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Record W2944313669 · doi:10.1002/admi.201900091

Photo(electro)catalytic Nitrogen Fixation: Problems and Possibilities

2019· article· en· W2944313669 on OpenAlexafffund
Manh‐Hiep Vu, S.A. Hassanzadeh-Tabrizi, Trong‐On Do

Bibliographic record

VenueAdvanced Materials Interfaces · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Science and Technology, Ministry of Science and Technology, IndiaRoyal Society of ChemistryRoyal Society
KeywordsNitrogen fixationCatalysisNitrogenPhotocatalysisMaterials scienceAmmoniaNanotechnologyAmmonia productionSelectivityAdsorptionFixation (population genetics)PhotochemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Photo(electro)catalytic nitrogen fixation is considered as a competing alternative for the Haber–Bosch (HB) process due to the direct production of ammonia (NH 3 ) from nitrogen and water with zero carbon dioxide emission, which has made it a very hot research topic in recent years. Particularly, photo‐driven nitrogen reduction has been attracted to a specific focus in the scientific community since it can be powered by limitless solar energy at ambient conditions. However, unsolved challenges have remained to date such as, electron–hole separation, low quantum efficiency, weak visible light harvesting, catalytic selectivity, N 2 adsorption, and activation. In this Review, the recent achievements and related approaches toward nitrogen fixation are presented. In addition, the discussions on mechanistic photofixation of nitrogen, catalytic engineering design, and the outlook for enhancing the photocatalytic performance of ammonia photosynthesis are also devoted. Finally, the emerging trend of advanced photo(electro)catalysts for nitrogen fixation is proposed.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations116
Published2019
Admission routes2
Has abstractyes

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