How to Develop Accurate Catalytic Converter Performance Models
MAR 24, 20269 MIN READ
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Catalytic Converter Technology Background and Objectives
Catalytic converters represent one of the most critical emission control technologies in modern automotive systems, serving as the primary mechanism for reducing harmful pollutants from internal combustion engines. Since their widespread adoption in the 1970s following stringent environmental regulations, these devices have evolved from simple oxidation catalysts to sophisticated three-way catalytic systems capable of simultaneously reducing nitrogen oxides, carbon monoxide, and unburned hydrocarbons.
The fundamental principle underlying catalytic converter operation involves the acceleration of chemical reactions through precious metal catalysts, typically platinum, palladium, and rhodium, deposited on high-surface-area ceramic or metallic substrates. The technology has progressed through several generations, from early pellet-type converters to modern monolithic honeycomb structures that offer superior flow characteristics and enhanced catalyst utilization efficiency.
Contemporary catalytic converter development faces increasingly stringent emission standards, including Euro 7, China VI, and Tier 3 regulations, which demand near-zero tailpipe emissions across diverse operating conditions. These regulatory pressures have driven the need for more sophisticated catalyst formulations, advanced substrate designs, and optimized thermal management systems. Additionally, the integration of hybrid powertrains and the transition toward electrification present unique challenges for catalyst thermal conditioning and performance optimization.
The primary objective of developing accurate catalytic converter performance models centers on creating predictive tools that can reliably forecast conversion efficiency, pressure drop, thermal behavior, and durability characteristics across the entire operating envelope. These models must account for complex interactions between gas-phase and surface reactions, mass and heat transfer phenomena, catalyst aging mechanisms, and transient operating conditions including cold-start scenarios and thermal cycling.
Advanced performance modeling aims to enable virtual catalyst development, reducing the reliance on extensive experimental testing while accelerating the optimization of catalyst formulations and converter designs. The ultimate goal involves establishing comprehensive digital twins that can predict real-world performance, support control system development, and facilitate the design of next-generation emission control systems capable of meeting future regulatory requirements while maintaining cost-effectiveness and durability standards.
The fundamental principle underlying catalytic converter operation involves the acceleration of chemical reactions through precious metal catalysts, typically platinum, palladium, and rhodium, deposited on high-surface-area ceramic or metallic substrates. The technology has progressed through several generations, from early pellet-type converters to modern monolithic honeycomb structures that offer superior flow characteristics and enhanced catalyst utilization efficiency.
Contemporary catalytic converter development faces increasingly stringent emission standards, including Euro 7, China VI, and Tier 3 regulations, which demand near-zero tailpipe emissions across diverse operating conditions. These regulatory pressures have driven the need for more sophisticated catalyst formulations, advanced substrate designs, and optimized thermal management systems. Additionally, the integration of hybrid powertrains and the transition toward electrification present unique challenges for catalyst thermal conditioning and performance optimization.
The primary objective of developing accurate catalytic converter performance models centers on creating predictive tools that can reliably forecast conversion efficiency, pressure drop, thermal behavior, and durability characteristics across the entire operating envelope. These models must account for complex interactions between gas-phase and surface reactions, mass and heat transfer phenomena, catalyst aging mechanisms, and transient operating conditions including cold-start scenarios and thermal cycling.
Advanced performance modeling aims to enable virtual catalyst development, reducing the reliance on extensive experimental testing while accelerating the optimization of catalyst formulations and converter designs. The ultimate goal involves establishing comprehensive digital twins that can predict real-world performance, support control system development, and facilitate the design of next-generation emission control systems capable of meeting future regulatory requirements while maintaining cost-effectiveness and durability standards.
Market Demand for Advanced Catalytic Converter Models
The automotive industry faces unprecedented pressure to develop sophisticated catalytic converter performance models driven by increasingly stringent global emission regulations. The European Union's Euro 7 standards, California's Advanced Clean Cars II program, and China's National VI emission standards are pushing manufacturers toward more precise emission control technologies. These regulatory frameworks demand real-time monitoring capabilities and predictive maintenance systems that can only be achieved through advanced modeling approaches.
Traditional catalytic converter design methodologies rely heavily on empirical testing and simplified mathematical models, which prove inadequate for modern multi-pollutant control requirements. The market increasingly demands models capable of predicting performance across diverse operating conditions, fuel compositions, and aging scenarios. This shift represents a fundamental change from reactive to predictive emission control strategies.
The electric vehicle transition paradoxically intensifies rather than diminishes this market demand. Hybrid powertrains require more sophisticated emission control due to frequent engine start-stop cycles and varying thermal conditions. Additionally, the growing heavy-duty transportation sector, including commercial vehicles and marine applications, presents substantial opportunities for advanced catalytic converter modeling solutions that can optimize performance across extended operational ranges.
Automotive OEMs are actively seeking modeling solutions that can reduce development costs while accelerating time-to-market for new emission control systems. The integration of artificial intelligence and machine learning techniques into catalytic converter performance prediction represents a significant market opportunity, with suppliers investing heavily in digital twin technologies and physics-informed neural networks.
The aftermarket sector also drives substantial demand for accurate performance models, particularly for diagnostic applications and replacement part optimization. Fleet operators require predictive maintenance capabilities to minimize downtime and ensure regulatory compliance, creating additional market segments for advanced modeling technologies.
Emerging markets in developing countries present unique challenges requiring localized modeling approaches that account for varying fuel qualities and operating conditions. This geographic diversity expands the total addressable market while necessitating more robust and adaptable modeling frameworks that can perform reliably across different environmental and operational contexts.
Traditional catalytic converter design methodologies rely heavily on empirical testing and simplified mathematical models, which prove inadequate for modern multi-pollutant control requirements. The market increasingly demands models capable of predicting performance across diverse operating conditions, fuel compositions, and aging scenarios. This shift represents a fundamental change from reactive to predictive emission control strategies.
The electric vehicle transition paradoxically intensifies rather than diminishes this market demand. Hybrid powertrains require more sophisticated emission control due to frequent engine start-stop cycles and varying thermal conditions. Additionally, the growing heavy-duty transportation sector, including commercial vehicles and marine applications, presents substantial opportunities for advanced catalytic converter modeling solutions that can optimize performance across extended operational ranges.
Automotive OEMs are actively seeking modeling solutions that can reduce development costs while accelerating time-to-market for new emission control systems. The integration of artificial intelligence and machine learning techniques into catalytic converter performance prediction represents a significant market opportunity, with suppliers investing heavily in digital twin technologies and physics-informed neural networks.
The aftermarket sector also drives substantial demand for accurate performance models, particularly for diagnostic applications and replacement part optimization. Fleet operators require predictive maintenance capabilities to minimize downtime and ensure regulatory compliance, creating additional market segments for advanced modeling technologies.
Emerging markets in developing countries present unique challenges requiring localized modeling approaches that account for varying fuel qualities and operating conditions. This geographic diversity expands the total addressable market while necessitating more robust and adaptable modeling frameworks that can perform reliably across different environmental and operational contexts.
Current Modeling Challenges and Technical Limitations
Developing accurate catalytic converter performance models faces significant computational complexity challenges stemming from the multiphysics nature of the system. The simultaneous occurrence of fluid dynamics, heat transfer, mass transfer, and chemical reactions creates a highly nonlinear system that requires sophisticated numerical methods. Current computational fluid dynamics approaches struggle to capture the intricate interactions between turbulent flow patterns and catalytic surface reactions, particularly in the washcoat layer where most conversion occurs.
The heterogeneous nature of catalytic processes presents another fundamental modeling limitation. Traditional models often assume uniform catalyst distribution and activity across the substrate, which poorly represents real-world conditions where catalyst aging, poisoning, and thermal degradation create spatial variations in performance. These non-uniformities significantly impact conversion efficiency predictions, especially under transient operating conditions.
Transient modeling capabilities remain inadequate for capturing real-world driving scenarios. Most existing models are optimized for steady-state conditions and fail to accurately predict performance during cold starts, rapid acceleration, or deceleration events. The thermal inertia of the catalyst substrate and the time-dependent nature of surface reactions create modeling challenges that current approaches cannot adequately address.
Scale bridging represents a critical technical limitation in current modeling frameworks. The need to connect molecular-level reaction mechanisms with macroscopic flow phenomena across multiple length scales creates computational bottlenecks. Existing models often sacrifice accuracy at one scale to maintain computational feasibility at another, resulting in compromised overall prediction capabilities.
Validation and calibration challenges further constrain model accuracy. Limited availability of detailed experimental data under controlled conditions makes it difficult to validate complex models across the full range of operating parameters. The expensive and time-consuming nature of comprehensive catalyst testing restricts the development of robust validation datasets necessary for model refinement.
Current models also struggle with aging and degradation prediction. The complex interplay between thermal cycling, chemical poisoning, and mechanical stress on catalyst performance over extended periods remains poorly understood and inadequately modeled. This limitation significantly impacts the ability to predict long-term converter performance and optimize catalyst formulations for durability.
The heterogeneous nature of catalytic processes presents another fundamental modeling limitation. Traditional models often assume uniform catalyst distribution and activity across the substrate, which poorly represents real-world conditions where catalyst aging, poisoning, and thermal degradation create spatial variations in performance. These non-uniformities significantly impact conversion efficiency predictions, especially under transient operating conditions.
Transient modeling capabilities remain inadequate for capturing real-world driving scenarios. Most existing models are optimized for steady-state conditions and fail to accurately predict performance during cold starts, rapid acceleration, or deceleration events. The thermal inertia of the catalyst substrate and the time-dependent nature of surface reactions create modeling challenges that current approaches cannot adequately address.
Scale bridging represents a critical technical limitation in current modeling frameworks. The need to connect molecular-level reaction mechanisms with macroscopic flow phenomena across multiple length scales creates computational bottlenecks. Existing models often sacrifice accuracy at one scale to maintain computational feasibility at another, resulting in compromised overall prediction capabilities.
Validation and calibration challenges further constrain model accuracy. Limited availability of detailed experimental data under controlled conditions makes it difficult to validate complex models across the full range of operating parameters. The expensive and time-consuming nature of comprehensive catalyst testing restricts the development of robust validation datasets necessary for model refinement.
Current models also struggle with aging and degradation prediction. The complex interplay between thermal cycling, chemical poisoning, and mechanical stress on catalyst performance over extended periods remains poorly understood and inadequately modeled. This limitation significantly impacts the ability to predict long-term converter performance and optimize catalyst formulations for durability.
Existing Performance Modeling Solutions and Methods
01 Model-based catalytic converter monitoring and diagnostics
Advanced monitoring systems utilize mathematical models to predict and assess catalytic converter performance in real-time. These models incorporate various parameters such as temperature, exhaust gas composition, and flow rates to determine converter efficiency. The systems can detect degradation, poisoning, or failure by comparing actual performance against predicted values, enabling proactive maintenance and ensuring compliance with emission standards.- Model-based catalytic converter monitoring and diagnostics: Advanced monitoring systems utilize mathematical models to predict and assess catalytic converter performance in real-time. These models incorporate various parameters such as temperature, exhaust gas composition, and flow rates to determine converter efficiency. The systems can detect degradation, poisoning, or failure by comparing actual performance against predicted values, enabling proactive maintenance and ensuring compliance with emission standards.
- Neural network and machine learning approaches for performance prediction: Artificial intelligence and machine learning techniques are employed to create adaptive models that learn from operational data to improve accuracy over time. These systems can handle complex non-linear relationships between multiple variables affecting converter performance. The models are trained using historical data and can predict conversion efficiency, breakthrough times, and remaining useful life with high precision across varying operating conditions.
- Sensor-based validation and model calibration methods: Integrated sensor systems provide real-time feedback to validate and calibrate performance models continuously. Multiple sensors measure key parameters including oxygen levels, temperature gradients, and pressure drops across the converter. The collected data is used to adjust model parameters dynamically, compensating for aging effects, fuel quality variations, and environmental conditions to maintain prediction accuracy throughout the converter's lifecycle.
- Thermal modeling and heat transfer analysis: Sophisticated thermal models simulate heat distribution and temperature profiles within the catalytic converter structure. These models account for exothermic reactions, heat losses, and thermal inertia to predict light-off times and optimal operating temperatures. Accurate thermal modeling is essential for predicting conversion efficiency under cold-start conditions and transient operations, helping optimize catalyst formulation and substrate design.
- Chemical kinetics and reaction rate modeling: Detailed chemical kinetic models describe the complex reactions occurring on catalyst surfaces, including oxidation and reduction processes. These models incorporate reaction mechanisms, activation energies, and surface coverage effects to predict conversion rates for various pollutants. The accuracy of these models depends on understanding catalyst composition, active site distribution, and the effects of inhibitors or poisons, enabling precise prediction of converter performance across different fuel types and operating conditions.
02 Neural network and machine learning approaches for performance prediction
Artificial intelligence and machine learning techniques are employed to create adaptive models that learn from operational data to improve accuracy over time. These systems can handle complex non-linear relationships between multiple variables affecting converter performance. The models are trained using historical data and can predict conversion efficiency, breakthrough times, and remaining useful life with high precision under varying operating conditions.Expand Specific Solutions03 Sensor-based validation and model calibration methods
Integrated sensor systems provide real-time feedback to validate and calibrate performance models continuously. Multiple sensors measure critical parameters including oxygen levels, temperature gradients, and pressure drops across the converter. The collected data is used to adjust model parameters dynamically, compensating for aging effects, fuel quality variations, and environmental conditions to maintain prediction accuracy throughout the converter's lifecycle.Expand Specific Solutions04 Thermal modeling and heat transfer analysis
Sophisticated thermal models simulate heat distribution and temperature profiles within the catalytic converter structure. These models account for exothermic reactions, heat losses, and thermal inertia to predict light-off times and optimal operating temperatures. Accurate thermal modeling is essential for predicting conversion efficiency under cold-start conditions and transient operations, helping to optimize catalyst formulation and substrate design.Expand Specific Solutions05 Chemical kinetics and reaction rate modeling
Detailed chemical kinetic models describe the complex reactions occurring on catalyst surfaces, including oxidation and reduction processes. These models incorporate reaction mechanisms, activation energies, and surface coverage effects to predict conversion rates for various pollutants. The accuracy of these models depends on understanding catalyst composition, active site distribution, and mass transfer limitations, enabling optimization of catalyst formulations and prediction of performance degradation over time.Expand Specific Solutions
Key Players in Catalytic Converter and Modeling Industry
The catalytic converter performance modeling field represents a mature technology sector within the broader automotive emissions control industry, which has reached a market size of approximately $15-20 billion globally. The industry is currently in a consolidation phase, driven by increasingly stringent emission regulations and the transition toward electrification. Technology maturity varies significantly across market players, with established automotive suppliers like Robert Bosch GmbH, Continental Emitec, and BASF Corp. leading in advanced modeling capabilities and substrate technologies. Major automotive manufacturers including BMW, Mercedes-Benz, Toyota Motor Europe, Ford Global Technologies, and GM Global Technology Operations have developed sophisticated in-house modeling expertise to optimize their emission control systems. Chemical companies such as Tosoh Corp. and Wanhua Chemical Group contribute specialized catalyst materials knowledge, while emerging players like Katcon Global focus on cost-effective solutions for developing markets. The competitive landscape shows clear technological leadership among German and Japanese companies, with increasing participation from Chinese manufacturers and research institutions like Central South University, reflecting the global shift in automotive manufacturing centers.
Robert Bosch GmbH
Technical Solution: Bosch develops comprehensive catalytic converter performance models using advanced sensor integration and real-time data analytics. Their approach combines physical modeling with machine learning algorithms to predict converter efficiency under various operating conditions. The company utilizes lambda sensors, temperature monitoring, and exhaust gas composition analysis to create dynamic models that account for catalyst aging, thermal cycling, and poisoning effects. Their models incorporate three-way catalyst kinetics, oxygen storage capacity modeling, and light-off temperature predictions. Bosch's simulation tools enable optimization of catalyst formulations and substrate designs while predicting performance degradation over vehicle lifetime, supporting both development and diagnostic applications in modern emission control systems.
Strengths: Extensive automotive expertise, integrated sensor systems, real-world validation data. Weaknesses: Proprietary systems may limit academic collaboration, high development costs.
Continental Emitec Verwaltungs GmbH
Technical Solution: Continental Emitec specializes in metallic substrate catalytic converters and develops performance models focused on heat transfer characteristics and flow dynamics. Their modeling approach emphasizes the unique properties of metallic substrates, including rapid light-off capabilities and thermal management. The company creates detailed computational fluid dynamics models that simulate exhaust gas flow patterns, temperature distribution, and conversion efficiency across different cell densities and foil thicknesses. Their models incorporate substrate durability factors, thermal expansion effects, and coating adhesion properties. Continental Emitec's performance prediction tools help optimize substrate geometry, cell structure, and catalyst loading to achieve maximum conversion efficiency while maintaining mechanical integrity under extreme operating conditions.
Strengths: Specialized metallic substrate expertise, advanced thermal modeling capabilities, proven durability solutions. Weaknesses: Limited to metallic substrates, smaller market presence compared to ceramic alternatives.
Core Innovations in Catalytic Converter Modeling Patents
A system for determining age of a catalytic converter and method thereof
PatentActiveIN202141013878A
Innovation
- A system using two lambda sensors and an Engine Control Unit (ECU) to measure oxygen levels in untreated and treated exhaust gases, determining the operating condition, and estimating the catalytic converter's age based on phase shifts and pre-fed data for multiple operating conditions, allowing for precise age prediction across various conditions.
Computer-implemented method for simulating chemical reactions in an aged catalytic converter
PatentWO2020259790A1
Innovation
- A computer-implemented method that applies a single common aging coefficient to groups of Arrhenius equations, simplifying the simulation of chemical reactions in aged catalytic converters by multiplying pre-exponential factors, thereby reducing the need for individual calibration and maintaining accuracy in reaction rate calculations.
Environmental Regulations Impact on Modeling Requirements
Environmental regulations serve as the primary driving force behind catalytic converter performance modeling requirements, establishing stringent emission standards that directly influence model accuracy and complexity demands. The evolution from Euro 1 to Euro 6 standards in Europe, alongside similar progressions in EPA Tier regulations in the United States, has systematically reduced permissible NOx, CO, and hydrocarbon emissions by over 95% since the 1990s. These increasingly restrictive limits necessitate models capable of predicting converter performance within narrow tolerance bands, often requiring accuracy levels of ±5% or better for emission predictions.
Real Driving Emissions (RDE) regulations introduced in Euro 6d have fundamentally transformed modeling requirements by mandating performance validation under actual driving conditions rather than laboratory cycles alone. This regulatory shift demands dynamic models that can accurately predict catalytic converter behavior across varying temperature profiles, transient operating conditions, and diverse driving patterns. Models must now incorporate real-world factors such as cold-start performance, thermal cycling effects, and aging characteristics under actual usage scenarios.
California's Advanced Clean Cars II program and similar zero-emission vehicle mandates are pushing modeling requirements toward hybrid and electric vehicle applications, where catalytic converters operate under intermittent conditions with frequent thermal cycling. These regulations require models to predict performance during engine restart scenarios, extended idle periods, and varying thermal management strategies unique to electrified powertrains.
Onboard Diagnostics (OBD) regulations mandate continuous monitoring of catalytic converter efficiency, creating demand for simplified yet accurate models suitable for real-time implementation in engine control units. These embedded models must balance computational efficiency with sufficient accuracy to detect converter degradation and ensure compliance throughout vehicle lifetime, typically requiring processing capabilities under 10 milliseconds per calculation cycle.
Future regulations targeting particulate number emissions and ammonia slip are driving development of multi-pollutant models capable of simultaneous prediction across various emission species. The anticipated introduction of Euro 7 standards will likely impose even stricter limits while extending durability requirements, necessitating models that can accurately predict long-term performance degradation and aging effects over extended mileage intervals exceeding 200,000 kilometers.
Real Driving Emissions (RDE) regulations introduced in Euro 6d have fundamentally transformed modeling requirements by mandating performance validation under actual driving conditions rather than laboratory cycles alone. This regulatory shift demands dynamic models that can accurately predict catalytic converter behavior across varying temperature profiles, transient operating conditions, and diverse driving patterns. Models must now incorporate real-world factors such as cold-start performance, thermal cycling effects, and aging characteristics under actual usage scenarios.
California's Advanced Clean Cars II program and similar zero-emission vehicle mandates are pushing modeling requirements toward hybrid and electric vehicle applications, where catalytic converters operate under intermittent conditions with frequent thermal cycling. These regulations require models to predict performance during engine restart scenarios, extended idle periods, and varying thermal management strategies unique to electrified powertrains.
Onboard Diagnostics (OBD) regulations mandate continuous monitoring of catalytic converter efficiency, creating demand for simplified yet accurate models suitable for real-time implementation in engine control units. These embedded models must balance computational efficiency with sufficient accuracy to detect converter degradation and ensure compliance throughout vehicle lifetime, typically requiring processing capabilities under 10 milliseconds per calculation cycle.
Future regulations targeting particulate number emissions and ammonia slip are driving development of multi-pollutant models capable of simultaneous prediction across various emission species. The anticipated introduction of Euro 7 standards will likely impose even stricter limits while extending durability requirements, necessitating models that can accurately predict long-term performance degradation and aging effects over extended mileage intervals exceeding 200,000 kilometers.
Machine Learning Applications in Catalytic Performance
Machine learning has emerged as a transformative technology in catalytic converter performance modeling, offering unprecedented capabilities to handle the complex, non-linear relationships inherent in catalytic processes. Traditional empirical models often struggle with the multidimensional nature of catalytic systems, where temperature, flow rates, gas compositions, and catalyst aging interact in intricate ways that are difficult to capture through conventional mathematical approaches.
Neural networks, particularly deep learning architectures, have demonstrated exceptional promise in modeling catalytic converter behavior. These systems excel at identifying hidden patterns within large datasets of operational parameters and performance metrics. Convolutional neural networks have been successfully applied to analyze spatial temperature distributions across catalyst substrates, while recurrent neural networks effectively capture temporal dependencies in catalyst performance degradation over time.
Support vector machines and random forest algorithms have proven particularly effective for classification tasks in catalytic systems, such as predicting catalyst failure modes or identifying optimal operating conditions. These methods demonstrate robust performance even with limited training data, making them valuable for scenarios where extensive experimental datasets are unavailable or costly to obtain.
Ensemble learning approaches combine multiple machine learning models to enhance prediction accuracy and reliability. By integrating different algorithmic strengths, ensemble methods can provide more robust performance predictions while quantifying uncertainty levels, which is crucial for safety-critical automotive applications where catalytic converter failure could lead to emissions violations.
Real-time adaptive learning systems represent an advanced application where machine learning models continuously update based on incoming sensor data. These systems can adapt to changing catalyst conditions, fuel compositions, and driving patterns, maintaining model accuracy throughout the catalyst's operational lifetime. Such adaptive capabilities are particularly valuable for addressing catalyst aging effects and performance drift.
Feature engineering and dimensionality reduction techniques, including principal component analysis and autoencoders, help identify the most influential parameters affecting catalytic performance. This approach not only improves model efficiency but also provides insights into fundamental catalytic mechanisms, bridging the gap between data-driven modeling and physical understanding of catalytic processes.
Neural networks, particularly deep learning architectures, have demonstrated exceptional promise in modeling catalytic converter behavior. These systems excel at identifying hidden patterns within large datasets of operational parameters and performance metrics. Convolutional neural networks have been successfully applied to analyze spatial temperature distributions across catalyst substrates, while recurrent neural networks effectively capture temporal dependencies in catalyst performance degradation over time.
Support vector machines and random forest algorithms have proven particularly effective for classification tasks in catalytic systems, such as predicting catalyst failure modes or identifying optimal operating conditions. These methods demonstrate robust performance even with limited training data, making them valuable for scenarios where extensive experimental datasets are unavailable or costly to obtain.
Ensemble learning approaches combine multiple machine learning models to enhance prediction accuracy and reliability. By integrating different algorithmic strengths, ensemble methods can provide more robust performance predictions while quantifying uncertainty levels, which is crucial for safety-critical automotive applications where catalytic converter failure could lead to emissions violations.
Real-time adaptive learning systems represent an advanced application where machine learning models continuously update based on incoming sensor data. These systems can adapt to changing catalyst conditions, fuel compositions, and driving patterns, maintaining model accuracy throughout the catalyst's operational lifetime. Such adaptive capabilities are particularly valuable for addressing catalyst aging effects and performance drift.
Feature engineering and dimensionality reduction techniques, including principal component analysis and autoencoders, help identify the most influential parameters affecting catalytic performance. This approach not only improves model efficiency but also provides insights into fundamental catalytic mechanisms, bridging the gap between data-driven modeling and physical understanding of catalytic processes.
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