Discover the latest trends, best practices, and innovations in battery manufacturing and quality control
Explore how data-driven approaches are transforming battery manufacturing, from traditional trial-and-error methods to evidence-based development using AI and machine learning.
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From vibe experimentation to evidence-based progress - how data transforms battery development.
Read ArticleSelecting the right battery testing device is crucial for accurate research results and efficient development workflows.
Read ArticleInterested in creating reliable and optimized batteries that thrive in a range of conditions? It starts with Micantis and HPPC Battery testing.
Read ArticleSince the Micantis platform captures technical cell design information and pairs it with the test data for the cell, it can generate analyses that relate design characteristics with performance data with very little work from the user.
Read Article"I need you to produce more innovation per unit time." This is a real quote from an industry CEO to his engineers, and a direct statement of the business problem. Learn how optimal Design of Experiments can accelerate battery R&D and help you go to market faster.
Read ArticleLooking for the ideal tool to support your battery research and development efforts? BioLogic potentiostats stand out as indispensable instruments in electrochemistry.
Read ArticleIn the simplest terms, a potentiostat is the brain behind electrochemical experiments. It's a piece of electronic hardware that controls the electric potential between two electrodes, making sure that experiments run smoothly and accurately.
Read ArticleA potentiostat is an instrument used in electrochemistry to control and measure the electrical potential between a working electrode and a reference electrode in a solution.
Read ArticleWhen it comes to battery modeling, there's no magic bullet. The right model for a specific case depends on what questions need to be answered, and which phenomena drive the results of interest.
Read ArticleOptimal Design of Experiments (DOE) maximizes knowledge produced for a given number of tests. A design of experiment is simply a table of values where each column corresponds to a control variable, and each row corresponds to a cell design that will be built and tested in the experiment.
Read ArticleEverything you need to know about implementing effective quality control in battery manufacturing, from incoming inspection to final testing.
Real examples of manufacturers using machine learning to predict battery performance and reduce testing time by 85%.