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Interface engineering of silver (Ag) electrodes provides an effective strategy to tune activity and selectivity in electrochemical CO₂ reduction (CO₂RR). Here, Ag and Ag oxide (Ag/AgxO) electrodes were prepared via controlled infrared (1064 nm) laser treatment and evaluated in KHCO₃ and phosphate electrolytes. Laser-induced surface restructuring modified oxidation states, crystallographic orientations, and interfacial electronic properties, leading to electrolyte- and potential-dependent shifts in product distribution. In KHCO₃ electrolyte, CO remained the dominant product, whereas phosphate electrolyte significantly enhanced CO selectivity with increasing laser treatment. Oxidized Ag surfaces suppressed hydrogen, formate, and C₂+ hydrocarbons. Product selectivity showed clear potential dependence: CO and C₂+ hydrocarbons peaked at moderate potentials, while formate formation increased at more negative potentials. Long-chain C₂+ hydrocarbons followed a Fischer-Tropsch-like growth trend. Electrochemical impedance spectroscopy revealed changes in charge-transfer kinetics and interfacial capacitance. Density functional theory calculations indicate that CO formation via COOH intermediates is favored on Ag(111) and Ag₂O(111), whereas formate formation via HCOO* is more favorable on metallic Ag. These results clarify how Ag/Ag oxide interfaces regulate CO₂RR pathways.

Surface-enhanced Raman spectroscopy (SERS) is an analytical technique widely used for molecular identification and trace detection, offering minimal sample preparation, high sensitivity, molecular fingerprinting, and quantitative capability. However, SERS measurements generate large, complex spectral datasets whose interpretation is time-consuming and requires specialized expertise. The integration of artificial intelligence (AI) and machine learning (ML) provides effective strategies for automated data processing, feature extraction, and pattern recognition, enabling a new generation of intelligent SERS sensing platforms. Unlike previous reviews, which have largely catalogued AI/ML-SERS applications by domain, this work provides a critical synthesis structured around four contributions: (i) a workflow-level taxonomy linking SERS data characteristics to algorithm selection; (ii) a quantitative meta-analysis of approximately 40 representative studies covering the distribution of reported accuracies, the relationship between dataset size and reported performance, and the temporal shift from classical chemometrics to deep learning; (iii) a dedicated assessment of validation pitfalls specific to AI/ML-SERS, including substrate batch effects, technical-replicate leakage, spectrum-level versus sample-level splitting, and the absence of external validation cohorts; and (iv) a practical reporting checklist for future AI/ML-SERS studies. By foregrounding methodological rigor rather than enumeration, the review aims to support both SERS practitioners adopting AI/ML and ML researchers entering spectroscopic sensing.

Electrochemical CO₂ reduction (CO₂RR) on copper (Cu) typically produces a broad spectrum of products, making it difficult to selectively generate desired chemicals. Here, we show that incorporating trace amounts of iron (Fe) into Cu electrodes enables a phase-selective modulation of CO₂RR pathways. In the liquid phase, Fe incorporation significantly enhances formate selectivity, while concurrently suppressing the formation of other liquid products such as ethanol and propanol. In the gas phase, Fe promotes C₃+ hydrocarbon formation, increasing their Faradaic efficiency while diminishing the yields of C₂ species like ethylene. Structural and spectroscopic characterizations reveal that Fe induces electronic and morphological reconfiguration of the Cu surface, including facet redistribution, oxide formation, and local coordination changes. This dual-phase selectivity control via Fe doping offers a new design principle for tuning product distributions in CO₂RR and advancing catalytic strategies for sustainable carbon conversion.