SirenOpt is a commercial spin-out from the Mesbah Lab at the Chemical & Biomolecular Engineering Department at the University of California, Berkeley. At the Mesbah Lab SirenOpt’s co-founders (Jared O’Leary, Ali Mesbah, and Joel Paulson) were investigating learning-based methods for characterizing, modeling, and controlling intrinsically stochastic systems with nonlinear dynamics, including systems of cold atmospheric plasmas (CAPs),weakly ionized gases operating at room temperature.

Though CAPs have been deployed in industry for decades to treat and clean complex materials, SirenOpt’s co-founders realized that they could maintain CAPs at extremely low energy levels that could allow the CAPs to non-destructively interact with these complex materials. They further realized that the resulting CAP-material interactions could reveal vast amounts of visual, electrical, thermal and spectroscopic data about materials and could create uniquely distinctive, multifaceted material fingerprints in real-time.

Thus, the idea of SirenOpt was born, as SirenOpt’s co-founders “accidentally” discovered the CAP system they were investigating for treatment applications could be combined with physics-informed machine learning techniques to develop a real-time, non-destructive, advanced materials manufacturing intelligence platform.

Early traction with Bay Area battery start-up companies then led SirenOpt to be accepted into the Berkeley SkyDeck Accelerator and the Activate Fellowship.  Support from these two programs allowed SirenOpt to progress quickly, culminating in a $6.6m Seed Funding Round that closed in July 2024.  

SirenOpt’s core technology expertise and intellectual property includes low-energy plasma control, machine learning and optimized decision making for advanced process and equipment control.