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Record W4394873308 · doi:10.1002/advs.202402234

Secondary Structure in Enzyme‐Inspired Polymer Catalysts Impacts Water Oxidation Efficiency

2024· article· en· W4394873308 on OpenAlexfundno aff
Graziela C. Sedenho, Steffane Q. Nascimento, Marjon Zamani, Frank N. Crespilho, Ariel L. Furst

Bibliographic record

VenueAdvanced Science · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsnot available
FundersArmy Research OfficeLaboratório Nacional de NanotecnologiaNational Institutes of HealthMinistério da Ciência, Tecnologia e InovaçãoCentro Nacional de Pesquisa em Energia e MateriaisFundação de Amparo à Pesquisa do Estado de São PauloNational Institute of Environmental Health SciencesCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCanadian Institute for Advanced Research
KeywordsRandom coilCatalysisElectrocatalystActive siteChemistryProtein secondary structureCopperMaterials sciencePolymerChemical engineeringElectrodeElectrochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Protein structure plays an essential role on their stability, functionality, and catalytic activity. In this work, the interplay between the β-sheet structure and its catalytic implications to the design of enzyme-inspired materials is investigated. Here, inspiration is drawn from the active sites and β-sheet rich structure of the highly efficient multicopper oxidase (MCO) to engineer a bio-inspired electrocatalyst for water oxidation utilizing the abundant metal, copper. Copper ions are coordinated to poly-histidine (polyCuHis), as they are in MCO active sites. The resultant polyCuHis material effectively promotes water oxidation with low overpotentials (0.15 V) in alkaline systems. This activity is due to the 3D structure of the poly-histidine backbone. By increasing the prevalence of β-sheet structure and decreasing the random coil nature of the polyCuHis secondary structures, this study is able to modulates the electrocatalytic activity of this material is modulated, shifting it toward water oxidation. These results highlight the crucial role of the local environment at catalytic sites for efficient, energy-relevant transformations. Moreover, this work highlights the importance of conformational structure in the design of scaffolds for high-performance electrocatalysts.

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 score0.007
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.003
GPT teacher head0.209
Teacher spread0.206 · 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
Published2024
Admission routes1
Has abstractyes

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