Publications

2024 – 2026

  1. Tian Y, Kim B, Pamukçu I, Akyuz Turumtay E, Tan AH, Saini V, Chavez AI, Tang A, Su AZ, Baidoo EEK, Rencoret J, Del Río JC, Donohue TJ, Noguera DR, Eudes A. Engineered accumulation of protocatechuate in corn biomass to enhance biomanufacturing. ACS Sustain Chem Eng 2025;13:20204–14. https://doi.org/10.1021/acssuschemeng.5c09025. 
  2. Bowen BP, Harwood TV, de Raad M, Louie KB, Kosina SM, McMahon KD, Taş N, Bouskill NJ, Hazen TC, Bench SR, Mackelprang R, Petras D, Wang M, Maestre FT, Giovannoni SJ, Northen TR. ENVnet provides a global molecular resource of dissolved organic matter. Nat Biotechnol 2026:1–12. https://doi.org/10.1038/s41587-026-03230-0. 
  3. Gautam S, Mishra U, Scown CD. Machine learning based reduced-order models to predict spatiotemporal dynamics of soil carbon and biomass yield of different bioenergy crops. Carbon Capture Sci Technol 2025;15:100440. https://doi.org/10.1016/j.ccst.2025.100440. 
  4. Dai T, Ellebracht NC, Hunter-Sellars E, Aui A, Goldstein HM, Li W, Hellwinckel CM, Price L, Wong AA, Nico P, Basso B, Robertson GP, Pett-Ridge J, Langholtz M, Baker SE, Pang SH, Scown CD. Land-based resources for engineered carbon dioxide removal in the United States exceed the expected needs. One Earth 2025;8:101349. https://doi.org/10.1016/j.oneear.2025.101349. 
  5. Sordo Z, Chagnon E, Hu Z, Donatelli JJ, Andeer P, Nico PS, Northen T, Ushizima D. Synthetic scientific image generation with VAE, GAN, and diffusion model architectures. J Imaging 2025;11:252. https://doi.org/10.3390/jimaging11080252. 
  6. Chavez T, Zhao Z, Jiang R, Koepp W, McReynolds D, Zwart PH, Allan DB, Gann EH, Schwarz N, Ushizima D, Barnard ES, Mehta A, Sankaranarayanan S, Hexemer A. A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange. J Appl Crystallogr 2025;58:731–45. https://doi.org/10.1107/S1600576725002328. 
  7. Mukherjee S, Lang J, Kwon O, Zenyuk I, Brogden V, Weber A, Ushizima D. Foundation models for zero-shot segmentation of scientific images without AI-ready data. arXiv [CsCV] 2025. https://doi.org/10.48550/arXiv.2506.24039. 
  8. Sordo, Z., Andeer, P., Sethian, J. et al. RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs. Sci Rep 14, 12907 (2024). https://doi.org/10.1038/s41598-024-63497-8  OSTI ID:2370586