Enabling Technologies for Bioengineering and Quantum Science-Based Sensors

Enabling Technologies for Bioengineering and Quantum Science- Based Sensors The U.S. DOE Office of Science Biological and Environmental Research (BER) program seeks to understand the basic biological, biogeochemical, environmental, and physical principles needed to fundamentally understand and predict processes found in nature occurring from the molecular to the global scale. Gaining a predictive understanding of these fundamental processes will help enable innovations in the design of new biosystems relevant to energy, chemical and materials production, new bioprocesses such as critical minerals and materials extraction and processing, and designing resilient crop production systems for a burgeoning bioeconomy. BER research also advances fundamental understanding of dynamic processes governing environmental systems through SI-driven and physical modeling coupled to field and process research to project impacts over time on crucial energy grid operation and infrastructure. This SBIR/STTR topic seeks projects focused on enabling technologies for quantum science-based sensors as well as enabling technologies to advance bioengineering. Projects should advance BER research and have commercial potential.

Areas of Interest Specific areas of interest include the following topics.

a. Quantum sensing for atmospheric and terrestrial measurements: Taking advantage of quantum effects such as entanglement, superposition, interference, squeezed states, and coherence may improve resolution and detection limits, signal-to-noise ratio, and long-term signal stability of measurements compared to existing classical methods [1, 2, 3, 4, 5]. While there have been significant advances in quantum sensing measurement approaches in theoretical and laboratory settings [3, 4, 5], there are still many challenges in deploying quantum sensing technologies in real-world environments [6].

Goals and Metrics • The objective of this priority area is to support the development and commercialization of new technologies that will enable the field deployment of robust quantum sensing instrumentation for atmospheric and terrestrial measurements under a wide range of environmental conditions. • Applications are sought for innovative technologies that exploit quantum phenomena to surpass limitations of conventional methods for improved ground-based or airborne measurements of atmospheric or terrestrial properties relevant to BER research on landatmosphere interactions. Properties relevant to this subtopic are those that provide information about the soil-plant-atmosphere continuum including moisture, heat, turbulence, and hydro-biogeochemical fluxes; evapotranspiration; vegetation composition; redox potential; biological volatile organic compounds; primary biological aerosol particles; and boundary-layer aerosol, cloud, and precipitation properties. • Applications must focus on the development of quantum sensing technologies suitable for real-world, field-deployable instruments by considering factors such as sensitivity to environmental noise or vibration, measurement stability, calibration, error correction, duty-cycle, electromagnetic interference, decoherence, and power/weight. • Applications may focus on hardening a quantum sensor to make it more suitable for inclusion in a field-deployable quantum sensing instrument, rather than developing a complete instrument, but must clearly explain how the proposed quantum component could eventually be utilized in a field-deployable instrument to improve measurements of one of properties of interest.

• Applications must indicate how the proposed quantum sensing technology would improve the measured accuracy, precision, detection limit, sensitivity, or other quality of the property of interest compared to existing conventional measurements.

References 1. Toward Quantum Enhanced Sensing and Measurements for Earth Observation in 2040, NASA Earth Science Technology Office Technical Interchange Meeting, NASA, Aug. 2024, https://esto.nasa.gov/files/NASA-QuantumSensing-TM_Report-Final.pdf. 2. Liu, H., Qin, C., Papangelakis, G., et al., "Compact all-fiber quantum-inspired LiDAR with over 100 dB noise rejection and single photon sensitivity," Nat. Commun., vol. 14, p. 5344, 2023, https://doi.org/10.1038/s41467-023-40914-6. 3. Dalvit, D. A. R., et al., "Quantum frequency combs with path identity for quantum remote sensing," Phys. Rev. X, vol. 14, p. 041058, 2024. 4. Wang, J., Wong, K. M., "Quantum illumination radars: Target detection," IET Radar Sonar Navig., vol. 18, no. 9, pp. 1541–1553, 2024, https://doi.org/10.1049/rsn2.12592. 5. Torromé, R. G. and S. Barzanjeh, "Advances in quantum radar and quantum LiDAR," Prog. Quantum Electron., vol. 93, p. 100497, 2024, https://doi.org/10.1016/j.pquantelec.2023.100497. 6. Bringing Quantum Sensors to Fruition, Subcommittee on Quantum Information Science, Committee on Science, National Science and Technology Council, Mar. 2022, https://www.quantum.gov/wpcontent/uploads/2022/03/BringingQuantumSensorstoFruition.pdf.

b. Quantum Enabled Bioimaging and Sensing Approaches for Biological Systems: BER Bioimaging science program supports integrative approaches to detect, visualize, and measure biological processes in situ to gain a predictive understanding of cellular function. The program focuses on development of new imaging instrumentation and technologies for study of cellular and molecular systems and networks critical to research DOE-relevant plants, microbes, and microbial communities. Applications are invited that employ innovative, use-inspired approaches for quantum-enabled bioimaging and sensing to facilitate commercialization of these technologies [1, 2, 3].

Goals and Metrics • The approaches should exploit quantum phenomena to surpass limitations of classical optics and propose a comparative advantage over competing classical optical methods. • Applications can explore new quantum science-enabled light sources, imaging detectors or biosensors envisioned to overcome the current challenges of resolution and detection limits, signal-to-noise ratio, limitations on temporal dynamics, long term signal stability, sample photodamage and limited penetration, or selective biomolecule sensing. • Approaches can include quantum entanglement imaging combined with quantum science-based probes for sensing and measurement; quantum-based biosensors to detect other physical or chemical cues from the local biological environment and report conditions with photon emissions; and optical or other non-optical methods for quantum sensor signal readout including electromagnetic or particle detection. • The development of new quantum science-enabled probes and sensors should be applicable for imaging of plant and microbial systems relevant to feedstocks for conversion to fuels, chemicals, and other bio-based materials and/or environmental research in BER. These quantum approaches and imaging systems need to visualize

plant and cellular structures and processes in a nondestructive manner, and at sufficient resolution to enable the validation of hypotheses of cellular function. • Systems capable of measuring cellular dynamics in vivo are encouraged but not required. • Imaging in two or three dimensions is emphasized while time varying measurements are desirable but not necessary in early-stage development. Research areas and processes of interest include probing biological systems to understand dynamic spatial and temporal relationships by characterizing structures of molecular and cellular components, chemical exchanges that facilitate the flow of information and materials across membranes and between intracellular partitions and measuring signaling processes between cells or organelles and plant-microbe and microbe-microbe interactions. Investigations at a larger scale can include the rhizosphere or whole plant responses to changing environments and within field settings. Networks of quantumenabled sensors that provide discontinuous but expansive environmental maps of measurements might also be considered if spatial or temporal resolution are appropriate to the biological or environmental problem to be addressed.

Not of Interest Proposals focused only on computational studies are not in scope, but computational and super intelligence/machine learning (SI/ML) approaches for implementation and increased utility of the bioimaging approaches through integrated, automated and autonomous workflows would be of interest.

References 1. U.S. Department of Energy, DOE BER Market Research Study: Transitioning Quantum Imaging and Sensing Technologies to Bioimaging Markets (2022), https://science.osti.gov/-/media/sbir/pdf/Market- Research/DOE_QuantumSenorTechToMarket-071422.pdf (accessed June 26, 2024). 2. U.S. Department of Energy, Office of Science, Biological and Environmental Research, Quantum-Enabled Bioimaging and Sensing Approaches for Bioenergy (2021), https://science.osti.gov/grants/FOAs/FOAs/2022/DE-FOA-0002603 (accessed June 26, 2024). 3. Subcommittee on Quantum Information Science, Committee on Science of the National Science & Technology Council, Bringing Quantum Sensors to Fruition (2022), https://www.quantum.gov/wpcontent/uploads/2022/03/BringingQuantumSensorstoFruition.pdf (accessed June 26, 2024).

c. Improving the Efficiency of Plant Regeneration and Nucleic Acid Delivery in Feedstock Crops Leveraging SI: This priority area seeks proposals to support the development and commercialization of new technologies that will improve the efficiency of plant regeneration and nucleic acid delivery (plant transformation) in bioenergy/feedstock crops (such as sorghum, switchgrass, miscanthus, populus, camelina, pennycress and energy cane) leveraging ML and SI.

Goals and Metrics • Proposals should clearly indicate how the proposed methods will offer an improvement to or overcome barriers in the current methods.

• Qualifying technologies include but are not limited to developing predictive models to select optimal explants (starting material), improving selection of callus for regeneration, nucleic acid delivery and selection of transgenics, and using SI algorithms for developing optimal tissue culture conditions/protocols to ensure consistent and reproducible results and resource optimization. • Applications should focus on design, development, and optimization at the lab scale.

Note: It is highly recommended that applicants discuss their technology with DOE to ensure fit prior to applying.

References

1. Albiol, J., Campmajó, C., Casas, C., & Poch, M., "Biomass estimation in plant cell cultures: a neural network approach," Biotechnology Progress, vol. 11, no. 1, pp. 88–92, 1995. 2. Alanagh, E.N., G-a, G., Haddad, R., Maleki, S., Landín, M., & Gallego, P.P., "Design of tissue culture media for efficient Prunus rootstock micropropagation using artificial intelligence models," Plant Cell, Tissue and Organ Culture, vol. 117, no. 3, pp. 349–359, 2014. 3. Arteta, T., Hameg, R., Landin, M., Gallego, P., & Barreal, M., "Neural networks models as decision-making tool for in vitro proliferation of hardy kiwi," European Journal of Horticultural Science, vol. 83, no. 4, pp. 259–265, 2018. 4. Hesami, M., & Jones, A.M.P., "Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture," Applied Microbiology and Biotechnology, vol. 104, pp. 9449–9485, 2020. 5. Hesami, M., Condori-Apfata, J.A., Valderrama Valencia, M., et al., "Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat," Applied Sciences, vol. 10, no. 15, p. 5370, 2020.

d. Complex Data: Advanced Data Analytic Technologies to Advance Bioengineering: BER science programs generate very large, complex, and multimodal data sets that have all the characteristics of big data – these data sets and associated analytics are critical for scientific discovery and bioengineering applications. Technology improvements in biolo