Direct Electrosynthesis of C <sub>3+</sub> Hydrocarbons from CO <sub>2</sub> via Size-Controlled Nickel Nanoislands on a Carbon Support
Bibliographic record
Abstract
Direct synthesis of C 3+ hydrocarbons via the electrochemical CO 2 reduction reaction is highly desirable for producing sustainable chemicals. However, this approach remains challenging due to the limited ability of current electrocatalysts to adsorb and couple key reaction intermediates effectively, with promising systems, such as Ni oxyhydroxide-derived catalysts, still exhibiting partial current densities toward C 3+ hydrocarbons <0.9 mA cm –2 . Motivated by the limited activity and control over the active site environment of these systems, we hypothesize that reducing the size of metallic Ni modifies its electronic states and introduces interfacial metal–support sites that promote more balanced *CO adsorption, critical for facilitating C–C coupling beyond C 2 intermediates. Here, we report a plasma-assisted deposition method to synthesize size-controlled metallic Ni nanoislands on a carbon support. Characterization revealed that reducing the nanoisland size (<12 nm) forms undercoordinated, electron-deficient, and strained surfaces with a downshifted d-band center─features associated with weakened *CO binding, favoring intermediate coupling and C 3+ hydrocarbon formation. Nanoislands as small as ∼3.5 nm delivered a 120-fold increase in C 3+ hydrocarbon specific activity relative to large particles (bulk-like Ni). CO stripping voltammetry shows weaker *CO adsorption on isolated nanoislands. While C 3+ partial current densities remain low (∼0.1 mA cm –2 ), these findings identify nanoparticle size and metal–support interactions as key design parameters for advancing CO 2 conversion to long-chain hydrocarbons, offering a foundation for further improvement, as demonstrated by a >20-fold enhancement in the Ni-mass-based activity versus state-of-the-art catalysts.
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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 | 0.000 |
| 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) | 0.001 | 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 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".