MétaCan
Menu
← Back to cohort
Record W2743513307 · doi:10.1149/ma2016-02/40/3026

Tungsten Carbide As a Catalyst for CO<sub>2</sub> Electrochemical Reduction

2016· article· en· W2743513307 on OpenAlexaff
Subiao Liu, Jing‐Li Luo

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCatalysisMaterials scienceElectrochemistryMethaneCarbideNoble metalTransition metalCarbon monoxideChemical engineeringMethanolMethanationNanotechnologyChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Currently, conversion of CO2 and through its reduction reaction is considered as a promising method for significantly reducing CO2 emission while permitting the use of fossil fuels to meet the ever-increasing energy demands worldwide [1]. CO2 is an extremely stable molecule, because it has high thermodynamic stability and kinetic inertia. Thus, its conversion is an energy-intensive process. As a consequence, the large scale conversion of CO2 requires highly efficient catalysts capable of converting CO2 to target chemicals, such as carbon monoxide, methane, ethane, ethylene, methanol, etc. This is particularly important in the electrochemical reduction of CO2 since a highly selective and energy-efficient catalyst is a prerequisite, to ensure the high selectivity for the products and greatly reduce the energy consumption, an important topic worth of extensive exploration and in-depth studies. For the catalysts containing noble metals such as Ag, Au and Pt, the high costs of metals are the main hindrance for their large-scale industrial applications [2]. Recent progress has been made in the improvement of the catalytic activities of noble metals, the required noble metal content and the associated costs to achieve a certain level of catalytic efficiency may be reduced significantly with those new catalysts [3]. In the meantime, transition metal carbides (TMCs) have received considerable attention as the alternative electrocatalysts and supporting materials because TMCs display remarkable catalytic activities owing to their similar electronic and catalytic properties to Pt-group metals (by inducing carbon into the metal lattice). Therefore, TMCs have been identified as the most promising candidates to replace Pt or reduce its content in catalysis reactions [4]. In particular, carbides of group 4-6 TMs have been investigated extensively for their catalytic properties towards various reactions including oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) [5]. However, carbides as catalysts in electrochemical CO2 reduction have not been investigated so far. Herein, we developed a tungsten carbide (WC) catalyst to efficiently reduce CO2 to CO in a KHCO3 solution. The XRD pattern of the prepared WC powders shows that no impurity phases were detected. Furthermore, the electrocatalytic activity of WC towards the CO2 reduction was studied in a full electrochemical cell. A catalyst suspension was prepared by mixing WC powders with Nafion solution, 2-propanol, and de-ionized water, and then dropped onto the glassy carbon electrode (GCE) after ultrasonic for dispersion. The cyclic voltammetry curves were recorded in an argon saturated solution and CO2 saturated solution between -0.5 V (vs. SCE) and -1.5 V (vs. SCE). It shows that the onset potential for the hydrogen evolution reaction (HER) was -1.11 V (vs. SCE), a high overpotential for HER. Normally, for HER at pH 7, the onset potential is -0.41 V. When the gas supply was changed to CO2, the current density increased significantly, this increment in the current density indicates that the CO2 reduction reaction occurred and the WC powders had high catalytic activity for CO2 reduction. Therefore, this group of carbides can be the promising materials in the electrochemical CO2 reduction at room temperature. Figure 1

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.009
GPT teacher head0.229
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicElectrocatalysts for Energy Conversion→French-language works237,207→