Sep 1, 2026 · 20 min
Livermore redesigns the path from breakthrough to product
Science of Scale-Up
Most promising technologies fail to reach users because laboratory performance does not survive the demands of cost, durability, manufacturing, and real-world operation.
- 1Scale-up works best when researchers define the finished technology and its operating requirements before expanding production.
- 2Risk assessments, modeling, testing, and industry collaboration can expose costly failures while designs remain easy to change.
- 3Successful commercialization requires an ecosystem linking basic science, manufacturing, workforce, economics, automation, and policy.
Don't miss
Andrew Wong’s carbon-capture example shows how years of improving production can be wasted when a technology’s operating environment is tested too late.
The brief
Rechargeable lithium-ion batteries took decades to become affordable, durable products, setting up the episode’s central question: how can researchers design for adoption earlier?
Sarah Baker and Andrew Wong explain that scale means more than production volume; it includes durability, power, cost, manufacturing, and fit with a specific application.
A carbon-capture microcapsule project shows the cost of scaling the wrong thing: years of production improvements preceded the discovery that the capsules would fail in operation.
The initiative works backward from the finished technology, combining risk assessment, modeling, manufacturing, testing, and refinement while designs are still easy to modify.
A gas-separation project reveals how changing from 3D printing to stamping and layering created performance losses that iterative modeling and testing had to solve.
Livermore’s broader scale-up ecosystem connects laboratories, industry, manufacturing, workforce, automation, economics, and policy around one measure of success: repeated real-world adoption.
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