Holey Bismuth for Selective Electrochmical CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Because of the consumption of fossil fuel, CO2 emission, a product of burning fossil fuel, causes the global warming. Electrochemical CO2 reduction reaction (eCO2RR) provides a solution that allows to reduce the carbon emission and produce useful products. Among those CO2 reduction products, formic acid is one of the most attractive candidates because it has higher volumetric capacity of H2 (53 g H2/L) and can release H2 by catalyst under room temperature, which makes it a promising energy carrier.[1] Also, it is feasible to transport since it is nonflammable and stable under room temperature. Among the materials for eCO2RR, bismuth has better selectivity toward formate production (Faraday efficiency > 80%).[2] However, production rate, partial current density toward formate, of those catalyst remained low. One of the approach to boost the production rate is to increase the surface area of catalyst. By creating the porous structure, surface area of catalyst will be increased and thus enhances the production rate of formate. In this work, we successfully synthesized sponge-liked bismuth by chemical vapor deposition (CVD) method for eCO2RR. As results, it shows an excellent current density (28 mA/cm2) and faraday efficiency toward formate (95%) in moderate applied potential -1.05 V (vs. reversible hydrogen electrode (RHE)) under low catalyst loading. Furthermore, Comparing to electroplating bismuth, sponge-liked bismuth have double current density under same electroactive surface area. [1] Eppinger, J. and K.W. Huang, Formic Acid as a Hydrogen Energy Carrier . Acs Energy Letters, 2017. 2(1): p. 188-195. [2] Larrazabal, G.O., A.J. Martin, and J. Perez-Ramirez, Building Blocks for High Performance in Electrocatalytic CO2 Reduction: Materials, Optimization Strategies, and Device Engineering. J Phys Chem Lett, 2017. 8(16): p. 3933-3944.
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 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) | 0.002 | 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".