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Record W4393392026 · doi:10.1016/j.isci.2024.109655

Tuning the color of high-karat gold in Au-TiO2 nanoparticle composites all the way to black

2024· article· en· W4393392026 on OpenAlexfundno aff
L. Rossi, Endre Horváth, Tianyi Wang, Claudio Grimaldi, Andrzej Sienkiewicz, Bence G. Márkus, Dávid Beke, L. Forró

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsnot available
FundersNotre Dame Integrated Imaging Facility, University of Notre DameMagyar Tudományos AkadémiaBeef Cattle Research CouncilÉcole Polytechnique Fédérale de Lausanne
KeywordsColloidal goldPalette (painting)NanotechnologyMaterials scienceStructural colorationScope (computer science)NanoparticlePlasmonNanostructureHueComputer scienceVisual artsArchitectural engineeringOptoelectronicsArtArtificial intelligenceEngineeringPhotonic crystal

Abstract

fetched live from OpenAlex

For centuries, artisans have harnessed gold nanoparticles to imbue their creations with the vibrant hues that captivate the eye through interactions with visible light. In modern times, these distinct optoelectronic characteristics have pivoted toward the forefront of innovative technologies, finding their niche in advanced applications from solar energy to medicine, overshadowing their artistic heritage. This investigation reimagines the utilitarian scope of gold by innovating the optical characteristics of gold-titania nanostructures. This allows for an expanded palette of colors that retain the value of the precious metal. We employ nanostructured TiO 2 in a high-pressure-high-temperature sintering technique that stabilizes Au nanoparticles, thwarting coalescence, and Oswald ripening. Further refinement is possible by engineering TiO 2 color centers through the introduction of oxygen vacancies and Ti 3+ ions, which aid in creating an opulent high-karat black-gold, but preserve the mechanical attributes essential to the integrity and function of the final product.

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)0.001
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 score0.023
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.264
Teacher spread0.239 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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