Ying Li

Reliable experimental datasets are essential for accelerating sustainable hydrogen production through data-driven electrocatalyst discovery. Large language models (LLMs) have enabled the rapid construction of such datasets by extracting catalytic features directly from unstructured scientific literature. However, the structural reliability of such datasets for machine learning (ML) applications remains poorly understood. In this work, the reliability of LLM-extracted datasets for hydrogen evolution reaction (HER) catalysts is systematically evaluated. It is shown that high feature-level extraction accuracy does not guarantee instance-level correctness when multiple catalyst features must be jointly valid, leading to multiplicative error propagation across data records. To address this challenge, a quality control (QC)-aware data construction framework is developed. When evaluated under an identical ML pipeline, the QC-refined dataset recovered approximately 83% of the achievable predictive performance improvement toward the manually curated golden dataset. The resulting QC-refined dataset further enables compositional performance mapping of HER catalysts, revealing activity trends across dopant–active phase combinations and providing insights that can help guide HER catalyst design.

A promising approach to mitigate climate change is to use renewable electricity to convert carbon dioxide (CO2) emissions into useful products like carbon monoxide (CO) and hydrocarbons. While research has largely focused on developing high-performance catalysts for electrochemical CO2 reduction (eCO2R), scalability of catalyst synthesis remains underexplored. Metal–nitrogen–carbon (M–N–C) catalysts with dominant single-atom sites are among the most effective materials for CO production, but conventional synthesis methods rely on energy-intensive steps and complex pre- and post-treatment processes, hindering scalability and adding negative environmental impacts. This work demonstrates a scalable, single-step synthesis of M–N–C (M = Ni/Fe) catalysts using commercially available multiwalled carbon nanotubes (MWCNTs) and melamine as the feedstock. The method leverages inherent Ni/Fe impurities in MWCNTs, requiring only moderate-temperature pyrolysis (650 °C) without any pre- or post-treatments. Batch sizes up to 75 g were successfully produced using both regular-grade (>95% carbon content) and cheaper but more impure industrial-grade (>90% carbon content) MWCNTs. Structural, chemical, and electrochemical analyses confirmed reproducibility. Catalysts consistently achieved >98% CO selectivity at commercially relevant current densities (500 mA cm–2), outperforming benchmark Ag nanoparticle catalysts. Techno-economic analysis (TEA) further underscored the commercialization potential, yielding a base case minimum selling price (MSP) of \$145 per tonne of CO, \$255 below the current CO market price and lower than that obtained using Ag catalysts. Life cycle assessment (LCA) showed significant environmental benefits, registering 21% lower CO2-equivalent emissions compared to using Ag catalysts. Overall, this scalable approach produces high-performance M–N–C catalysts while delivering clear environmental and economic advantages, advancing the commercialization of eCO2R technology.

The electrochemical CO2 reduction (eCO2R) offers a compelling route for converting CO2 into value-added fuels and chemicals. Among CO2-derived products, methane (CH4) occupies a distinct position, serving both as a key intermediate for emerging cascade electro-oxidation to oxygenates and as a strategically important extraterrestrial fuel that can be generated in situ from off-planet CO2 resources. Although Cu-based catalysts capable of selectively producing CH4 have been reported, they seldom sustain high selectivity at practically relevant current densities. Here, we created a single-step co-pyrolysis strategy toward generating and anchoring Cu sub-nanometer clusters (CuSNC) atop Cu-Nx single-atom (SA) motifs embedded within N-doped carbon (NC), with controllable nanostructures through tuning of the synthesis parameters. Complementary spectroscopic analyses and density functional theory (DFT) calculations help reveal a structure−activity correlation that could guide the catalyst design. The CuSNC@NC sample synthesized at 550 °C pyrolysis temperature (best described and modeled as Cu3-CuN4 domains) represents the most effective combination of cluster size, metal-nitrogen coordination, and adsorption energetics needed to selectively promote CH4 generation versus other eCO2R products. Incorporating pulsed electrolysis and hydrophobicity-modulated transport tuning at the triple-phase boundary (TPB) further enhanced CH4 production achieving a partial CH4 current density of ∼321 mA cm−2, 53% Faradaic efficiency (FECH4), and less than 4% combined FE for other eCO2R products, simplifying downstream CH4 purification or upgrading. This work establishes generalizable principles for controlling Cu cluster atomicity and metal−nitrogen coordination, both of which are recognized determinants of CH4-efficient eCO2R.

Tandem electrolyzer strategies represent an innovative and stepwise evolution toward the commercialization of electrochemical CO₂ reduction. This review provides a comprehensive introduction to the fundamentals of tandem electrolyzers in catalysis, highlighting their significant advantages over traditional single-cell approaches, particularly in terms of electrical energy efficiency and cost-effectiveness. A detailed discussion is presented on experimental designs, including electrolyzer configurations and optimal reaction environments. Contemporary tandem research is categorized based on targeted products, clearly illustrating how tandem designs enhance performance metrics. By deconvoluting the complex direct conversion of CO₂ into two or more simplified, independently optimized steps, tandem strategies effectively mitigate or eliminate the persistent challenges of carbon loss and carbonate formation inherent in alkaline CO₂ reduction. This approach enhances the efficiency of individual reaction steps, improves overall process performance, extends the product range, and enables innovative versatile sequential reaction designs powered by renewable energy. Such strategies offer promising routes to overcoming longstanding barriers in CO₂ electrolysis, with implications for both industrial deployment and broader decarbonization efforts. Looking ahead, further advances will depend on addressing single-cell issues in catalyst selectivity and stability and on rigorous, tandem-level scale-up demonstrations, accompanied by comprehensive techno-economic analyses.

A novel single‐step low‐temperature pyrolysis method is developed to efficiently remove encapsulated Ni nanoparticles (NPs, 10–50 nm) from both regular‐grade (3 N‐NCs, along with some metallic Ni‐NCs. The treated materials demonstrate exceptional electroactivity toward CO ₂ reduction to CO, with the best‐performing CNT‐PTFE‐Mel‐650 sample achieving an ultra‐low onset overpotential of −19 mV and 98% CO selectivity in a current density range of 100–700 mA cm ⁻² . This NC catalyst demonstrates 25% lower voltage at 700 mA cm ⁻² compared to the single atom catalyst (SAC) control. Experimentally verified ab initio molecular dynamics (AIMD) models are simulated, and subsequent density functional theory (DFT) calculations further support the thermodynamic stability of Ni ₃ N‐NCs and their favorability for CO ₂ reduction. This work establishes a new method for creating ligand‐free electroactive NCs for efficient electrochemical reactions.

The electrochemical CO₂ reduction reaction (eCO₂RR) offers a promising route for converting CO₂ into value-added chemicals and fuels using renewable electricity.

This perspective critiques advancements in integrated CO₂ capture and electrochemical conversion, contrasting emerging methods like eRCC via amine or (bi)carbonate pathways and direct ACC with traditional sequential capture and conversion strategies.

Metal–Organic Frameworks (MOFs), praised for structural flexibility and tunability, are prominent catalyst prototypes for exploring oxygen evolution reaction (OER). Yet, their intricate transformations under OER, especially in industrial high‐current environments, pose significant challenges in accurately elucidating their structure–activity correlation. Here, we harnessed an electrooxidation process for controllable MOF reconstruction, discovering that Fe doping expedites Ni(Fe) MOF structural evolution, accompanied by the elongation of Ni−O bonds, monitored by in situ Raman and UV/Visible spectroscopy. Theoretical modeling further reveals that Fe doping and defect‐induced tensile strain in the NiO₆ octahedra augments the metal ds‐O p hybridization, optimizing their adsorption behavior and augmenting OER activity. The reconstructed Ni(Fe) MOF, serving as the anode in anion exchange membrane water electrolysis, achieves a noteworthy current density of 3300 mA cm⁻² at 2.2 V while maintaining equally stable operation 500 mA cm⁻² for 300 h and 1000 mA cm⁻² for 170 h. This undertaking elevates our comprehension of OER catalyst reconstruction, furnishing promising avenues for designing highly efficacious catalysts across electrochemical platforms.

Bimetallic Ni-Cu nanoparticles confined in MOF-derived carbon/ZrO2 nanocomposite for efficient and stable dry reforming of methane.