Computational
Patinas
Environmental Analysis of Patinas
Predicting how architectural surfaces transform through environmental exposure
Zahner R&D · Summer Internship 2025
Role: Research & Development · Hardware Systems · Environmental Sensing · Computational Workflow Design · Data Infrastructure · Experimental Design
Team: Hana Khurshid, Viola Tan
Computational Patinas investigates how architectural metal surfaces evolve over time under real environmental conditions. Developed during a Research & Development internship at Zahner, the project focuses on building instrumented systems that capture, quantify, and predict patina aging across climates.
Rather than treating patinas as static finishes, this work reframes them as dynamic material systems shaped by environmental exposure, application methods, and time. By combining automated imaging, environmental sensing, accelerated weathering, and data infrastructure, the project establishes a foundation for predictive, data-driven surface design in architecture.
Patinas are widely used in architectural applications, yet their long-term behavior in exterior environments remains difficult to predict. UV exposure, humidity, pollutants, temperature cycling, and application methods can all alter surface appearance and performance over time. This uncertainty complicates material guarantees, slows innovation, and limits confidence in specifying new finishes.
Four out of Eight Legacy Patinas
System Architecture
The project was structured as an integrated testing and data pipeline consisting of:
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Automated imaging systems for consistent visual capture
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Accelerated weathering protocols to simulate extreme climates
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Long-term outdoor exposure studies for calibration
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Environmental sensor arrays measuring UV, humidity, temperature, pollutants, and rainfall
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Cloud-based infrastructure for data storage and analysis
Together, these components form a scalable framework for studying surface evolution across both controlled and real-world conditions.
Imaging & Automation
Custom imaging rigs were designed to eliminate variability in lighting, positioning, and camera settings. All images were captured with fixed parameters and uploaded automatically, ensuring reliable comparison across time and test conditions.
Fabricating mounting the camera above the lightbox.
Rhino model of jig that was fabricated using the shop mill.
Environmental Testing (Accelerated + Natural)
Accelerated Weathering
Accelerated testing simulated hot-humid, hot-dry, cold-humid, and UV-intensive climates using modified ASTM protocols. These tests enabled rapid comparison across surface treatments and environmental stressors.
Natural Weathering
Outdoor exposure systems captured real-time aging behavior, providing calibration data for accelerated tests and grounding predictions in real environmental conditions.
Climate Zone Map with 6 main climate zone areas to test for
Testing the worst case in a cluster to calibrate other areas in the same zone
Accelerated Weathering
To simulate these environments in the lab, we relied on equipment from Q-Lab, a manufacturer specializing in accelerated weathering chambers. Zahner already had a QUV Chamber, which typically runs the ASTM G154 cycle: eight hours of UV light at 60°C, followed by four hours of condensation at 50°C. This 12-hour cycle approximates hot-humid conditions like those found in Miami or Key West, Florida. Daily imaging was integrated into this cycle, feeding our dataset for later analysis.
Accelerated Testing Equipment
QUV Accelerate Chamber
Building a Custom Freeze–Thaw Chamber
Freeze–thaw cycling, a critical stressor in cold climates, could not be reproduced using QUV or Q-Sun equipment. To address this gap, I adapted the ASTM C666 concrete standard for patina testing, defining controlled freeze and thaw intervals.
Lacking a dedicated chamber, I designed and built a custom freeze–thaw system using a chest freezer, heating pads, and an Inkbird ITC-308 thermostat. By exploiting a configuration loophole in the controller’s software, I enabled automated switching between temperature extremes, effectively converting a low-cost consumer device into a programmable freeze–thaw chamber.
Custom freeze-Thaw Chamber
Hacking into the freezer with Inkbird
Shop Drawings for fabricating Freezer Baskets
Our final set-up
Natural Weathering
While accelerated testing gave us snapshots of patina performance under controlled conditions, it could not be directly calibrated to real-world time. One cycle in a QUV chamber, for instance, might represent weeks, months, or even years of outdoor exposure, but without baseline field data, there was no way to know. Natural weathering was therefore essential as a calibration benchmark. By placing samples outdoors and monitoring them continuously, we could begin to map accelerated test cycles against real-time change.
Outdoor Raspberry Pi Setup
Consistent Imaging Outdoors
Consistent imaging was just as important outdoors as in the lab, though the challenges were different. We identified four key requirements:
• Automation: Images had to be captured and uploaded without manual intervention.
• Quality: Each image needed to resolve all patina samples clearly, without distortion.
• Durability: The setup had to withstand continuous use and variable weather.
• Weatherproofing: Protection against rain, wind, and dust was critical.
Outside preliminary setup
Raspberry Pi & Camera setup in a waterproof surveillance camera casing
Temporary mounting prototype to mount the camera
Testing image quality and QR codes readability (taken at 12pm)
Testing image quality and QR codes readability (taken at 12pm)
Sensing & Data Infrastructure
Multi-sensor arrays recorded UV radiation, temperature, humidity, surface temperature, rainfall, and air quality at regular intervals. All data was timestamped, geotagged, and uploaded to cloud storage, creating a synchronized dataset linking surface change to environmental exposure.
Sensors connection to Raspberry Pi
Appending data and uploading it to S3
Weatherproofing & Reliability
Protecting the sensor array from the elements required as much ingenuity as coding it. We housed the setup in an IP66-rated enclosure, but modifications were necessary. Because ABS plastic blocks UV, I embedded quartz glass windows for the UV and IR sensors, sealed with silicone rings and 3D-printed bezels. Cable glands were added for power leads, and Gore-Tex breathable vents were drilled in to ensure accurate humidity and pressure readings. The YL83 rain detector was extended outside the box with heat-shrink tubing to sit directly on top of the housing.
Sensor box prototype
Computational Analysis & Visualization
Image data was processed using color difference metrics (ΔE, ΔL, ΔA, ΔB) to quantify surface change over time. These metrics were integrated into interactive visual tools that allow designers to explore how surfaces evolve across climates and test conditions.
G154 Results
Interactive App: Delta E Analysis
Interactive App: Delta L – Lightness (Dirty Penny)
Interactive App: Delta A – Red-Green (Spectura)
Map-based web application
Knowledge Graph & Predictive Framework
Experimental results, environmental data, and process variables were organized into relational databases and knowledge graphs, establishing a foundation for future machine-learning models that connect material behavior, climate, and fabrication parameters.
Knowledge graph format (Left) | Example hypothesis (Right)
SQL Database
Neo4j Visualization of SQL Database
Outcome
This work establishes a repeatable, scalable framework for studying architectural surface aging. By embedding sensing, automation, and computation directly into material research, Computational Patinas shifts surface design from reactive observation toward predictive, data-driven decision-making