Photocatalytic Degradation Kinetics Simulation Software
Budget: $30 – $250 USD
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
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