
The Secret to Slashing Battery Test Times: Ultra-High Precision Coulometry (UHPC) and Machine Learning
High resolution raw data, unique cycle metrics – UHPC has valuable applications in development, quality, and machine learning lifetime prediction.
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The Battery Talent Census provides the most comprehensive, data-driven analysis to date, drawing insights from over 1,000 professionals. It examines workforce demographics, skill demand, compensation, retention, and student career perceptions.

High resolution raw data, unique cycle metrics – UHPC has valuable applications in development, quality, and machine learning lifetime prediction.

In battery research and development, one critical challenge is characterizing where lithium ends up and how it behaves. Laser Ablation Laser Ionization with Time-of-Flight Mass Spectrometry (LALI-TOF-MS) offers a revolutionary solution for pinpointing lithium in battery materials.

Metallurgical-grade silicon (MG-Si) is emerging as a game-changing alternative to graphite in lithium-ion batteries, offering 10x the energy capacity at a fraction of the cost. Recent innovations have overcome silicon’s long-standing cycling challenges, enabling the use of 100% MG-Si in battery anodes. This breakthrough could dramatically lower battery prices and boost performance, paving the way for more scalable and sustainable energy storage.