In-situ Studies of Copper-based CO2 Reduction Electrocatalystsby Scanning Transmission Soft X-ray Microscopy
Bibliographic record
Abstract
A micro-fluidic enabled electrochemical device has been developed to investigate electrochemically active materials under reaction conditions using in-situ scanning transmission soft X-ray microscopy (STXM). In-situ STXM measurements at the Cu 2p edge were conducted on electro-deposited Cu catalysts under electrochemical CO2 reduction (CO2R) conditions. The study provides detailed, quantitative results about the changes in the morphology and chemical structure (oxidation state) of the catalyst particles as a function of applied electrode potentials. The initially electrochemically deposited Cu particles contain both Cu(0) and Cu(I). As an increasingly cathodic potential is applied, the Cu(I) species gradually converted to Cu(0) over the potential range of +0.4 to 0 V versus the reversible hydrogen electrode (VRHE). During this process, Cu(I) particles of various sizes are converted to metallic Cu at different reaction rates and at slightly different electrode potentials, indicating a degree of heterogeneity in the electrochemical response of discrete particles. At CO2R relevant potentials, only metallic Cu is observed, and the morphology of the particles is fairly stable within the spatial resolution limits of STXM (~ 40 nm). We also prepared a working electrode with relatively thick Cu-based electro-deposits. The spatially resolved chemical analysis by STXM identified that Cu-oxide species can persist under CO2R conditions, but only when the catalyst particles are electronically isolated from the working electrode and therefore are catalytically irrelevant. Thus, in-situ STXM is presented as a technique to gain advanced morphological and spatially-resolved chemical structure insight into electrochemically active materials, which was used to provide improved understanding regarding Cu electrodes under CO2 reaction conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".