Status: Available for Licensing and/or Collaboration

Background

Monitoring carbon fixation in photosynthetic microorganisms is essential for optimizing biomass production. Traditional methods like TSS and chlorophyll measurements are slow and unreliable for real-time control.

Unlike models built from internal data using regression or AI, this method is grounded in carbonate chemistry, making it easier to understand and apply across different cultivation systems without needing extensive new data. It uses pH measurements to estimate CO₂ transfer and infer carbon fixation rates with greater accuracy and adaptability. This science-informed approach can be embedded into existing platforms to enhance process control and system performance.

Technology Overview

Researchers at Montana State University and the University of Toledo have developed a novel method to estimate carbon fixation rates in aqueous photosynthetic systems using pH measurements. The model uses real-time pH data and initial water chemistry to calculate CO₂ transfer and infer fixation rates based on carbonate equilibrium shifts during algal growth. This enables rapid, non-invasive monitoring of algal growth, respiration, and productivity.

The model is adaptable to various environmental conditions (e.g., temperature, salinity) and cultivation systems (e.g., raceways, flasks) and has been validated in both abiotic and biotic experiments. It supports direct air capture (DAC) of CO₂ in high pH/high alkalinity systems, reducing the need for CO₂ sparging and enhancing cultivation stability.

This is a collaborative opportunity to co-develop a customized algorithm that integrates directly into a company’s existing software platforms. Rather than creating a stand-alone product, the model can be embedded into current control systems to enhance existing technology. This approach offers strategic advantages, making the solution more attractive and licensable as an improvement to current operations.

Graph of pH measurements over time as predicted by the model and compared to observed data

pH measurements over time as predicted by the model (blue) and compared to observed data (red). Fixation rates are inferred from pH changes, which reflect CO2 uptake and carbonate equilibrium shifts. 

Benefits

  •  Non-invasive and low-cost: relies on pH sensors already common in cultivation systems, reduces CO2 input costs
  • Improves process control: enables dynamic optimization of growth conditions
  • Adaptable: model can be tuned for different temperatures, salinities, and system geometries
  • Integration-ready: can be embedded into existing software platforms for enhanced functionality

Applications

  • Algal biofuel and bioproduct production/carbon capture and utilization (CCU) systems
  • Environmental monitoring of photosynthetic systems
  • Academic and industrial R&D in algal physiology and modeling
  • Integration into bioreactor control systems for real-time feedback

Opportunity

Available for exclusive license: predictive model, algorithms, and supporting data. This is an opportunity to build customized know-how into a company’s existing technology. The model can be tailored to specific operational needs and embedded into proprietary software systems to enhance current capabilities.

We welcome collaboration to refine the model, develop user interfaces, and validate performance in real-world systems. Patent prosecution will be pursued jointly with interested partners, ensuring alignment with commercialization goals.

IP Status

Provisional patent application filed. Available for licensing and/or collaboration.

Contact

Tess Kirkpatrick

(404) 994-7775

tesskirkpatrick@montana.edu