1 to 9 of 9 Results
Feb 6, 2024 - Potenziamento in-situ della dealogenazione microbica in acque di falda mediante utilizzo di composti bio-based
Zecchin, Sarah, 2024, "Replication data for: "Effectiveness of Permeable Reactive Bio-Barriers for Bioremediation of an Organohalide-Polluted Aquifer by Natural-Occurring Microbial Community"", https://doi.org/10.13130/RD_UNIMI/OCM22P, UNIMI Dataverse, V1
This dataset includes sequences generated with Illumina sequencing of bacterial and archaeal 16S rRNA genes of groundwater samples collected in a site contaminated with chlorinated hydrocarbons. |
Feb 6, 2024Dipartimento di Scienze per gli Alimenti, la Nutrizione e l'Ambiente
Il progetto studia bio-processi innovativi per la bonifica in situ di acque di falda contaminate da sostanze organiche alogenate. L'obiettivo è quello di validare un intervento di bonifica biologica per la decontaminazione di acque sotterranee da solventi clorurati e studiare gli... |
Feb 5, 2024 - Electro-active biochar: scalable bioelectrodes to ‘power’ circular nutrients recovery and soil carbon sinks
Zecchin, Sarah, 2024, "Replication data for: "Plant nutrients recovery from agro-food wastewaters using microbial electrochemical technologies based on porous biocompatible materials"", https://doi.org/10.13130/RD_UNIMI/IUOJV2, UNIMI Dataverse, V1
The dataset includes fasta files originated from Illumina sequencing of bacterial and archaeal 16S rRNA genes. The microbial communities were sampled from different matrices of bioelectrochemical systems including terracotta separators. |
Feb 5, 2024Dipartimento di Scienze per gli Alimenti, la Nutrizione e l'Ambiente
Microbial electrochemical technologies (METs) are based on the capacity of certain microbes to use solid conductors (bioelectrodes) as electron acceptors or donors for their metabolism. During the last 2 decades, a series of promising environmental applications of METs were propo... |
Oct 4, 2021 - A conditional linear Gaussian network - Sangiovese grapes
Stefanini, Federico M.; Alessandro Magrini; Stefano Di Blasi, 2021, "Replication Data for "A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes"", https://doi.org/10.13130/RD_UNIMI/BMCJ8L, UNIMI Dataverse, V1, UNF:6:zl6bIXUg5THPPd+pQwFMWw== [fileUNF]
In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two- year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found th... |
Oct 4, 2021
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Sep 10, 2018
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