Cathode lithium-rich compensators for next-generation lithium-ion batteries: classification framework, challenges, and synergistic prelithiation strategies

Proposes a classification framework for cathode LRCs based on lithium compensation mechanisms, dividing materials into binary, ternary, over-lithiated, sacrificial lithium salt, and sustained-release types. The text states that despite rapid progress, the relationships among lithium-release chemistry, charge-compensation mechanisms, electrode compatibility, and cell-level performance remain insufficiently organized; this framework responds directly to that gap. A review-level conceptual synthesis based on the authors' systematic summary of working principles, typical charge compensation pathways, and practical performance, rather than a single experimental dataset.

Systematically summarizes key challenges including air instability, gas evolution, high delithiation voltage, and processing issues, and pairs them with mitigation strategies such as nanoscale engineering, surface coating, defect and doping design, and electrolyte optimization. Organizes challenges and countermeasures in matched pairs, allowing issues at the material design, electrode engineering, and system integration levels to be viewed within one framework. Inductive evidence from literature review; the text presents these as discussed challenges and highlighted mitigation strategies without a unified quantitative metric.

Compares cathode-side, anode-side, and electrolyte-mediated lithium compensation routes, clarifies their complementary effects, and proposes a synergistic multimodal compensation strategy integrating one-shot prelithiation with sustained lithium release. Brings compensation approaches previously scattered across different technical routes into a single comparative framework, framing them as complementary rather than mutually exclusive. A strategic proposal put forward by the review based on comparative investigation of different compensation routes, still requiring subsequent experimental validation.

Proposes an AI-driven strategy for synergistic optimization of cathode-, anode-, and electrolyte-side prelithiation, and analyzes the potential of AI for materials screening, molecular structure design, and precise matching of lithium sources. Introduces artificial intelligence into the materials chemistry problem of prelithiation as a possible path for cross-scale synergistic optimization. A forward-looking outlook; the text presents it as a proposed strategy and a systematic analysis of potential, without reporting specific models or validation results.

This work is positioned as a review, suited to researchers and engineers seeking a systematic understanding of cathode prelithiation chemistry, especially readers focused on material design, electrode engineering, and cell-level system integration. Its classification framework and synergistic multimodal compensation strategy target the development of high-energy-density, long-life lithium-ion batteries, while the AI-driven strategy targets stages such as materials screening, molecular structure design, and lithium source matching.

This reading is at the abstract level and does not include the main text figures and specific data, so the concrete performance values, comparison conditions, and experimental details of each LRC type cannot be presented here. The synergistic multimodal compensation strategy and the AI-driven optimization outlook are directions proposed in the text; their feasibility, applicable boundaries, and validation approaches remain questions a reader can continue to watch.