BOOSTING CO2 ELECTROREDUCTION VIA MEMBRANE ELECTRODE ASSEMBLIES WITH INCREASED CO2 CONVERSION RATES AND SELECTIVITY TOWARDS CO
Bibliographic record
Abstract
To combat the escalating environmental challenges and alleviate the current energy crisis, CO2 conversion to fuels and chemical feedstocks provides a reliable approach to mitigate the devastating impact of greenhouse emissions on climate change. CO2 conversion/reduction could be carried out by several methods; however, the electrochemical CO2 reduction (CO2R) approach has coupled several advantages. For instance, CO2R occurs in near-ambient reaction conditions and could be driven through the employment of renewable energy resources (wind or solar) to generate electricity. However, this reaction has a large energy barrier which requires a catalyst to facilitate its pathway. In this context, various catalyst designs were developed and investigated during the last decades, such as heterogenous (metal and metal oxide) and homogenous (organic molecules) catalysts. A new class of materials – atomically dispersed metal nitrogen–doped carbon support (M–N–C)– has emerged recently and showed remarkable enhancement for CO2R compared to the state-of-the-art. In particular, Ni–N–C catalysts have demonstrated an improved selectivity toward CO production compared to precious metal catalysts. Researchers have postulated this superior performance to the high atomic utilization (theoretically 100%) of the metal sites under reaction conditions and the enhanced electronic properties. In addition, intermetallic carbides have been included as a promising class of catalysts for CO2R due to their unique physical and chemical characteristics. These catalysts could be synthesized using different precursors; among them, MOFs are currently one of the most promising platforms that generate several catalyst designs. It was demonstrated that MOF’s unique characteristics, such as high surface area and porosity, would be transitioned to the derived catalysts. In this thesis, two MOF architectures (ZIF-8 and MOF-74) were initially selected to be employed as precursors for deriving atomically dispersed Ni–N–C catalysts. Both MOF-derived catalysts were evaluated for CO2R using a customized electrochemical cell (E-cell) with a 3–electrode configuration. The derived Ni–N–C catalysts using ZIF-8 and MOF-74 have achieved enhanced CO selectivity with Faradaic efficiencies (FE) > 90% at less negative applied potentials, –0.68 and –0.76 V vs RHE, respectively. Further, various synthetic conditions were explored in these studies, such as the role of the Ni content and the pyrolysis temperature on the resulted catalyst structure, and the electrocatalytic performance during CO2 electrolysis. Subsequently, one of the MOF topologies – ZIF-8 – was further utilized to develop other designs of electrocatalysts by introducing different synthetic conditions. This has resulted in generating various moieties that are able to produce CO during CO2R. For example, one derived catalyst design consists of homogenously distributed atomically dispersed dual Ni–Zn–NX/C sites. Whereas the other design demonstrated a heterogenous structure of Ni3ZnC-based particles anchored on atomically dispersed dual Ni–Zn–NX/C sites. Both electrocatalyst designs were integrated into a gas diffusion electrode (GDE) and evaluated for CO2R using an MEA-based electrolyzer. Our findings revealed that the co-existence of Ni3ZnC particles and dual Ni–Zn–NX/C active sites in a heterogenous structure has boosted the electrocatalytic activity towards CO production, achieving near unity CO FE at 448 mA/cm2 at an overall cell voltage of 3.1 V. Aside from the electrocatalytic performance, the nature of active sites in the developed catalyst designs has been studied using in-situ and ex-situ X-ray absorption spectroscopy. Other analytical techniques such as transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), powder X-ray diffraction (PXRD), and X-ray photoelectron spectroscopy (XPS) have also been used to identify the catalysts’ composition and morphology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".