Training

Technical Advisory

Experimental Design & Data Interpretation

R&D Teams

Government Labs, Agencies

Industry

Experiments that do not address the underlying technical or decision question
Gaps in expertise, infrastructure, or test protocols that limit data quality
Misinterpretation of electrochemical or corrosion data
Test artifacts mistaken for real material or system behavior
Difficulty translating laboratory results to field performance
Mechanism-first approach: focus on the underlying physics and chemistry so solutions address root causes, not symptoms.
Purpose-driven experimental design: experiments designed to answer real operational and decision questions, not just generate data.
Clear interpretation of noisy or incomplete data: extract sound conclusions from noisy or incomplete data, quantify uncertainty, and identify limits.
Training built into the engagement: targeted instruction and hands-on coaching that builds team capability and judgment for future challenges



publications