Data sets for "Biohydrogen production through dark fermentation of agricultural waste: Novel strain and feedstock characterisation".

Data sets (as excel files) underpinning the figures presented in:

Biohydrogen production through dark fermentation of agricultural waste: Novel strain and feedstock characterisation,

Ariadna Vidal, Obaidullah Mohiuddin, Ella Chance, Sergio Serrano-Blanco, Thomas Howard, Jose Muñoz-Muñoz, Sharon Velasquez-Orta, Leonardo Rios-Solis

Bioresource Technology 2025, 132839, https://doi.org/10.1016/j.biortech.2025.132839.

Hydrogen is a promising alternative energy source that could help reduce reliance on fossil fuels and support the transition to decarbonisation of the energy sector. Biohydrogen can be produced through biological processes such as dark fermentation of agricultural waste. Lignocellulosic biomass from willow, hay, wheat or barley, is abundant and contains approximately 40 % carbon, which if properly pretreated, can serve as an excellent feedstock for microorganisms. In this study, Shewanella oneidensis MR-1, Cellvibrio japonicus Ueda107 and Sorangium cellulosum So ce27 were tested for biohydrogen (H2) production through dark fermentation of lignocellulosic waste. Maximum biohydrogen concentrations were obtained for S. oneidensis with 787.6 ± 69.3 mL H2/L willow hydrolysate after 12 h, and C. japonicus with 851.6 ± 20.8 mL H2/L hay hydrolysate after 36 h of fermentation. The main metabolite produced by S. oneidensis was acetic acid with a maximum yield of 6.48 mmol/L in willow-derived media after 36 h. C. japonicus and S. cellulosum mainly produced isobutyric in wheat-derived media, with 9.7 ± 12.4 mmol/L and 15 ± 10.1 mmol/L, respectively. This study proposes novel feedstocks and strains for biohydrogen and metabolites production from agricultural waste.

Keywords: Biohydrogen; Lignocellulosic biomass; Dark fermentation; Waste valorisation

The data sets deposited here contain the underpinning data used to generate Tables, Figures and SI Figures associated with this publication. Files are named after the relevant Table or Figure within the paper. All methods used to generate the data are presented in the paper.