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Record W3025844894 · doi:10.1149/ma2020-01512796mtgabs

Protein Assisted Fabrication of Metallic Nanorings and Spherical Nanoparticles for Electrocatalytic and Organocatalytic Applications

2020· article· en· W3025844894 on OpenAlexaff
Yani Pan, Janine Mauzeroll

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanomaterialsNanoparticleNanotechnologyCatalysisMaterials scienceSelectivityBiomoleculeAqueous solutionChemical engineeringChemistryCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Inspiration from nature has driven the development and application of greener nanomaterials prepared using biotemplates in the field of nanoscience. Compared to traditional chemical routes, bioinspired nanomaterial synthesis has the advantage of mild synthetic conditions such as aqueous environment, ambient temperature, and no requirement for organic capping agent. In addition, the structures of some biomolecules make it possible to fabricate nanomaterials with complex and interesting morphology that are difficult to achieve through chemical methods. In this study, tobacco mosaic virus coat protein (TMV cp) was investigated as a versatile template to mediate the synthesis of nanorings and spherical nanoparticles under neutral and alkaline conditions, respectively. While the prepared silver nanorings displayed superior selectivity (95% Faradaic Efficiency) and stability of catalyzing CO 2 electroreduction (CO 2 RR) compared to free silver nanoparticles prepared by chemical methods, the platinum nanorings showed excellent electrocatalytic activity for hydrogen evolution reaction (HER). Spherical nanoparticles synthesized under alkaline conditions (Pt, Pd and Au NPs) were also investigated for their catalytic behavior towards organic transformations such as 4-nitrophenol reduction and alkyne hydrogenation. References: [1] Huang J. et al. Chem. Soc. Rev., 2015, 44, 6330. [2] Pan Y. et al. Nanoscale, 2019, 11, 1895. [3] Kim C. et al. J. Am. Chem. Soc., 2015, 137, 13844. Figure 1

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)0.001
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 score0.006
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.030
GPT teacher head0.240
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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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