ABSTRACT
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteriesâa current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
Subject(s)
Artificial Intelligence , Machine LearningABSTRACT
Vibrio species isolated from diseased seahorses were characterized by PCR amplification of repetitive bacterial DNA elements (rep-PCR) and identified by 16S ribosomal RNA gene sequence analysis. The results demonstrated that Vibrio alginolyticus and Vibrio splendidus were predominant in the lesions of these seahorses. To our knowledge, this is the first time that these bacterial species have been associated with disease symptoms in captive-bred seahorses.
