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This work presents a computationally inexpensive framework for modeling combined pyrolysis and gas-phase combustion of general vegetative fuels, which improves on existing solvers by incorporating detailed chemical kinetics and predicts the ignition behavior. The main motivation for this work is capturing the burning behavior of live wildland fuels, which can differ from those of dead fuels. Existing models are unable to accurately predict the ignition time and temperature variations for the live fuel cases. The kinetics model used here accounts for the non-primary constituents of fuels, or “extractives”, which are expected to play a role in this distinct behavior. Validation studies show that the developed model is a promising tool for understanding the effects of fuel chemistry and spatial variation on ignition and fuel burning behavior. Case studies using the tool suggest that variations in ignition time can be explained by combined effects of variables such as moisture content, initial composition, and density. 

Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how to impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization (GR-LMF) to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph where each node is a solution and edges are relationships indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). For an experimental data set of the pairwise miscibility of 68 solutions from Peacock et al. [ACS Appl. Mater. Interfaces 2021, 13, 11449–11460], we find that GR-LMF more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers that use physicochemical features of the solutes. GR-LMF obviates the need for features of the solutions and solutions to impute missing miscibility outcomes, but it cannot predict the miscibility of a new solution without some observations of its miscibility with other solutions in the training data set.