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Record W4366146725 · doi:10.11159/icnnfc23.122

Anatase Nanoparticles for Raman Nanothermometry

2023· article· en· W4366146725 on OpenAlexvenueno aff
Thomas Pretto, Marina Franca, Veronica Zani, Silvia Gross, Danilo Pedron, Roberto Pilot, Raffaella Signorini

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsnot available
FundersUniversità degli Studi di Padova
KeywordsRaman spectroscopyAnataseNanoparticleMaterials scienceComputer scienceNanotechnologyChemistryPhysicsOpticsPhotocatalysis

Abstract

fetched live from OpenAlex

The determination of the local temperature is an interesting and intriguing topic in the nanotechnology and nanomedicine world, in terms of tuning the best noninvasive measurement protocol and identification of the more versatile and performing material.In this paper, the Raman technique and titania NPs have been exploited for the realization of a new optical nanotermometer.Biocompatible titania NPs have been properly synthesized, following a combination of sol-gel and solvothermal green synthesis approaches, with the aim of obtaining samples of pure anatase, characterized by crystallite dimensions defined and good control over the final morphology and dispersibility.Powder XRD measurements and room temperature Raman measurements confirmed that the synthesized samples are single-phase anatase.The SEM images clearly showed the nanometric dimension of NPs.Stokes and anti-Stokes Raman measurements, collected with the excitation laser at 514.5 nm (CW Ar/Kr ion laser), substantiate the possibility of evaluating the local temperature, which has been tested in the range of 298 -313 K, a range of interest for biological applications.The power of the laser has been carefully chosen in order to avoid eventual heating due to the laser irradiation.The data show that TiO2 NPs possess a high sensitivity and low uncertainty in the range of a few degrees as Raman nanothermometer material.

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 score0.112
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Recent Advances in NanotechnologySame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207