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Record W4385577976 · doi:10.3390/molecules28155878

Contemporary Strategies for Immobilizing Metallophthalocyanines for Electrochemical Transformations of Carbon Dioxide

2023· review· en· W4385577976 on OpenAlexafffund
Scheryn E. Lawson, Daniel B. Leznoff, Jeffrey J. Warren

Bibliographic record

VenueMolecules · 2023
Typereview
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlexibility (engineering)Electrochemical reduction of carbon dioxideNanotechnologyCarbon dioxideReduction (mathematics)Carbon monoxideMaterials scienceElectrochemistryCarbon fibersProcess engineeringCatalysisBiochemical engineeringChemistryEngineeringOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Metallophthalocyanine (PcM) coordination complexes are well-known mediators of the electrochemical reduction of carbon dioxide (CO2). They have many properties that show promise for practical applications in the energy sector. Such properties include synthetic flexibility, a high stability, and good efficiencies for the reduction of CO2 to useful feedstocks, such as carbon monoxide (CO). One of the ongoing challenges that needs to be met is the incorporation of PcM into the heterogeneous materials that are used in a great many CO2-reduction devices. Much progress has been made in the last decade and there are now several promising approaches to incorporate PcM into a range of materials, from simple carbon-adsorbed preparations to extended polymer networks. These approaches all have important advantages and drawbacks. In addition, investigations have led to new proposals regarding CO2 reduction catalytic cycles and other operational features that are crucial to function. Here, we describe developments in the immobilization of PcM CO2 reduction catalysts in the last decade (2013 to 2023) and propose promising avenues and strategies for future research.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.068
GPT teacher head0.337
Teacher spread0.269 · 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.

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

Citations5
Published2023
Admission routes2
Has abstractyes

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