<i>In Situ</i> Studies of Copper-Based CO <sub>2</sub> Reduction Electrocatalysts by Scanning Transmission Soft X-ray Microscopy
Bibliographic record
Abstract
A microfluidic-enabled electrochemical device has been developed to investigate electrochemically active nanomaterials under reaction conditions using in situ scanning transmission soft X-ray microscopy (STXM). In situ STXM measurements were conducted on electrodeposited Cu catalysts under electrochemical CO 2 reduction (CO 2 R) conditions. The study provides detailed, quantitative results about the changes in the morphology and chemical structure of the catalytic nanoparticles as a function of applied potentials. The deposited Cu nanoparticles initially contain both Cu(0) and Cu(I). As an increasingly cathodic potential is applied, the Cu(I) species gradually convert to Cu(0) over the potential range of +0.4 to 0 V versus the reversible hydrogen electrode (V RHE ). During this process, Cu(I) particles of various sizes are converted to metallic Cu at different reaction rates and at slightly different potentials, indicating a degree of heterogeneity in the electrochemical response of discrete particles. At CO 2 R 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 report in situ STXM studies of a working electrode with relatively thick Cu-based electrodeposits. The spatially resolved chemical analysis identifies that Cu-oxide species can persist under CO 2 R conditions, but only when the catalytic nanoparticles are electronically isolated from the working electrode and therefore are catalytically irrelevant. In summary, in situ STXM is presented as a technique to gain advanced morphological and spatially resolved chemical structure insights into electrochemically active nanomaterials, which was used to provide improved understanding regarding Cu nanomaterial catalysts under CO 2 reduction conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".