Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.
Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.
Using multiple specialized techniques, IDREAM researchers gained a better understanding of how trace impurities within gibbsite affect the hydrogen yield.
Integrating experiments and modeling show how a fungal exudate-inspired substrate mixture elicits an emergent growth phenotype and metabolic responses in Pseudomonas putida.
Uncovered how the choice of carbon source influences the stress tolerance, redox balance, and regulatory responses of yeast, which can help provide resilience against scale-up challenges.
A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.
Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.
Replacing commercial acid with acidic waste enables researchers to improve nickel extraction efficiency, lower projected costs, and improve process economics.
Researchers discovered that a polymer additive promotes smooth, layer-by-layer deposition on metal electrodes by tuning interactions with the substrate.