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Record W4285397640 · doi:10.1149/ma2022-017632mtgabs

N-Doped Colloid Imprinted Carbons As Promising ORR Catalysts for Alkaline Applications

2022· article· en· W4285397640 on OpenAlexaff
Samantha Luong, Anand Chandra Singh, Xia Tong, Dayna Wiebe, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrolyteCatalysisMaterials scienceChemical engineeringEnvironmentally friendlyElectrochemistryCarbon fibersEnergy storageElectrochemical energy conversionNanotechnologyElectrodeChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The oxygen reduction reaction (ORR) has long been of interest in relation to its many energy applications and interesting multi-pathway mechanisms. The ORR is a key reaction in a range of electrochemical energy conversion and storage devices, such as hydrogen fuel cells and metal-air batteries, respectively. These devices are expected to play an ever-increasing role in the global transition to net zero emissions. Metal-air batteries, such as Zn/air batteries, can operate at room temperature, use recyclable materials, are environmentally friendly, and are preferred in relation to consumer safety than batteries relying on organic solvents and reactive electrode species.1 High rates of the ORR are crucial for the development of high performance Zn/air batteries and catalysts play a significant role, with lowering of the catalyst cost also of increasing importance. The costs of conventional Pt-based ORR catalysts are high. Therefore, metal-free carbon-based ORR electrocatalysts are viewed as increasingly promising alternatives, especially as they are lower in cost due to the availability of the precursor materials. Carbon is also a good electrical conductor and support material, is chemically stable, and can have large surface areas. However, the ORR kinetics are sluggish on carbon and chemical and physical modifications are required to enhance its activity. In typical Zn/air batteries, the ORR occurs at the three-phase boundary (TPB) formed between the solid electrode, liquid electrolyte, and gaseous oxygen. The porosity of the catalyst layer, the wettability of the catalyst/electrolyte interface, and the gas permeability and hydrophobicity of the gas diffusion layer (GDL) are thus also important, significantly influencing the cathode performance and durability. The catalyst layer (CL) must therefore be constructed with both a high-performance catalyst and an optimized TPB length to provide high performance without compromising durability. In the current work, we have doped nitrogen into the lattice of a family of nanoporous colloid imprinted carbon (CIC) powders to increase its ORR activity. The CICs are unique for their versatility in terms of pore size control and ease of surface functionalization.2 Pore sizes in the range of 12 to 100 nm were examined and their effect on the ORR activity and mass transport limitations were investigated. To carry out N-doping, the CICs were exposed to ammonia at 800 ˚C for 7 hr. Catalyst inks were then prepared by mixing the CICs with a binder in an isopropyl alcohol/water solution. Aliquots of the ink were drop-casted on the disc of an RRDE system, or were spray coated or drop-casted on a GDL to determine the ORR performance in an in-house Zn/air battery testing cell, with the N-doped CIC catalyst layer sandwiched between an electrolyte chamber and a graphite current collector. In this setup, O2 gas was flowed through the pores in the GDL to the catalyst/electrolyte interface, a Zn wire installed in the electrolyte chamber was used as the reference electrode, and a Ni sponge was used as the counter electrode. The RRDE experiments showed that, after N doping of the CIC powders, the production of peroxide decreased significantly and the ORR onset potential increased to a very respectable value of ca. 0.9 V vs RHE, indicating the successful activation of the ORR. Electron transfer numbers were found to be greater than 3.5, indicating that either a direct or pseudo- 4 electron transfer ORR pathway is dominant. In agreement with the literature, the ORR currents in the kinetic regions increased linearly with mass loading, expected to be proportional to the total active N-doped CIC surface area.3 The N-doped CIC samples retained excellent performance up to a loading of 0.350 μg/cm2 without losing mechanical stability. Similar N-doped CICs and binders of different hydrophobicity were tested in the Zn/air battery testing system. Electrodes made with a hydrophobic NCS microporous layer (MPL) showed much better ORR performance and durability than hydrophilic NCS MPLs. Although electrodes made using hydrophobic polytetrafluoroethylene (PTFE) as the binder in the catalyst layer showed a similar initial performance to those made using hydrophilic Nafion binders, the PTFE based electrodes exhibited better durability. Furthermore, the temperature and pressure used during electrode fabrication were also found to have a significant impact on the binder distribution and ORR performance. Once optimized, a very good correlation was obtained between the N-doped CIC catalyst performance in the RRDE setup and in the Zn/air battery testing system. References J. Pan et al., Adv. Sci., 5, 1700691 (2018). X. Li et al., ACS Appl. Mater. Interfaces, 10, 2130–2142 (2018). N. Gavrilov et al., J. Power Sources, 220, 306–316 (2012).

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.248
Teacher spread0.236 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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