Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 486

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

Theoretical Insights into Superior Nitrate Reduction to Ammonia Performance of Copper Catalysts

Hu, Tao, Wang, Changhong, Wang, Mengting, Li, Chang Ming, Guo, Chunxian·2021·ACS Catalysis

Abstract

Nitrate reduction to ammonia (NRA) is critical and attractive for environmental remediation and energy conservation. Copper represents one of the most promising non-noble-metal NRA electrocatalysts while its intrinsic catalytic activity of facets and pH influence remain unclear. Using density functional theory calculations, nitrate reduction to ammonia pathways are evaluated on low-index crystal surfaces, Cu(111), Cu(100), and Cu(110), at different pH. Systematic thermodynamic and kinetic analysis indicates that the pathway NO 3 – → *NO 3 → *NO 2 → *NO → *NOH → *NHOH → *NH → *NH 2 → *NH 3 → NH 3 (g) is the most probable in all pH ranges, ending a long-standing debate on NRA pathways. Both the catalytic deoxygenation and hydrogenation processes in NRA are substantially affected by pH. Thus, the rate-determining steps and overpotentials exhibit pH-dependent characteristics. Besides, it is found that the pH influences the competition between the hydrogen evolution reaction (HER) and NRA. By considering NRA and HER on different surfaces, we found that Cu(100) and Cu(111) contribute most to NRA other than Cu(110). Specifically, in near-neutral and alkaline environments, Cu(111) exhibits the best NO 3 – to NH 3 performance, while Cu(100) is more effective in a strong acidic environment. This result rationalizes recent experimental observations. The NRA activity differences of copper surfaces are attributed to the local coordination environment and electronic states of surface atoms. Thanks to a stereospecific Cu–Cu couple, both strong *NOH adsorption and weak *NH 3 adsorption are realized on Cu(111) and Cu(100), facilitating superior NRA.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acscatal.1c03666

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 486

Metadata

Authors
Hu, T., Wang, C., Wang, M., Li, C. M., & Guo, C.
Venue
ACS Catalysis
Published
2021-11-15
Volume
11
Issue
23
Pages
14417-14427
ISSN
2155-5435 (ISSN-L)
OpenAlex
W3212240724
Cited by
486
References
46
FWCI
11.95
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.