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Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning

open access: yesAdvanced Engineering Materials, EarlyView.
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose   +7 more
wiley   +1 more source

Frontiers in manganese catalysis: a sustainable platform for bond construction and heterocycle synthesis. [PDF]

open access: yesRSC Adv
Das AK   +7 more
europepmc   +1 more source

Chloride-Selective Transmembrane Channel Formation by <i>Meta</i>-Positioned Halogen Bond Donors. [PDF]

open access: yesJACS Au
Sharma R   +6 more
europepmc   +1 more source

Curvularia lunata drives biodeterioration of PVC secondary cable insulation involving surface colonization, moisture retention and chemical deterioration. [PDF]

open access: yesPLoS One
Liu Y   +14 more
europepmc   +1 more source

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