. Machine learning models, particularly extreme gradient boosting regression (XGBR), significantly reduced computational costs while maintaining high accuracy (root-mean-square error, RMSE = 0.128 eV). These results demonstrate the potential of HEAs for efficient and sustainable hydrogen production.
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First-Principles Screening of 3d-Transition-Metal-Doped Hydrous Cobalt Phosphate Catalysts for Enhanced Oxygen Evolution Reaction
(2025-09-01)
Rittiruam, Meena
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Saelee, Tinnakorn
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Khajondetchairit, Patcharaporn
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Ektarawong, Annop
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Alling, Björn
Hydrous cobalt phosphate (CoPO) is a promising OER catalyst, but its activity is limited by poor electron transport and weak intermediate binding. This study reveals how 3d transition metal dopants can be used to tune these properties through first-principles calculations. Sc, Ti, V, and Cr improve catalytic activity by promoting electron transfer and stabilizing *O intermediates, while Mn, Fe, Cu, and Zn reduce performance. Among all candidates, Ni doping strikes the optimal balance, enhancing conductivity and providing moderate *O binding energy that minimizes overpotential. These trends follow electronic descriptors such as d-band center and electronegativity, and are validated by volcano plot analysis. Ni-CoPO emerges as the most effective design, offering a clear strategy for improving OER catalysts by controlling dopant identity and electronic structure.
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Machine-learning-accelerated density functional theory screening of Cu-based high-entropy alloys for carbon dioxide reduction to ethylene
(2025-03-01)
Rittiruam, Meena
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Khamloet, Pisit
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Tiwtusthada, Sirapat
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Ektarawong, Annop
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Saelee, Tinnakorn
Computational screening of high-entropy alloy (HEA) catalysts as alternatives to the typical Cu electrocatalyst for CO<inf>2</inf> reduction reaction (CO<inf>2</inf>RR) has been extensively focused on C<inf>1</inf> products, but C<inf>2+</inf> products have received significantly less attention. This work optimized CuZnPdAgAu HEA catalyst composition for CO<inf>2</inf>RR to ethylene via density functional theory and supervised machine learning regression techniques. Candidates were identified from 106,045 HEA data for enthalpy of adsorption of *CO<inf>2</inf>, *H, *HOCCOH, and *C<inf>2</inf>H<inf>4</inf> species, and the Gibbs free energy of *H. The electrocatalytic properties during the reaction were examined on the surface of the optimized HEA candidate – Cu<inf>0.36</inf>Zn<inf>0.18</inf>Pd<inf>0.10</inf>Ag<inf>0.18</inf>Au<inf>0.18</inf> benchmarked to Cu (111). The Pd site of such a candidate functions as the active site for the CO<inf>2</inf> activation step. In terms of catalytic activity, it showed lower Gibbs free energy for the potential determining step – the *OCCOH formation step compared to that on the Cu (111). Insight into electronic properties demonstrated that the candidate reduces the uphill reaction energy for *HOCCOH production pathway due to increased electron density in the C–C bond, donated from two Cu sites. It is shown that the HEA catalyst candidate has the potential for CO<inf>2</inf>RR targeting ethylene, an alternative to a common Cu catalyst.

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