Wen-Fang Cai

TL;DR: The results suggest that incorporating microbial community data with machine learning algorithms can be used for the prediction of feed substrate and for the potential improvement of MFC-based biosensor signal specificity, providing a new use of machine learning techniques that has substantial practical applications in biotechnological fields.

TL;DR: In this paper, a unique structural stainless steel (SS)-based anode for high-performance microbial fuel cells (MFCs) was proposed by in-situ electrochemical depositing polypyrrole (PPy) onto SS.

TL;DR: In this article, a transient, two-dimensional model for single-chamber, air cathode MFC was developed by finite element method considering two kinds of microorganisms growth, internal mass transfer and bio-electrochemical kinetics.

TL;DR: In this paper, the authors investigated the feasibility of 6 ML algorithms using genomic data and their corresponding operational parameters from 8 research groups to predict methane yield, and they achieved accuracies of 0.77 using operational parameters alone and 0.78 with genomic data at the bacterial phylum level alone.

TL;DR: Results suggest that abundance of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience.