Who We Are
AeroPhotonix LLC is a technology startup developing advanced optical sensing systems to improve the measurement and understanding of atmospheric particles. Located in Reno, Nevada, USA, the company is developing next-generation optical instruments for particulate matter analysis, bioaerosol detection, and remote sensing to support environmental monitoring, scientific research, and defense applications.
Our mission is to develop next-generation aerosol measurement technologies that advance how airborne particles are measured, identified, and monitored, translating laboratory research into real-world sensing platforms.
Our Vision
AeroPhotonix envisions becoming a global leader in intelligent aerospace photonics by redefining how airborne particles are measured, understood, and managed. We aim to advance beyond conventional particulate monitoring toward physics-driven, AI-enabled optical sensing ecosystems that deliver real-time insight into particle size, morphology, composition, and radiative impact. By integrating wide-angle scattering, polarization analytics, multi-modal photonics, and embedded machine learning, we seek to empower climate research, space missions, environmental health protection, and defense readiness with next-generation, field-deployable sensing technologies.
Where Current Technologies Fall Short... and We Excel...
Accurate characterization of atmospheric aerosols remains a persistent challenge. Airborne particles vary widely in size, morphology, composition, and mixing state, yet many existing monitoring technologies rely on limited-angle optical measurements or bulk mass concentration estimates. As a result, these systems often provide incomplete information about particle properties and have limited capability to resolve morphology, polarization behavior, or biological signatures.
AeroPhotonix is working to develop next-generation optical sensing approaches designed to expand how airborne particles are measured and interpreted. Our research focuses on combining wide-angle light scattering observations, polarization-resolved detection, and complementary optical diagnostics to better capture particle–light interactions across a broad angular range.
By observing scattering signals across multiple angles and polarization states, future systems are intended to retrieve additional information related to particle structure, size distribution, and refractive characteristics. This approach aims to move beyond traditional intensity-based particulate monitoring toward measurements that are more closely linked to aerosol microphysical properties.
Our development strategy emphasizes an integrated framework in which sampling, optical measurement, calibration, and data analysis are designed together. Computational approaches, including machine learning and inversion methods, are being explored to assist in interpreting complex scattering signatures and improving particle classification.
The technology is currently under laboratory validation, where key optical measurement concepts and data analysis approaches are being tested. AeroPhotonix plans to advance these developments toward prototype instruments and eventual field-deployable systems. The objective is to enable new sensing capabilities for atmospheric research, environmental monitoring, and related applications across ground and airborne platforms.
Our Technology/Core Innovations
Elastic Light Scattering Systems: Offers the broadest angular coverage (0.32°–179°), surpassing the limitations of current systems thereby yielding More accurate particle size, morphology, and refractive index retrieval.
Polar Nephelometry (Phase Function (F11) + Linear Depolarization Ratio (LDR) + Degree of Linear Polarization (DoLP) + Machine Learning (ML))
Ambient Aerosol Detection (LIF + ML)
AI-Driven Inversion Algorithms ( Real-time size retrieval, Particle classification, Multi-modal data fusion and Noise suppression)
Multi-modal Sensing Techniques (Integration of Static Light Scattering, Holography, Fluorescence, and AI)
Angularly Resolved Multi-Parameter Measurement (F11, LDR, DoLP ) at Multiple Angles Simultaneously
AI-based Inversion