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Record W4321789702 · doi:10.26434/chemrxiv-2023-cvxq0

Synthesis and Photothermal Properties of UV-Plasmonic Group IV Transition Metal Carbide Nanoparticles

2023· preprint· en· W4321789702 on OpenAlexafffund
Matthew J. Margeson, Yashar E. Monfared, Mita Dasog

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaOcean Frontier InstituteCanada Foundation for InnovationCMC Microsystems
KeywordsMaterials sciencePhotothermal therapyCarbideSurface plasmon resonanceTransition metalNanoparticleNanostructurePlasmonOxideMetalNanotechnologyChemical engineeringComposite materialMetallurgyOptoelectronicsChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Refractory nanostructures are low-cost and chemically and thermally robust alternatives to noble metal based plasmonic materials. Transition metal nitrides have received much of the attention lately, but there has been less emphasis on closely related non-layered carbide counterparts. In this work, plasmonic group IV transition metal carbide (TiC, ZrC, and HfC) nanostructures were prepared using a facile magnesiothermic reduction method which yielded phase pure product. TiC, ZrC and HfC with rock salt crystal structure and an average particle size of 24, 31, and 42 nm, respectively were obtained by reacting corresponding metal oxide, magnesium, and biochar in solid-state. Calculations performed using finite element method predicted these group IV carbide nanostructures to have localized surface plasmon resonance in the UV region between 150 175 nm. The photothermal transduction efficiency of each carbide was explored to further verify the plasmonic behavior. HfC was found to have the highest photothermal transduction efficiency (73%), followed by ZrC (69%), and then TiC (60%) at 365 nm.

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 score0.002
Threshold uncertainty score0.767

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 studies0.0000.000
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 head0.038
GPT teacher head0.234
Teacher spread0.197 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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