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Record W4382918766 · doi:10.1021/acs.analchem.3c01638

Diamonds in the Rough: Direct Surface Enhanced Infrared Spectroscopic Evidence of Nitrogen Reduction on Boron-Doped Diamond Supported Metal Catalysts

2023· article· en· W4382918766 on OpenAlexaff
Osai J. R. Clarke, Annabel Rowley, Robert V. Fox, Abderrahman Atifi, Ian J. Burgess

Bibliographic record

VenueAnalytical Chemistry · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Saskatchewan
FundersIdaho National LaboratoryLaboratory Directed Research and DevelopmentBattelleIdaho Operations Office, U.S. Department of EnergyU.S. Department of Energy
KeywordsChemistryDiamondBoronCatalysisDopingNitrogenInfraredMetalInorganic chemistryNanotechnologyOptoelectronicsOrganic chemistryOptics

Abstract

fetched live from OpenAlex

In situ investigations of electrocatalytic processes of increasing societal interest such as the nitrogen reduction reaction (NRR) require aggressive experimental conditions that are not readily compatible with surface sensitive techniques such as attenuated total reflection surface enhanced infrared absorption spectroscopy (ATR-SEIRAS). A method for performing ATR-SEIRAS studies at very negative potentials where conventional IR-active films delaminate and fail is reported. The method relies on a thin film of very robust boron-doped diamond deposited on a micromachined Si wafer, which provides extended mid-IR transparency at long wavelengths. SEIRAS activity is achieved by electrodepositing gold nanoparticles onto the conductive BDD layer. The Au@BDD layers are shown to sustain prolonged periods of electrolysis at negative potentials, with no degradation of the modifying layer. The efficacy of these substrates for electrocatalysis is demonstrated by studying the reduction of N 2 at −1.5 V vs Ag/AgCl in an aqueous-based electrolyte. Under these conditions, direct spectroscopic evidence of both NH 3 and hydrazine formed from the nitrogen reduction reaction (NRR) is provided.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.305
Teacher spread0.280 · 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 teacher head, 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

Citations14
Published2023
Admission routes1
Has abstractyes

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