How to Use It?

Getting Started

Using the FC-Catalyst Prediction Tool is straightforward. This guide will walk you through the process of making your first prediction and interpreting the results.

Step-by-Step Guide

Step 1: Navigate to the Prediction Page

Click on the "Predict" link in the top navigation bar, or go directly to the Predict page.

Step 2: Enter Catalyst Parameters

Fill in the required input fields with your catalyst parameters:

Catalyst Composition

Enter the catalyst material composition. Common examples include:

• Pt/C - Platinum on carbon support
• PtRu/C - Platinum-ruthenium alloy on carbon
• PtCo/C - Platinum-cobalt alloy on carbon
• Pd/C - Palladium on carbon support

Example: Pt/C

Loading Amount (mg/cm²)

Specify the catalyst loading amount on the electrode surface in milligrams per square centimeter.

• Typical range: 0.1 - 1.0 mg/cm²
• Common value: 0.4 mg/cm²

Example: 0.4

Temperature (°C)

Enter the operating temperature of the fuel cell in degrees Celsius.

• Typical range: 60 - 90°C
• Optimal range: 70 - 85°C

Example: 80

Pressure (atm)

Specify the operating pressure in atmospheres.

• Typical range: 1 - 3 atm
• Common value: 1.5 - 2.0 atm

Example: 2

Humidity (%)

Enter the relative humidity percentage.

• Range: 0 - 100%
• Optimal range: 50 - 80%

Example: 75

Step 3: Submit for Prediction

Once all fields are filled:

1. Review your inputs to ensure they're correct
2. Click the "Predict" button
3. Wait for the prediction to process (typically less than a second)

Step 4: Interpret the Results

After submission, the prediction results will appear below the form with the following information:

Predicted Efficiency

The estimated catalyst efficiency as a percentage. Higher values indicate better performance.

• Range: 0 - 100%
• Good performance: >80%
• Excellent performance: >90%

Model Confidence

The confidence level of the prediction model, indicating how certain the model is about its prediction.

• Range: 0 - 100%
• High confidence: >85%
• Moderate confidence: 70 - 85%
• Low confidence: <70%

TIP

Higher confidence scores indicate that your input parameters are similar to the training data, making the prediction more reliable.

Recommendations

The tool provides actionable recommendations based on your input parameters. These suggestions can help you optimize catalyst performance:

• Temperature adjustments: Suggestions for optimal operating temperature
• Loading optimization: Guidance on catalyst loading amounts
• Humidity control: Recommendations for humidity levels
• Pressure optimization: Advice on operating pressure

Step 5: Experiment and Iterate

Use the tool to explore different parameter combinations:

1. Adjust one parameter at a time to see its effect on efficiency
2. Compare different catalyst compositions under the same conditions
3. Find optimal operating conditions for your specific catalyst
4. Use the Reset button to clear the form and start a new prediction

Example Walkthrough

Let's walk through a complete example:

Scenario: Testing a Platinum Catalyst

Input Parameters:

• Composition: Pt/C
• Loading Amount: 0.4 mg/cm²
• Temperature: 80 °C
• Pressure: 2 atm
• Humidity: 70 %

Expected Results:

• Predicted Efficiency: ~85-90%
• Model Confidence: ~85-95%
• Recommendations: Operating conditions are within optimal range

Scenario: Low Temperature Operation

Input Parameters:

• Composition: Pt/C
• Loading Amount: 0.4 mg/cm²
• Temperature: 50 °C (lower than optimal)
• Pressure: 2 atm
• Humidity: 70 %

Expected Results:

• Predicted Efficiency: ~70-75% (lower due to temperature)
• Recommendations: "Consider increasing temperature for better performance"

Tips for Best Results

Input Validation

• Ensure all fields are filled before submitting
• Use realistic values within the specified ranges
• Double-check units (mg/cm², °C, atm, %)

Parameter Selection

• Start with typical values if you're unsure
• Use the placeholder examples as guidance
• Refer to published literature for realistic parameter ranges

Interpreting Predictions

• Consider both efficiency and confidence scores
• Pay attention to recommendations for optimization
• Use predictions as guidance, not absolute truth
• Validate critical predictions with experimental data

Exploring Parameter Space

• Test multiple compositions under the same conditions
• Vary one parameter while keeping others constant
• Look for trends and patterns in the predictions
• Document interesting parameter combinations

Common Use Cases

1. Catalyst Selection

Compare different catalyst compositions to identify the most promising candidate:

• Test Pt/C, PtRu/C, and PtCo/C under identical conditions
• Compare predicted efficiencies
• Select the best performer for experimental validation

2. Operating Condition Optimization

Find optimal operating conditions for a specific catalyst:

• Fix the catalyst composition
• Vary temperature, pressure, and humidity
• Identify the parameter combination with highest efficiency

3. Loading Amount Optimization

Determine the optimal catalyst loading:

• Test different loading amounts (e.g., 0.2, 0.4, 0.6, 0.8 mg/cm²)
• Balance efficiency gains against material costs
• Find the point of diminishing returns

4. Sensitivity Analysis

Understand how sensitive performance is to parameter changes:

• Make small adjustments to each parameter
• Observe the impact on predicted efficiency
• Identify critical parameters that require tight control

Troubleshooting

Validation Errors

If you see error messages:

• "Field is required": Fill in all input fields
• "Value out of range": Adjust the value to be within acceptable limits
• "Invalid input": Ensure you're entering numbers in numeric fields

Unexpected Results

If predictions seem unusual:

• Verify your input parameters are realistic
• Check that units are correct
• Review the confidence score - low confidence may indicate unusual parameter combinations
• Consider that the model may not have been trained on similar conditions

Technical Issues

If the prediction page doesn't load or function properly:

• Refresh the page
• Clear your browser cache
• Try a different browser
• Check your internet connection

Next Steps

Now that you know how to use the tool:

• Explore the API Documentation to integrate predictions into your own applications
• Read What's This? to learn more about the underlying methodology
• Check out the About page to learn about the project background

Need Help?

If you have questions or encounter issues:

• Review this guide carefully
• Check the API Documentation for technical details
• Visit the project repository to report bugs or request features
• Contact the development team for support

Happy predicting! 🚀