Catalyst Formulation Research Articles

Influence of Dopants on Pt/Al2O3-Based Monolithic Catalysts for Autothermal Oxidative Coupling of Methane

Autothermal oxidative coupling of methane (OCM) is a highly attractive approach for methane utilization. If platinum-based catalysts are operated in short-contact-time reactors with high space velocities, high methane conversion can be achieved. Using a 1 wt.% Pt/Al2O3 catalyst as a benchmark, the present study elucidates how different dopants, namely Ni, Sn, and V2O5, affect the OCM reaction. Kinetic catalyst tests reveal that acetylene (C2H2) is the predominant C2 product, irrespective of the catalyst formulation or operation conditions. Furthermore, the use of bimetallic catalysts allows either for the maintenance or even the improvement of the C2 selectivity during OCM, which is attributed to synergistic effects that occur when expensive Pt is partially replaced by cheaper dopants. In particular, the 1 wt.% Pt/Al2O3 reference catalyst yielded a maximum C2 selectivity of 8.2%, whereas the best-performing doped sample 0.25 wt.% Pt 0.75 wt.% V2O5/Al2O3 yielded a total C2 selectivity of 11.3%.

Cu‐based catalysts for the one‐pot condensation‐hydrogenation of cyclopentanone to produce valuable SAF precursors

A process combining aldol condensation and hydrogenation of bio‐based cyclopentanone (CPO) in a one‐pot reaction is proposed. Cu‐based bifunctional catalysts obtained from hydrotalcite‐type precursors were found highly active in mild reaction conditions (i.e. 130‐170°C), producing valuable bi‐cyclic (C10) and tri‐cyclic (C15) aliphatic alcohols/ketones with interesting application as sustainable aviation fuel (SAF) precursors with enhanced properties. The presence of surficial strong basic sites active in aldol condensation, together with the formation of highly dispersed copper nanoparticles able to catalyze C=C and C=O hydrogenation (TOF of 0.06‐0.16 s‐1), assured high catalytic efficiency and fine control of the products nature and distribution. From the optimization of both catalysts formulation and reaction conditions it was found that, properly tuning the Cu content, it is possible to drive products distribution towards the production of bi‐cyclic C10 aliphatic alcohol with high selectivity (75% at total CPO conversion, when Cu = 10 wt.%, at 170°C, 1.0 MPa of H2) and short reaction time (4h). The optimized catalyst formulation showed impressive stability over multiple reaction/regeneration cycles, demonstrating the high potential of the proposed catalytic process for future applications in sustainable biofuels production.

Vitreous silica supported metal catalysts for direct non-oxidative methane coupling

Direct non-oxidative methane coupling (NMC) transforms methane into valuable chemicals and hydrogen, which is a promising pathway to boost industrial decarbonization. The reaction, however, is challenged by high C-H bond activation temperature, catalyst coking, and low selectivity towards hydrocarbon products. Here we studied the efficacy of five vitreous silica supported 3d transition metal (Mn, Fe, Co, Ni, and Cu) catalysts (denoted as M/SiO2-v) for NMC. These catalysts were prepared by flame fusion of mixtures of quartz and metal silicate precursors. The metal species in the as-synthesized catalysts were fully or partially oxidized, with their stability following the order of Mn > Fe ≈ Co > Ni > Cu in the NMC reaction. The Cu species has low methane reaction rate, due to the high adsorption and dissociation energies of methane. The Mn and Ni species have high methane reaction and coke formation rates, which is supported by easy kinetics of methane deep dehydrogenation to carbon. In contrast, Fe and Co species showed the best hydrocarbon product selectivity. Particularly, the Co species emerged as the optimal catalyst for NMC, showcasing a balanced methane activation, hydrocarbon formation, and low coke yield. The relationship between the evolved metal species under reaction conditions and their NMC performance was discussed. The present study screens and provides effective catalyst formulations for NMC reactions.

Low Temperature Sabatier CO2 Methanation

AbstractThe CO2 methanation reaction, or Sabatier reaction, is experiencing renewed interest in the context of large‐scale recycling of point CO2 emissions, leading to the power‐to‐gas technology. The reaction represents a flexible route to transform CO2 into methane by hydrogenation with (green) dihydrogen. This exothermic transformation takes place at a reasonable rate at temperatures above 200 °C and is directed to the targeted product at low temperatures. The CO2 methanation nevertheless remains kinetically limited due to the chemical stability of CO2 and the high bond dissociation energy for C═O in CO2. Therefore, the current urgent demand is for the development of catalysts and associated processes with superior activity for CO2 activation at low temperatures. This critical review aims to overview the state of the art of this low‐temperature technology using thermal, plasma and photo‐assisted catalysis. We summarize research advances around low‐temperature CO2 methanation, focusing on catalyst formulations (metal, supports and promoters), reaction mechanisms and suitable activation processes. We discuss each of these critical aspects of the technology and identify the main challenges and opportunities for low temperature (≤200 °C) CO2 methanation.

Formulation of a carbon-based solid acid catalyst from groundnut husk for the esterification of oleic acid

In this research, a biochar was prepared from groundnut husk and sulphuric acid. The biochar wassulphonated with chlorosulphonic acid to give a carbon-based sulphonated solid acid catalyst (SAC). The catalyst was characterised by N2 adsorption and desorption at 77K after degassing overnight under vacuum at 250˚ C, scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, and total acid density measurement. The results of the acid density measurement showed that the sulphonated biochar has a relatively high acid density of 3.2 mmol/g, while that of the non-sulphonated counterpart is 0.56 mmol/g. Comparison of the percentage conversion of oleic acid by esterification with methanol and butanol was carried out under three different conditions, viz: without catalyst, with the non-sulphonated biochar and with the sulphonated biochar, under the same experimental conditions of 5% catalyst weight percent (based on weight of acid), acid-methanol mole ratio of 1:12, reaction temperature of 70 oC, and five hours reaction time. Up to 91.6% percentage conversion of oleic acid to methyl oleate and 86.4% for butyl oleate were achieved, proving the effectiveness of the sulphonated biochar in catalysing the esterification reaction of oleic acid with methanol and butanol. The results of this study are expected to provide insights into the development of sustainable and cost-effective solid acid catalysts for the synthesis of oleate esters and other value-added materials from renewable feedstocks.

Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements

Rising levels of atmospheric carbon dioxide (CO2) and methane (CH4) have sparked the interest of researchers in resolving this issue. Various technologies have been utilized such as dry reformation of methane (DRM), that turns these greenhouse gases (GHGs) into syngas. Numerous studies have been done in recent years to produce efficient and competitive catalysts for DRM. However, a “state of the art” review is still required due to a lack of literature on improvements and scalability of robust catalytic systems. This study aims to evaluate recent advancements in catalyst formulations, their activities, stability, and selectivity. We have discussed a variety of materials, including Metal-Organic Frameworks (MOF), a type of two-dimensional transition metal carbides and nitrides (MXene), reducible and irreducible metal oxides, transition metal oxides, zeolites, carbon, and biochar-based catalysts, which are responsible for improving the effectiveness of the DRM process. Furthermore, machine learning (ML) approaches are highlighted for their potential in catalyst design and optimization, providing increased accuracy and efficiency. This review highlights major enhancements to the DRM method, which result in higher hydrogen yield and catalyst stability. Novel catalysts have been created to tackle crucial problems including coking and sintering, which are necessary to progress commercialization. To ensure successful commercial application, future research should concentrate on finding catalysts with excellent features that are also economically viable. This study provides a significant resource for future catalyst development and promotes greater cooperation between academic and commercial communities.

Catalytic decomposition of NH3 as a by-product of magnetically confined nuclear fusion

A statistical design of experiments was conducted to optimize a trimetallic catalyst formulation consisting of ruthenium, yttrium, and potassium on γ-Al2O3 (RuYK/ γ-Al2O3) for use as ammonia (NH3) decomposition catalyst in the hydrogen isotope impurity processing for magnetically confined nuclear fusion systems. Optimal weight loadings of 6.9 wt-% Ru, 4.3 wt-% Y, and 12 wt-% K were determined through the design of experiments. The thermal stability of the catalyst was investigated through thermal cycling of the catalyst over 30 cycles. The optimized catalyst remained stable over the cycles under reducing conditions. As oxygen, carbon dioxide and water are the primary impurities in the Tokamak exhaust, the chemical stability of the catalyst was determined against these impurities. While these impurities initially decreased the NH3 decomposition activity, the initial activity was attained once the impurity was removed from the stream.

Synthesis, characterization, and performance evaluation of unsupported tetrametallic CoMoMnAl and CoMoZnAl catalysts for selective hydrodesulfurization (HDS)

New formulations of unsupported tetrametallic catalysts derived from layered double hydroxides (LDH) compounds were synthesized by a constant pH coprecipitation method, characterized, and evaluated in the selective hydrodesulfurization (HDS) of thiophene in the presence of cyclohexene. The layered double hydroxides were prepared using terephthalate (C8H8O2)2- as the interlayer anion with nominal aluminum molar fraction of 0.5, and nominal cobalt to MII (M = Zn or Mn) ratios of 0.2, 0.4 and 0.6. Molybdenum introduction in the LDH precursor was accomplished by an ion exchange process using the heptamolybdate anion. The calcined Mn samples showed a higher surface area than those obtained from Zn. The sulfidation was carried out in situ with a heating ramp up to 673 K and the catalysts were evaluated in a tubular flow reactor. The catalytic evaluation of Zn and Mn-based materials for simultaneous thiophene HDS and cyclohexene hydrogenation (HYD) was conducted at 573 K and 20 bar. The results show that the Zn-based unsupported catalysts synthesized here exhibited higher selectivity for the HDS reaction than an industrial one.

Formulation of zeolite-mesoporous silica composite catalysts for light olefin production from catalytic cracking

Framework materials such as zeolites and mesoporous silicas are commonly used for many applications, especially catalysis and separation. Here zeolite-mesoporous silica composite catalysts (employing zeolite Y, ZSM-5, KIT-6, SBA-15 and MCM-41 mesoporous silica) were prepared (with different weight percent of zeolite Y and ZSM-5) and assessed for catalytic cracking (using n-heptane, as the model compound at 550 °C) with the aim to improve the selectivity/yield of light olefins of ethylene and propylene from n-heptane. Physicochemical properties of the parent zeolites and the prepared composites were characterized comprehensively using several techniques including X-ray diffraction, nitrogen physisorption, scanning electron microscopy, fourier transform infrared spectroscopy, pulsed-field gradient nuclear magnetic resonance and thermogravimetric analysis. Catalytic cracking results showed that the ZY/ZSM-5/KIT-6 composite (20:20:60 wt %) achieved a high n-heptane conversion of 85% with approximately 6% selectivity to ethylene/propylene. In contrast, the ZY/ZSM-5/SBA-15 composite achieved a higher conversion of 95% and an ethylene/propylene ratio of 8%, indicating a more efficient process in terms of both conversion and selectivity. Magnetic resonance relaxation analysis of the ZY/ZSM-5/KIT-6 (20:20:60) catalyst confirmed a micro-mesoporous environment that influences n-heptane diffusion and mass transfer. As zeolite Y and ZSM-5 have micropores, n-heptane can move and undergo hydrogen transfer reactions, whereas KIT-6 has mesopores that facilitate n-heptane’s accessibility to the active sites of zeolite Y and ZSM-5.

Integrated catalytic systems for simultaneous NOx and PM reduction: a comprehensive evaluation of synergistic performance and combustion waste energy utilization.

The global transition towards sustainable automotive vehicles has driven the demand for energy-efficient internal combustion engines with advanced aftertreatment systems capable of reducing nitrogen oxides (NOx) and particulate matter (PM) emissions. This comprehensive review explores the latest advancements in aftertreatment technologies, focusing on the synergistic integration of in-cylinder combustion strategies, such as low-temperature combustion (LTC), with post-combustion purification systems. Selective catalytic reduction (SCR), lean NOx traps (LNT), and diesel particulate filters (DPF) are critically examined, highlighting novel catalyst formulations and system configurations that enhance low-temperature performance and durability. The review also investigates the potential of energy conversion and recovery techniques, including thermoelectric generators and organic Rankine cycles, to harness waste heat from the exhaust and improve overall system efficiency. By analyzing the complex interactions between engine operating parameters, combustion kinetics, and emission formation, this study provides valuable insights into the optimization of integrated LTC-aftertreatment systems. Furthermore, the review emphasizes the importance of considering real-world driving conditions and transient operation in the development and evaluation of these technologies. The findings presented in this article lay the foundation for future research efforts aimed at overcoming the limitations of current aftertreatment systems and achieving superior emission reduction performance in advanced combustion engines, ultimately contributing to the development of sustainable and efficient automotive technologies.

Machine learning-enhanced optimal catalyst selection for water-gas shift reaction

The water-gas shift (WGS) reaction is pivotal in industries aiming to convert carbon monoxide, a byproduct of steam reforming of methane and other hydrocarbons, into carbon dioxide and hydrogen. Selecting an effective catalyst for this transformation poses a substantial challenge, as it requires a delicate balance between conversion, stability, and cost. We combine machine learning-driven prediction models with Bayesian optimization to explore and identify novel catalyst compositions. The proposed method efficiently explores the catalysis composition space for a predefined set of active metals, supports, and promoters to identify the most promising catalyst formulations. We assign weights to different performance metrics of catalysts, enabling tailored optimization according to specific industry needs. Our screening system streamlines catalyst discovery and facilitates the screening and selection of catalysts that balance conversion performance, stability, and cost-effectiveness. This approach holds significant promise for advancement in heterogeneous catalysis to meet the growing demands of efficient industrial processes.

Core–Shell catalyst particles for tandem catalysis: An experimental/numerical approach towards optimal design

Tandem catalysis is a promising approach to intensify chemical processes and increase their efficiency. On the other hand, the design of efficient, optimal and targeted tailored tandem catalysts is yet so challenging as the optimal catalyst loading is difficult to assess a priori. In this article, we present a concise route towards the design of optimal core–shell tandem catalyst particles on the example of coupled RWGS and FTS reactions for any specific spherical morphology. The route features five consecutive steps including: tandem system identification, catalyst synthesis, i.e. mono- and tandem-functional, catalyst characterization and initial performance test, kinetic modeling with parameter estimation if necessary, and optimal design of catalysts. The initial step features thermodynamic equilibrium calculations for RWGS and FTS showing a common operational window. Then, Pt and Co are selected as active metals and the formulation of the tandem catalyst is designed. For the second step, the synthesis route for the tandem catalyst Pt,CeO2@SiO2-Co, and the mono-functional catalysts, Pt@SiO2 and CeO2@SiO2-Co are presented. For the third step, all catalysts were tested for CO2 hydrogenation as an exemplary tandem process. A reduced transport model from literature was adjusted for RWGS and FTS reactions with kinetic expression from literature to enable numerical optimization. The kinetic parameters are estimated based on the performance tests of the mono-functional reference materials, i.e. Pt@SiO2 for RWGS and CeO2@SiO2-Co for FTS. The model is validated by cross comparison to the data from the tandem reaction setup. In the fifth step, the model was used for the numerical optimization of the catalyst loading on core and shell leading to the identification of the optimal design, resulting in a significant increase of C2+ Yield.

Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery

Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp3–sp2 cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical properties using molecular descriptors to identify promising OPCs from a virtual library of 560 candidate molecules. This led to OPC formulations that are competitive with iridium catalysts by exploring just 2.4% of the available catalyst formulation space (107 of 4,500 possible reaction conditions).

Tailored Metal-Porphyrin Based Molecular Electrocatalysts for Enhanced Artificial Nitrogen Fixation to Green Ammonia.

Electrochemical nitrogen reduction (E-NRR) is one of the most promising approaches to generate green NH3. However, scarce ammonia yields and Faradaic efficiencies (FE) still limit their use on a large scale. Thus, efforts are focusing on different E-NRR catalyst structures and formulations. Among present strategies, molecular electrocatalysts such as metal-porphyrins emerge as an encouraging option due to their planar structures which favor the interaction involving the metal center, responsible for adsorption and activation of nitrogen. Nevertheless, the high hydrophobicity of porphyrins limits the aqueous electrolyte-catalyst interaction lowering yields. This work introduces a new class of metal-porphyrin based catalysts, bearing hydrophilic tris(ethyleneglycol) monomethyl ether chains (metal = Cu(II) and CoII)). Experimental results show that the presence of hydrophilic chains significantly increases ammonia yields and FE, supporting the relevance of fruitful catalyst-electrolyte interactions. This study also investigates the use of hydrophobic branched alkyl chains for comparison, resulting in similar performances with respect to the unsubstituted metal-porphyrin, taken as a reference, further confirming that the appropriate design of electrocatalysts carrying peripheral hydrophilic substituents is able to improve device performances in the generation of green ammonia.

Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)). The kinetically validated random forest models are deployed to locate optimal conditions that maximize C2 yield for each of the catalysts. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions in addition to the reaction conditions, while the predictions of methane conversion are largely governed by the reaction conditions. The machine learning (ML) regressor is used as a kinetic surrogate to find a locus of optimal reaction conditions that maximize both selectivity-conversion for each of the catalysts via a multi-objective optimization routine. The maximum C2 yields for catalysts are projected to be improved by 15% on average. Analyzing the catalysts with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, using the optimal locus eliminates variability in the process conditions to reveal distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and reaction conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts. The framework has the potential to lend itself to materials acceleration platforms where it is crucial to consider multi-scale phenomena that impact downstream KPIs.

High throughput screening of promoted bimetallic catalysts supported on alumina for production of light olefins via Fischer-Tropsch synthesis with artificial neural network and response surface methodology

The production of light olefins via Fischer-Tropsch Synthesis (FTS) was investigated by both experimental and modeling studies. Experimental studies were conducted by employing promoted FeCo/α-Al2O3 (FeCo) bimetallic catalysts. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods were used in modeling. Specifically, effects of the types and concentration of promoters on the catalytic performances of α-Al2O3 supported iron-cobalt bimetallic catalysts are investigated by using Ce, Ni, La, Ho, Ga Cu, Mn, and Zn used as promoters. A total of 49 catalysts were prepared by co-impregnation method and tested at 310 °C and 1 bar in a high-speed catalyst performance test system (HT-CPA). The FTS performances of catalysts showed that both the types and amounts of the promoters can affect light olefin production. The light olefin (C2=–C3=) production of unpromoted FeCo catalyst was about 3.87 × 10−3 (mol C/g active metal. h). The best performances were obtained from FeCo catalysts promoted with Ho, Cu, and Zn. Presence of these promoters increased olefin production up to ∼ 84 %, 76 % and 70 %, respectively.Performance parameters (MSE and R2) of RSM and ANN models revealed that both methods can be used to support experimental studies and provide a procedure for optimizing catalyst formulations yielding the desired product selectivity in FTS processes. The RSM outcomes aligned with experimental findings, suggest that that Ho, Cu, and Zn can serve as suitable promoters for the light olefin production in FTS. ANN model attained higher performance parameters (R2 = 0.95 and MSE = 2E−7) than RSM model (R2 = 0.85 and MSE = 3.4E−7). This indicates that ANN model, which designed as one hidden layer including 7 neurons, can provide better predictions and a fitting capacity. Therefore, it may be used in further optimization and sensitivity analysis approaches. Alignment degree between the experimental and modeling results demonstrates that the proposed methodology can provide a reliable and efficient way to guide experiments to optimize catalyst formulations through identification of potential promoters and their amounts to be used.

Autothermal Oxidative Coupling of Methane over Pt/Al2O3 Catalysts Doped with Rare Earth Oxides

AbstractThe effect of rare earth oxides as dopants on the Pt‐governed oxidative coupling of methane (Pt‐OCM) is investigated. The addition of 20 wt.‐% of La‐, Nd‐, Sm‐, and Pr‐oxides to the support benefits the C2 product selectivity and offers the potential to enhance the catalyst stability. Compared to the reference catalyst Pt/Al2O3, the addition of Sm2O3 increased the total selectivity by up to 23 %. Furthermore, La2O3 addition was found beneficial with respect to the (thermal) stability of the catalyst. Dopant content variations uncovered that the catalyst formulation with 20 wt.‐% La2O3 represents an optimum regarding performance and stability.

Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis

Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performance characteristics is a challenging task, as their performance depends on various factors such as precursor, support material, promoters, pretreatment conditions and the catalyst structures. Thus, it remains difficult to understand the structure-performance relationship of FTS and to optimize the catalyst formulations and operating conditions rationally. By integrating traditional chemistry with machine learning, we herein establish intrinsic correlations among reduction, reaction conditions, phase information and the methane selectivity of Fe-based FTS, using high quality experimental data. The content of the iron phases in the post-reaction phase, particularly χ-Fe5C2, significantly influences the methane selectivity of the catalyst. Four types of additives K, Cu, SiO2, and Ca could effectively suppress the methane selectivity, most likely by promoting or stabilizing the iron carbide phases, indicated by their strong correlation. The machine learned structure-performance relationships offers new insights into the design of Fe-based FTS catalysts, and could guide the further optimization of the preprocessing conditions and various parameter factors to minimize the methane selectivity of FTS.

Syntheses, catalytic performances and DFT investigations: A recent review of copper-based catalysts of methanol steam reforming for hydrogen production

Copper-based catalysts have demonstrated notable efficacy in facilitating methanol conversion and hydrogen yield at low temperatures. Given the straightforward and cost-effective nature of the process, further investigation into this area is merited. This article provides a summary of the progress made in the development of copper-based catalysts for hydrogen production through methanol steam reforming. The catalytic performance of conventional Cu/ZnO/Al2O3 catalysts can be exceeded by utilizing specific catalyst formulations and preparation procedures, which are characterized by their intricate structural and chemical properties. The studies about MSR mechanism have also been integrated. The realm of DFT research encompasses an examination of the monatomic systems, which have garnered significant attention in contemporary times. Furthermore, this paper presented potential avenues for future research on copper-based catalysts in the context of MSR. We believe that the forthcoming research will concentrate on the design, preparation, and molecular mechanism of copper-based catalysts that exhibit high stability, high activity, and low CO selectivity.

Exploring the feasibility of continuous CWAO of bisphenol A at near-ambient temperature and pressure through use of hydrophobic Pt catalysts

Hydrophobic Pt CWAO-catalysts can achieve complete removal of bisphenol A from a flow of contaminated water in a trickle-bed reactor at an operating temperature of 120°C, total air pressure of 8 bar and a liquid-hourly space velocity of 26.6 h−1. Although increasing the throughput of contaminated water while lowering the operating temperature results in bisphenol A conversions below 100%, these more demanding conditions allow structurally similar catalyst formulations to be differentiated from one another. At 60°C and 8 bar total pressure of air, 2%Pt supported on a SiC-TiC composite material has the highest initial activity from a group of three hydrophobic catalysts with similar surface areas and Pt particle diameters, but it begins to deactivate progressively after 15 hours on stream. This catalyst contains some localised hydrophilicity arising from the presence of surface TiO2, which forms when the exposed TiC component of the support material oxidises during catalyst preparation. At 80 °C and ambient air pressure, the activity is lower but there are no signs of deactivation during 24 hours on stream. The results are consistent with metallic platinum providing the active sites for CWAO of bisphenol A, with oxygen being directly activated from the gas phase at elevated pressures, but with dissolved oxygen also contributing to the reaction particularly at ambient air pressure. Continuous and irreversible deactivation, which occurs at air pressures ≥4 bar, appears to be associated with high occupancy of the active sites by adsorbed oxygen, resulting in leaching of platinum into the aqueous phase.