I need a skilled software developer to create a simulation platform that models photocatalytic degradation kinetics based on published literature data. This is NOT experimental work—all inputs are literature-derived; the software translates mathematical models into computational simulations. Project Scope Deliverables 1. Kinetic Simulation Module • Implement Langmuir-Hinshelwood rate equation: r = (k_r × K_ads × C) / (1 + K_ads × C) • Implement pseudo-first-order approximation: ln(C₀/Cₜ) = k_app × t • Solve differential equations (ODE solvers: ode45 or scipy.integrate.solve_ivp) • Generate concentration-time profiles (0–180 minutes) 2. Parametric Sensitivity Analysis (OFAT) • Vary initial [APAP]: 1, 5, 10, 20, 30, 50 mg/L • Vary pH: 3, 5, 6.5, 8, 10, 11 (apply pH adjustment factors from literature) • Vary catalyst dosage: 0.5, 1.0, 1.5, 2.0 g/L (account for optical shielding) • Vary light intensity: 20, 45, 65, 100 W/m² • Output: k_app values, % removal at fixed times (30, 60, 90 min), half-lives 3. Model Comparison & Validation • Simulate all three catalysts (TiO₂, ZnO, TiO₂/ZnO/WO₃) under identical conditions • Compare degradation curves side-by-side • Calculate synergy factor for ternary composite • Validate simulated k_app against published literature values (±15% tolerance) 4. Data Visualization • Concentration-time curves (C_t vs. time) • Pseudo-first-order plots (ln(C₀/C_t) vs. time) with R² regression statistics • Sensitivity analysis plots (k_app vs. pH, dosage, [APAP]) • Response surface contours (if 2D parameter variation) • Comparative bar charts (single-oxide vs. composite performance) 5. Documentation & Reproducibility • Well-commented code (clear variable names, equation derivations) • User manual explaining input parameters and output interpretation • Example input files with literature-based parameter sets • CSV export of all simulation results Input Parameters & Data Literature-Derived Kinetic Parameters (You will provide): Single-Oxide Catalysts: Catalyst k_app (min⁻¹) K_ads (L/mg) pH optimum Dosage range (g/L) TiO₂ 0.017–0.060 0.022–0.083 6.5 0.5–2.0 ZnO 0.009–0.047 0.020–0.070 6.5 0.5–2.0 WO₃ 0.009–0.015 0.018–0.060 6.5 0.5–2.0 Composite: Catalyst k_app (min⁻¹) Enhancement (%) TiO₂/ZnO/WO₃ (1:1:1) 0.035–0.050 +60–80% vs. single-oxide pH Adjustment Factors (f_pH): • pH 3: 0.65 • pH 5: 0.85 • pH 6.5: 1.00 (reference) • pH 8: 0.90 • pH 10: 0.70 • pH 11: 0.60 Dosage Adjustment (f_light): • 0.5 g/L: 0.67 • 1.0 g/L: 1.00 (reference) • 1.5 g/L: 0.95 • 2.0 g/L: 0.90 Technical Requirements Programming Language & Environment • Preferred: Python 3.8+ (SciPy, NumPy, Pandas, Matplotlib) • Alternative: MATLAB R2023a or later • Operating System: Windows, macOS, or Linux compatible • Dependencies: Clearly documented; use standard, open-source libraries Functionality Requirements 1. ✓ Read input parameters from CSV or Excel files 2. ✓ Solve ODE (pseudo-first-order kinetics) with default and user-defined parameters 3. ✓ Apply pH and dosage adjustment factors dynamically 4. ✓ Calculate % removal, half-life, k_app values 5. ✓ Generate publication-quality plots 6. ✓ Export results as CSV and high-resolution PNG/PDF plots 7. ✓ Validate regression R² values (must be ≥0.95 for pseudo-first-order fits) 8. ✓ Error handling for invalid inputs (negative concentrations, impossible pH, etc.) Code Quality • Clean, modular design (separate functions for: ODE solving, parameter adjustment, plotting, data export) • Inline comments explaining mathematical equations • Unit testing for key functions • README with installation and usage instructions Timeline & Deliverables Phase Deliverable Timeline 1 Core ODE solver + parameter input module Week 1 2 Sensitivity analysis (OFAT) implementation Week 1–2 3 Plotting & visualization module Week 2 4 Model validation + data export Week 2–3 5 Documentation, testing, finalization Week 3 Total Duration: 3 weeks Delivery Format: Complete Python/MATLAB package + documentation + example data Budget & Payment • Budget: my budget—suggest USD 100–200 depending on scope and experience • Payment Terms: 50% upon contract signing, 50% upon final delivery • Revision Policy: 2 rounds of free revisions included Ideal Candidate Profile ✓ Required: • 3+ years experience with Python (SciPy, NumPy, Matplotlib) OR MATLAB • Proficiency in numerical methods (ODE solving, curve fitting, regression) • Experience with data visualization and scientific computing • Strong coding documentation practices ✓ Nice-to-Have: • Background in environmental engineering, chemistry, or physics • Familiarity with photocatalysis or water treatment literature • Prior experience with kinetic modeling or process simulation • GitHub portfolio with clean, documented code examples ✓ Essential Soft Skills: • Clear communication (for clarifying requirements) • Ability to work with mathematical/scientific documentation • Responsive to feedback and iterative improvements