Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO₂ emissions by mitigating data curation costs for a 2030 worldwide battery retirement scenario. This paper highlights exploiting limited data for exploring extended data space using generative methods, given data can be time-consuming, expensive, and polluting to retrieve for many estimation and predictive tasks.

Regulating electron transport rate and ion concentrations in the local microenvironment of active site can overcome the slow kinetics and unfavorable thermodynamics of CO₂ electroreduction. However, simultaneous optimization of both kinetics and thermodynamics is hindered by synthetic constraints and poor mechanistic understanding. Here we leverage laser-assisted manufacturing for synthesizing Cu_xO bipyramids with controlled tip angles and abundant nanograins, and elucidate the mechanism of the relationship between electron transport/ion concentrations and electrocatalytic performance. Potassium/OH⁻ adsorption tests and finite element simulations corroborate the contributions from strong electric field at the sharp tip. In situ Fourier transform infrared spectrometry and differential electrochemical mass spectrometry unveil the dynamic evolution of critical *CO/*OCCOH intermediates and product profiles, complemented with theoretical calculations that elucidate the thermodynamic contributions from improved coupling at the Cu⁺/Cu²⁺ interfaces. Through modulating the electron transport and ion concentrations, we achieve high Faradaic efficiency of 81% at ~900 mA cm⁻² for C₂₊ products via CO₂RR. Similar enhancement is also observed for nitrate reduction reaction (NITRR), achieving 81.83 mg h⁻¹ ammonia yield rate per milligram catalyst. Coupling the CO₂RR and NITRR systems demonstrates the potential for valorizing flue gases and nitrate wastes, which suggests a practical approach for carbon-nitrogen cycling.