Part 2: Kinetics, Stoichiometry & Data Analysis

Reaction Rate Definition (Species A)

- The rate of consumption of reactant A defined per unit volume and time: (-rA) = (1/V) * (dNA/dt), with SI units of mol/(m^3·s)

Arrhenius Equation

- Describes the temperature-dependence of the reaction rate constant: k(T) = k0 * e^(-Ea / RT)

Role of Catalysts in Kinetics

Catalysts increase reaction rates by altering the reaction pathway and reducing the activation energy barrier (Ea)

Elementary Reactions

Reactions in which the rate equation directly corresponds to the stoichiometric coefficients due to direct molecular collisions (molecularity equals reaction order)

Nonelementary (Overall) Reaction

Reactions that proceed via complex intermediate steps where the empirical rate law does not directly match the overall stoichiometric coefficients

Reaction Order (n) vs. Molecularity

Molecularity applies only to elementary steps (integer 1, 2, or 3), whereas reaction order is an empirical exponent applicable to all reactions (can be fractional)

Rate Constant Units

General dimensional units for an n-th order homogeneous reaction rate constant: [k] = (concentration)^(1-n) * (time)^-1

Limiting Reactant

The reactant that is completely consumed first, bringing the reaction to a complete stop and dictating the maximum product yield

Fractional Conversion (XA)

The fraction of the initial reactant concentration consumed during a reaction: XA = (NA0 - NA) / NA0 = (CA0 - CA) / CA0

Differential Method (Rate Equations)

A data analysis method involving plotting concentration vs. time, drawing a smooth curve, taking tangent slopes to find (-dCA/dt), and plotting log(-rA) vs. log(CA) to find order (n) and constant (k)

Integral Method (Rate Equations)

A data analysis method that assumes a reaction order (1st or 2nd), integrates the rate law, and tests for linearity by plotting ln(CA0/CA) or (1/CA - 1/CA0) versus time

Computational Non-Linear Curve Fitting - Using numerical solver tools (e.g., Excel Solver) to determine reaction parameters (n and k) by minimizing the sum of squared differences between experimental and theoretical concentration correlations[cite: 3].