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Record W4416758700 · doi:10.1038/s41467-025-65627-w

The Effect of Applied Potential on the Li-mediated Nitrogen Reduction Reaction Performance

2025· article· en· W4416758700 on OpenAlexaff
Boaz Izelaar, Pranav Karanth, Arash Toghraei, Santosh K. Pal, Nandalal Girichandran, Mark Weijers, Ruud Hendrikx, Fokko M. Mulder, Ruud Kortlever

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsFaraday efficiencyElectrolyteInterphaseNitrogenElectrodeReduction (mathematics)Yield (engineering)Thickening

Abstract

fetched live from OpenAlex

Abstract The Li-mediated nitrogen reduction reaction (Li-NRR) has been proposed as one of the most promising ambient production routes for green ammonia. However, the effect of the applied potential ( E we ) on the reaction performance and the properties of the solid electrolyte interphase (SEI) remain poorly understood. Herein, we combine potential controlled experiments using a reliable Li x FePO 4 based reference electrode with post-mortem SEI characterization techniques, wherein we observe both an increase in the LiF concentration in the SEI, originating from LiTFSI decomposition, and the Faradaic efficiency (FE NH3 ) with an increasing E we . The transition from a predominantly organic SEI at low E we (−3.2 V SHE ) to a LiF-enriched layer at higher E we indicates the existence of kinetic barriers for the SEI formation reactions. Moreover, thicker and denser SEI structures observed at a higher E we enhance the Li-NRR by improving the mass transport regulation between reactant species. However, these thicker and denser SEI morphologies lead to current instabilities due to dynamic SEI thickening and breakdown.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.057
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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