(Invited) Nanotip-Defined High Local Electric Fields Accelerate CO<sub>2</sub> Reduction Heterogeneous Catalysts
Bibliographic record
Abstract
The electrochemical reduction of carbon dioxide (CO2) provides a promising path towards the storage of renewable energy and to the sustainable synthesis of carbon-based chemical feedstocks. A key challenge for such a process is the low activity and selectivity of the CO2 reduction reaction (CO2RR). It is intensely desired to reduce CO2 at low overpotentials, generating desired products at high current densities over extended periods, and reacting selectively without the formation of undesired byproducts. However, CO2RR today is hindered by the low local concentration of CO2 in aqueous electrolytes, in which more kinetically favorable reduction of protons to H2 often outcompetes CO2RR, eroding reaction selectivity. We developed a field-induced reagent concentration (FIRC) method to enhance the CO2RR process. Through shaping electrodes into arrays of nanoneedles, we generated a high local electric field even when using low applied overpotentials. The high field concentrates electrolyte cations, and the cations bring with these a high local concentration of CO2 to the active CO2RR surface. Simulations revealed that 10-fold higher electric fields can be achieved on nanometrically sharp tips compared to that of quasi-planar regions. Utilizing bottom-up synthesized gold nanoneedle electrodes, record-low onset potential and record-high geometric current density at the low potential of −0.35 V with nearly quantitative (>95%) Faradaic efficiency (FE) for CO2 reduction to CO conversion were achieved (1). This current density surpasses by an order of magnitude the performance of the best previously-reporeted gold nanorods, nanoparticles, and oxide-derived noble metal catalysts. The nanoneedle electrodes exhibited robust continuous reactions over 8 hours in an inorganic aqueous electrolyte. The FIRC concept has also be leveraged to build other metal nanoneedles and metal or metal sulfide covered gold nanoneedles. These electrodes exhibit much enhanced CO2to hydrocarbon conversion efficiencies, proving the wider application of the FIRC concept. References: (1) M. Liu, Y. Pang, B. Zhang, P. D. Luna, O. Voznyy, J. Xu, X. Zheng, C. T. Dinh, F. Fan, C. Cao, F. P. Arquer, T. S. Safaei, A. Mepham, A. Klinkova, E. Kumacheva, T. Filleter, D. Sinton, S. O. Kelley, E. H. Sargent, Nature, 2016, 537, 382.
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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) | 0.005 | 0.002 |
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".