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Lithium is the new green gold: it powers batteries, clean energy and futuristic tech. Extracting it from salty solutions, called brines, is a real challenge. Scientists have tested a method using low-field nuclear magnetic resonance (NMR), a technique already common in labs, but now adapted for real‑time monitoring. With two low‑field NMR devices, they measured lithium in both static samples and continuous flow, watching how hydrogen (¹H) and lithium (⁷Li) nuclei relax after a magnetic pulse. The results show lithium amount changes the relaxation time, allowing accurate quantification. They also examined temperature effects and sodium presence, another common brine component, to pinpoint the method’s reliability limits. In practice, this proof‑of‑concept suggests lithium plants could embed NMR sensors directly in production lines, checking purity without stopping the process. It’s a step toward faster, cheaper and greener operations to meet the booming demand for electric‑vehicle batteries.

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Nuclear magnetic resonance (NMR) exploits the magnetic moment of atomic nuclei. In brines, ⁷Li nuclei (spin = 3/2) and ¹H nuclei (spin = 1/2) respond to a static magnetic field B₀ and radio‑frequency pulses. The transverse relaxation time (T₂) depends on the nuclei's interaction with the molecular environment: higher ⁷Li concentration leads to more Li‑H₂O collisions, shortening T₂. In low‑field devices (B₀ ≈ 0.05–0.5 T), chemical shift resolution is limited, but the T₂ difference between ¹H and ⁷Li is sufficient for reliable quantification via exponential curve fitting. Flow analysis requires a small measurement cell (≈ 100 µL) to minimize transit time and keep thermal stability. Temperature affects water viscosity and ionic diffusivity, altering T₂ by about 2 %/°C. Sodium ions introduce spin‑exchange effects, increasing ⁷Li relaxation time when Na⁺ > 10 g L⁻¹. To maximize sensitivity, calibrate the system with lithium standards (0.5–5 mmol L⁻¹) and use Carr‑Purcell‑Meiboom‑Gill (CPMG) echo sequences with a 0.5 ms echo time. Multivariate analysis (PCA) can separate temperature, sodium and lithium contributions, improving estimation accuracy under variable industrial conditions.

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Authors:
Eric SchmidRahel LernerThomas RudszuckHermann NirschlGisela Guthausen
Date Published:
Original Article www.mdpi.com
Fact-check verified on 01/06/2026
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Creative Commons License CC BY-NC-SA 4.0

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