Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
Sammendrag
The treatment of organic waste (OW) by anaerobic digestion (AD) conforms to the concept of sustainable development. But AD is facing the issue of low conversion rate. In this work, the photo-AD system using visible light (LED lamp) as the source was constructed and the performances and mechanism of N-doped carbon quantum dots (NCQD) were explored in the system for the first time. The results showed that 0.5 g/L NCQD promoted a 23.1 % increase in cumulative CH4 yield in the photo-AD system. Microbial analysis results showed that in photo-AD with NCQD, the dominant strain was Methanosarciniales, with an abundance of 69.0 %. Microbial activity and structural integrity tests showed that the microorganisms were not damaged by free radicals. In addition, NCQD increased the redox peak intensity of the CV curve and increased photocurrent intensity of photo-AD. Furthermore, it promoted an increase of 18.2 % (0.26 ± 0.03 μmol/mL) in ATP concentration. The photoelectrochemical analysis and quantitative analysis of functional genes results indicated that NCQD mainly promoted methanogenesis by providing photoelectrons. This promotion mechanism increased the copynumber (61,652.8 g−1) of EchA in photo-AD, rather than Vht and Hdr related to cytochrome. This work provided new strategies for the enhancement of AD and clarified potential mechanisms.
Forfattere
Ruchiru D. Herath Uttam Pawar Dushyantha M. Aththanayake Kushan D. Siriwardhana Dimantha I. Jayaneththi Chatura Palliyaguru Miyuru Gunathilake Upaka RathnayakeSammendrag
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Forfattere
Junbin Zhao Simon Weldon Alexandra Barthelmes Erin Swails Kristell Hergoualc’h Ülo Mander Chunjing Qiu John Connolly Whendee L. Silver David CampbellSammendrag
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Forfattere
Erlend Sørmo Katinka Muri Krahn Gudny Øyre Flatabø Thomas Hartnik Hans Peter Heinrich Arp Gerard CornelissenSammendrag
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Forfattere
Yi Zhang Zhangmu Jing Yijing Feng Shuo Chen Yeqing Li Yongming Han Lu Feng Junting Pan Mahmoud Mazarji Hongjun Zhou Xiaonan Wang Chunming XuSammendrag
Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus, within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems.
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