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Background: Anaerobic digestion (AD) or acidogenic fermentation (AF) of biomass can generate either biogas fuel or C ‒ C volatile fatty acids (VFAs) as feedstocks for synthesis of other petrochemical products. Typical AD feedstocks require large amounts of land that could otherwise be used for food production. Unlike these traditional bioenergy crops, plants using the crassulacean acid metabolism pathway (CAM), such as cacti and succulents, may be cultivated on degraded or semi-arid land that cannot support conventional agriculture. This could allow significant biorefinery feedstock to be sourced with minimal impact on existing agriculture or biodiversity. Several economically important CAM crops (e.g. pineapple, agave, prickly pear) are cultivated globally, with waste biomass that could be valorised as a biorefinery feedstock.
Results: Here, we investigate the fermentation kinetics of this novel feedstock class (CAM plants) against traditional bioenergy crops with two contrasting inocula: AD sludge and rumen fluid. Fermentations were performed under the influence of a methanogenesis inhibitor (bromoethane sulfonate) to isolate the acidogenic fermentation processes. CAM and non-CAM substrates in this study demonstrated distinct degradation kinetics (yields and degradation rates). We demonstrate that regardless of the inoculum type, CAM crops show higher hydrolysis rates for VFA production. Moreover, yields of VFAs from three CAM crops (0.41 ± 0.01 - 0.48 ± 0.02 g/g) were higher than for the three non-CAM crops (0.21 ± 0.01 - 0.38 ± 0.01 g/g) when AD sludge was used as the inoculum. This superior performance appeared to correlate with a higher abundance of soluble material and lower structural carbohydrate content in CAM biomass.
Conclusions: At industrial scale, the observed kinetic advantages of VFA production from CAM-plant feedstocks could translate into process enhancements that would greatly improve the cost-competitiveness of anaerobic biorefinery. Assuming comparable biomass productivities of CAM and non-CAM crops, this high yield could allow higher VFA production per unit of cultivated land, improving the environmental credentials of CAM biorefinery.
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http://dx.doi.org/10.1186/s13068-025-02636-3 | DOI Listing |
J Am Soc Mass Spectrom
September 2025
Mass Spectrometry Laboratory, MolSys Research Unit, University of Liège, B4000, Liège, Belgium.
Supervised machine learning methods have shown impressive performance in interpreting mass signals and automatically segmenting spatially meaningful regions in Mass Spectrometry Imaging (MSI). Such segmentation generates maps that provide researchers with valuable insights into sample composition and serve as a foundation for downstream statistical analyses. However, these models often require data set-specific preprocessing and do not fully exploit the rich mass features available in high-resolution mass spectrometry (HRMS).
View Article and Find Full Text PDFSci Rep
August 2025
Computer Science & Engineering, Indian Institute of Technology, Kharagpur, India.
Crop disease is a significant challenge in agriculture, requiring quick and precise detection to safeguard yields and reduce economic losses. Traditional diagnostic methods are slow, labor-intensive, and rely on expert knowledge, limiting scalability for large-scale operations. To overcome these challenges, a novel architecture called Mob-Res, combining residual learning with the MobileNetV2 feature extractor, is introduced in this work.
View Article and Find Full Text PDFFront Pharmacol
August 2025
Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijin, China.
Purpose: To investigate the effects of lavender essential oil (LEO) on postoperative sleep quality and perioperative neurocognitive disorders (PNDs) in patients with intracranial tumors.
Patients And Methods: This study was a randomized controlled trial in which all patients were randomly assigned to either the control group, which received no intervention, or the experimental group, which received LEO. Postoperative sleep quality was assessed using a dedicated sleep monitor.
Sci Rep
August 2025
School of Computer Engineering, Manipal Institute of Technology, Bengaluru,, Manipal Academy of Higher Education, Manipal, India.
The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants.
View Article and Find Full Text PDFJ Plant Res
August 2025
Temasek Life Sciences Laboratory, Singapore, 117604, Singapore.
Gamma-aminobutyric acid (GABA) is a metabolite involved in plant growth and stress responses, with its synthesis regulated by glutamate decarboxylase (GAD). Plant GAD enzymes have an autoinhibitory α-helix at the C-terminus, which calmodulin (CaM) binding typically relieves. Eliminating this C-terminal motif usually increases GABA levels in crops.
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