Engineered Mn modulation for the dual enhancement of plasticity and degradation kinetics in biodegradable Zn–Li alloys
Source abstract
Abstract
The clinical application of Zn-based biodegradable materials is currently hampered by the intrinsic brittleness of the HCP structure and the risk of incomplete degradation due to surface passivation. To overcome these limitations, this work introduces Mn as a strategic microstructural modulator in the heterostructured Zn-0.8Li system. The incorporation of dissolved Mn induces strong constitutional supercooling, successfully refining both the coarse primary α-Zn grains and the surrounding eutectic β-LiZn 4 lamellar spacing. This multi-scale structural refinement establishes an ultra-dense interfacial network that fundamentally optimizes the deformation kinetics. Rather than relying on the activation of non-basal slip, the refined boundaries enforce intense micro-strain partitioning and maximize Heterogeneous Deformation Induced (HDI) strengthening, generating a massive internal back-stress. Consequently, the as-cast Zn-0.8Li-0.4Mn alloy achieves a highly competitive structural profile, including a Tensile Yield Strength (TYS) of 203.2 MPa and a Compressive Yield Strength (CYS) of 340.2 MPa. Furthermore, this structural modulation addresses the degradation necessity by establishing a uniform micro-galvanic “engine” across the refined phase boundaries. Driven by the inherent potential differences among the multi-phase constituents, this engine effectively disrupts early-stage Li-induced passivation, transforming the corrosion mode from passive retention to active, controllable clearance. This study demonstrates that Mn-mediated solute modulation is a highly effective strategy for balancing robust mechanical integrity with complete bio-resorption.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Magnesium Alloys: Properties and Applications · Electrodeposition and Electroless Coatings · Metallurgy and Material Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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