Interactive Advanced MOF Gas Adsorption Simulator

Advanced MOF Gas Adsorption Simulator

Developed By : Ir. MD Nursyazwi

An Homage to the Reticular Chemistry of Susumu Kitagawa

Operational Protocol: Defining the Adsorption Environment

This module provides a predictive simulation of Gas Adsorption on Metal-Organic Frameworks (MOFs) using a semi-empirical Langmuir model. It facilitates the determination of both intrinsic adsorption capacity and the requisite MOF mass to capture a specific volume of target gas at defined conditions.

Honorable Mention: This simulation and its core MOF dataset are directly inspired by the pioneering reticular chemistry and functional porous polymers first reported by Professor Susumu Kitagawa and his team.

1. Select the MOF structure (e.g., PCN, MOF-5) and the Gas Adsorbate (e.g., CO₂, CH₄).
2. Specify the experimental Equilibrium Pressure (bar) and Absolute Temperature (K).
3. Input your Target Gas Volume (V_target, L) for sequestration or separation.
4. Execute the "Run Simulation" command and analyze the quantitative and visual outputs.

Input Parameters: Materials and Thermodynamics

Metal-Organic Framework (MOF) Structure Kitagawa PCN-11 (Large Pore/High Surface) Kitagawa PCN-61 (Medium Pore/Kinetic Separation) Cu-BTC (HKUST-1 - Benchmark Example) MOF-5 (Zn-terephthalate - High Volume Capacity)

Target Gas Adsorbate Carbon Dioxide (CO₂) Methane (CH₄) Hydrogen (H₂) Nitrogen (N₂)

Equilibrium Pressure (P) [Bar]

Absolute Temperature (T) [Kelvin (K)]

Target Gas Volume (V_target) [Liters (L) at STP]

Simulating adsorption and mass balance...

Multiscale Visualization: Unit Cell and Engineering Scale

The 3D view presents a stylized MOF Unit Cell (molecular scale) clearly separated from the Engineering Mass Indicator (process scale). The height of the indicator is dynamically scaled logarithmically against the required MOF mass in kilograms.

Visualization Legend

Metal Nodes (Secondary Building Units)

Organic Linkers (Bridging Ligands)

Adsorbate Gas Molecules

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Required MOF Mass Indicator (log(M_MOF))

Interact: Click and drag inside the 3D window to control the rotational orientation of the MOF structure.

Quantitative Output: Performance Metrics and Mass Balance

Equilibrium Adsorption Capacity (Q_eq) at P, T [mmol/g]: ---

Framework Saturation Percentage (Relative to Q_sat): ---

Calculated MOF Mass (M_MOF) to Adsorb V_target [kg]: ---

Overall Engineering Verdict: ---

Suggested Engineering Solutions:

Isotherm Analysis: Adsorption Curve Visualization

The Isotherm plots the quantitative relationship between the Adsorption Capacity (Q) and the Equilibrium Pressure (P) at the constant simulation temperature (T). The blue point indicates your current simulation condition.

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Physicochemical Basis: Model and Theory

Reticular Chemistry: MOFs are highly crystalline porous polymers, characterized by their modular assembly from inorganic Secondary Building Units (SBUs) and organic linkers. This tunability is key to designing materials with ultra-high surface areas (up to 7000 m²/g) for selective gas capture.

Engineering Methodology

The simulator determines the required material mass by coupling two fundamental principles:

1. Capacity Modeling: We employ a temperature-corrected Langmuir Adsorption Isotherm (Equation: Q_eq = (Q_sat * K' * P) / (1 + K' * P)) to predict the equilibrium adsorption capacity in [mmol/g].
2. Mass Balance: The total moles of gas to be captured (n) is calculated via the Ideal Gas Law (PV = nRT). The required mass is then computed by dividing n by the Q_eq of the chosen MOF.

The Gas Constant used for these calculations is R = 0.08314 L ⋅ bar ⋅ mol^-1 ⋅ K^-1.

Computational Integrity: Ensuring Numerical Accuracy

To guarantee the results are reliable for engineering applications, the simulator adheres to rigorous computational protocols, focusing on data precision and defensive coding.

1. Data Precision and Floating-Point Arithmetic

The most critical aspect of calculation in JavaScript is handling the limitations of the IEEE 754 floating-point standard. Standard numbers cannot precisely represent certain decimal fractions, which can lead to small, unexpected rounding errors.

The Problem (0.1 + 0.2 is not exactly 0.3)

For example, in standard JavaScript arithmetic:

0.1 + 0.2 in JavaScript ≈ 0.30000000000000004
            

The Solution: Scaling to Integers (Preferred Method)

For high-precision financial or quantitative applications, the preferred methodology is to convert values to their smallest integer unit before calculation to perform precise arithmetic, although here we rely on aggressive rounding for display (toFixed()).

2. State Management for Calculation Integrity

A robust calculation methodology requires a clear and traceable data flow, ensuring all results are consistent with the current inputs.

The Principle of Derived State

Values that are calculated from other inputs (like Q_eq or M_MOF) are derived and are never stored as mutable state. They are computed every time an input (like Pressure or Temperature) changes, guaranteeing they are always consistent.

Data Flow Checks

All raw input data from the form fields is cleaned and validated before being used in any calculation:

• Type Casting: Inputs are parsed using parseFloat() to ensure they are treated as numerical values, not strings.
• Input Clamping: Values are checked to ensure they are physically valid (e.g., Pressure and Temperature must be greater than zero).

3. Handling Edge Cases and Defensive Coding

The application employs defensive coding practices to prevent crashes and ensure meaningful output when inputs are invalid:

Edge Case	Problem Description	Methodological Solution
Division by Zero	Results in Infinity or a runtime error (e.g., if Qeq is zero).	Guard Clause: The code checks if the capacity is zero before calculating the required mass. If true, it returns Infinity (displayed as 'IMPRACTICAL') to signal an an impossible capture condition.
Null or NaN Inputs	Results in NaN if the user enters non-numeric text or leaves a field blank.	Input Sanitization: A validation check at the start of runSimulation() ensures the calculation only proceeds if all inputs are valid numbers (> 0).
Negative Results (Context Dependent)	Capacity, pressure, and temperature cannot be negative.	Input Clamping/Validation: Min constraints are set on the input fields, and the simulation logic further verifies that all inputs are positive before calculation.

References

• Kitagawa, S. et al. (2004). Functional porous coordination polymers. Angew. Chem. Int. Ed. 43, 2334–2375. (Core Reference: Pioneering work on versatile MOF designs, upon which the selection of PCN structures in this simulator is based).
• Yaghi, O. M. et al. (1999). Reticular chemistry and metal-organic frameworks. Nature 400, 417–423. (Pioneering work on reticular synthesis).
• Sumida, K. et al. (2012). Carbon Dioxide Capture in Metal-Organic Frameworks. Chem. Rev. 112, 724–778. (A comprehensive review on gas adsorption applications).