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Record W6928936980 · doi:10.48336/nsz0-3c57

Electrochemical reduction of nitrite and CO₂, and oxidation of organic fuels

2023· article· en· W6928936980 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsChronoamperometryCyclic voltammetryElectrochemistryAmmoniaUreaCatalysisRenewable energy

Abstract

fetched live from OpenAlex

In today's world, the need for sustainable technology is more crucial than ever. With the continuous growth in demand for resources and energy, the environment is under severe strain due to climate change, depletion of natural resources, and environmental degradation. Electrochemical techniques offer a promising solution for sustainable development. One such approach is using carbon dioxide as a renewable, non-fossil-based feedstock to produce fuels and value-added chemicals via electrochemical processes that use renewable energy sources. In addition, coelectrolysis of carbon dioxide with environmental pollutants such as NO₂-, NO₃-, and NO has shown promising results for producing of sustainable fuels, commodity chemicals, and fertilizers while reducing environmental pollutants. Our research focuses on coreduction of CO₂ with nitrite (NO₂-) to produce ammonia and urea simultaneously using renewable power sources. Among the effective catalysts for this process, metallophthalocyanines (M-Pc) have been shown to be successful, especially iron-based phthalocyanine, with a high current efficiency. We investigated the electrochemical coreduction of NO₂- and CO₂ at carbon-supported iron-based phthalocyanine electrocatalysts to produce ammonia and urea under ambient conditions. To understand the electrochemical behavior of the electrodes, we used both cyclic voltammetry and chronoamperometry in 0.1 M NaHCO₃ and 5 mM NaNO₂ solution under N₂ and CO₂ environments. The produced ammonia and urea concentrations were determined using two different spectrophotometric techniques, and secondary analytical techniques, liquid-chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance spectrometric (1H-NMR), were used to confirm the accuracy of the results. Our results indicate that it is possible to produce urea at low overpotentials at various ironbased phthalocyanine electrocatalysts in NaHCO₃ as an electrolyte. However, our experiments revealed that ammonia was the primary electrolysis product when using carbon-supported iron phthalocyanine (FePc/C) as an electrocatalyst. At a potential of -0.347 V vs RHE, 85% of the current was used for NH₃ production, while only 4.1% was utilized for urea production. Nevertheless, we observed a significant amount of urea production at FePc/C, with a maximum yield of 5.8% at the lowest overpotential (-0.047 V vs RHE). We also observed that carbon supported sulfonated iron(III) phthalocyanine (FeTSPc/C) produced the highest faradaic yield (54.8%) of urea at a potential of +0.053 V vs RHE, with 25% coproduction of NH3. In a PEM electrolysis cell, the FePc/C catalyst demonstrated the potential to produce urea and ammonia simultaneously using very low NO₂- concentrations. The faradaic efficiency for urea was increased from 2.8% to 15.9% compared to the normal three-electrode cell. In addition to producing commodity chemicals, research has also focused on developing electrocatalysts for fuel cell applications. PtBi/C and PtPb/C catalysts were prepared by the surface decoration of a commercial Pt/C catalyst, and their catalytic activities for electrochemical oxidation of formic acid, methanol and ethanol were compared. It was found that the currents at 0 V vs SCE for formic acid oxidation at the PtBi/C and PtPb/C catalysts were ~ 6 and ~ 2 times higher, respectively, compared to the unmodified Pt/C catalyst. In addition, the PtBi/C catalyst also showed slightly higher activity for ethanol oxidation at low potentials compared to the unmodified Pt/C.

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 score0.002

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.255
Teacher spread0.237 · 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
Published2023
Admission routes1
Has abstractyes

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