Phase II SBIR - An Artificially Intelligent Raman Spectrometer for Aviation Jet Fuel Quality Testing in a Field Setting

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Related Phase I SBIR Research - Artificially Intelligent RAMAN Spectrometer to Detect Chemical and Biological Pathogens in a Field Setting

Aircraft fuel quality errors aren’t just inconvenient—they can be catastrophic. Fuel contaminated by hydraulic fluids, PAOs (polyalphaolefins), or degraded via thermal stress can knock out critical systems.

Traditional lab testing is slow (days to weeks) and expensive (hundreds to thousands of dollars per sample).

For forward-bases or expeditionary operations using the Agile Combat Employment (ACE) model, waiting for lab results isn’t viable. Fuel problems must be caught in the field.

See: "Bad Fuel" May Have Caused Back-To-Back Nimitz Aircraft Crashes"

Developing a handheld, low-cost device (targeting under ~$1,500) that uses Raman spectroscopy + AI/federated learning to spot and quantify contaminants in jet fuel, in the field.

Detects things like PAO contamination, microbial growth (in fuel/water interfaces), and indicators of thermal degradation.

The system promises near-real-time results (“minutes”) instead of days, and dramatically lower cost per test (aim: ~1 cent per sample) versus the ~$300-$2,300 current cost.

Small form factor (30 grams, key fob sized)

Rapid, in-field verification of jet fuel quality, enabling faster turnarounds and higher aircraft readiness.

Reduced mission risk from fuel contamination or degradation.

Cost savings and logistical simplification: less reliance on distant labs, less downtime.

Potential commercial uses beyond military: aviation refineries, petrochemical, sustainability monitoring.

If you're tasked with fuel assurance, maintenance readiness, or expeditionary support: this tool changes the game.

For procurement: a clear pathway to a field-deployable solution justifies investment and sole-source opportunities.

For operations: fewer delays, fewer mission interruptions due to fuel testing bottlenecks.

Cost Reduction: At under $800, Blaise™ delivers precise fuel analysis at a fraction of the cost of traditional laboratory-based methods, which can exceed $30,000 per system.

Operational Efficiency: The compact and ruggedized Blaise™ replaces bulky, inaccurate field kits and eliminates 30–45-day mission delays caused by PAO contamination in fifth-generation aircraft.

Federated Learning Integration: Enables real-time adaptation to evolving fuel contaminants without compromising security, reducing reliance on expensive and time-consuming central laboratory testing.

Predictive Maintenance: Blaise™ identifies contaminants like Polyalphaolefin (PAO), microbial growth, and potentially in the near future - thermal degradation indicators, preventing costly engine wear and reducing sustainment costs in high-performance aircraft. Microbial contamination detected includes the growth of bacteria and/or fungi in water deposits within fuel systems. Growth of these microorganisms has a consistency of slime or mayonnaise at the fuel/water interface. This contamination can plug aircraft filters, cause quantity probe malfunctions, and corrode integral fuel tanks. While fuel system icing inhibitor acts as a biostat, microbial contamination is most effectively controlled by keeping water out of the fuel system.

Expedited Decision-Making: Field-ready fuel analysis enhances Agile Combat Employment (ACE) operations, ensuring mission readiness and reducing fuel-related downtime.

Military Applications: Supports expeditionary fuel testing, jet fuel analysis, and missile propellant verification, directly improving F-35 fleet readiness and eliminating $81,000 per-unit defuel cart costs.

Commercial Adoption: Streamlines on-site fuel verification for refineries, aviation, and petrochemical industries, ensuring compliance with ASTM, DEF STAN, and MIL-DTL standards.

Sustainability and Compliance: Reduces waste and environmental impact by enhancing fuel efficiency and contamination prevention, aligning with cost-effective, sustainable operation requirements.

Artificial Intelligence (CNN/RNN), Android ATAK, Federated Learning Marketplace, Machine Learning, Microsoft Azure, signal processing (Wavelet and Fourier transformation), Raman Spectroscopy

Contested logistics (fuel and water quality testing), Chemical, Biological, Radiological, and Nuclear (CBRN) detection, explosives detection, Expeditionary Environments

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