SBIR Phase I: Combating Pathogens, Helios-1 Onsite Universal Detection

Award ID: 2304483

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is protection against pathogen-related infections. Currently, detection tests for pathological agents are laborious, time-consuming, expensive, and require advanced technical expertise to conduct. The proposed portable, onsite pathogen detector will allow for fast, specific, sensitive, and cost-effective pathogen tests that can be conducted with minimal personnel training and equipment. The solution is intended to be used at healthcare centers, transport nodes, defense facilities, and any other site where the spread of infectious diseases is a possibility. This technology will benefit the populations health and welfare, by facilitating the implementation of pathogen detection routines that reduce the risk of large-scale infections. Such infections disproportionally affect under-represented groups. The solution will also improve the national defense against bioterrorism, since the proposed technology could be as standard as a typical metal detector used in large, populated venues, on the battlefield protecting troops, or at airports to keep the traveling public safe. The nations economic competitiveness may also improve, since the proposed solution could mitigate and even avoid the economic consequences of a health crisis. The proposed project seeks to prove that Matrix Assisted Ionization can be coupled with Ion-Mobility Spectrometry (MAI-IMS) for pathogen detection and identification. The recent pandemic outbreak has demonstrated the necessity of rapid, on-site, and accurate pathogen detection devices. The proposed method is to use the existing IMS technology and modify it to detect pathogens by fabricating a Matrix assisted ionization vault (Helios-1) that overcomes the biomolecule volatility restriction of all current ion mobility spectrometers. A crucial technical hurdle is finding the device's optimal ionization and operational environment. To overcome this challenge, the most similar conditions to mass spectrometry must be found, which will involve experimental tests to determine the adequate environmental conditions and the engineering modifications of the MAI extension chamber to adapt IMS for non-volatile biomolecule detection. Standardize organism sample conditions and protocols are also needed. This challenge represents a critical step to prevent variation caused by the extraction of the sampling procedure. This challenge will be tackled by testing different extraction procedures until they meet the criteria for satisfactory performance. Additionally, machine learning algorithms will be employed for pathogen recognition. All of the above will help prove the feasibility of the proposed MAI-IMS-based pathogen detection and identification platform. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Erik Pierstorff