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Record W4401959535 · doi:10.1021/acs.accounts.4c00342

Toward Accurate Photoluminescence Nanothermometry Using Rare-Earth Doped Nanoparticles for Biomedical Applications

2024· article· en· W4401959535 on OpenAlexafffund
Miao Liu, Jinyang Liang, Fiorenzo Vetrone

Bibliographic record

VenueAccounts of Chemical Research · 2024
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéCanadian Cancer Society Research InstituteCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsPhotoluminescenceRare earthDopingNanotechnologyNanoparticleMaterials scienceChemistryOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

Conspectus Photoluminescence nanothermometry can detect the local temperature at the submicrometer scale with minimal contact with the object under investigation. Owing to its high spatial resolution, this technique shows great potential in biomedicine in both fundamental studies as well as preclinical research. Photoluminescence nanothermometry exploits the temperature-dependent optical properties of various nanoscale optical probes including organic fluorophores, quantum dots, and carbon nanostructures. At the vanguard of these diverse optical probes, rare-earth doped nanoparticles (RENPs) have demonstrated remarkable capabilities in photoluminescence nanothermometry. They distinguish themselves from other luminescent nanoprobes owning to their unparalleled and versatile optical properties that include narrow emission bandwidths, high photostability, tunable lifetimes from microseconds to milliseconds, multicolor emissions spanning the ultraviolet, visible, and near-infrared (NIR) regions, and the ability to undergo upconversion, all with excitation of a single, biologically friendly NIR wavelength. Recent advancements in the design of novel RENPs have led to new fundamental breakthroughs in photoluminescence nanothermometry. Moreover, driven by their excellent biocompatibility, both in vitro and in vivo, their implementation in biomedical applications has also gained significant traction. However, these nanoprobes face limitations caused by the complex biological environments, including absorption and scattering of various biomolecules as well as interference from different tissues, which limit the spatial resolution and detection sensitivity in RENP temperature sensing. Among existing approaches in RENP photoluminescence nanothermometry, the most prevalent implemented mechanisms either leverage the changes in the relative intensity ratio of two emission bands or exploit the lifetimes of various excited states. Photoluminescence intensity ratio (PLIR) nanothermometry has been the mainstream method owing to the readily available spectrometers for photoluminescence acquisition. Despite offering high temperature sensitivity and spatial resolution, this technique is restricted by tedious calibration and undesirable fluctuation in photoluminescence intensity ascribed to factors such as probe concentration, excitation power density, and biochemical surroundings. Lifetime-based nanothermometry uses the lifetime of a specific transition as the contrast mechanism to infer the temperature. This modality is less susceptible to various experimental factors and is compatible with a broader range of photoluminescence nanoprobes. However, due to relatively expensive and complex instrumentation, long data acquisition, and sophisticated data analysis, lifetime-based nanothermometry is still breaking ground with recently emerging techniques lightening its path. In this Account, we provide an overview of RENP nanothermometry and their applications in biomedicine. The architectures and luminescence mechanisms of RENPs are examined, followed by the principles of PLIR and lifetime-based nanothermometry. The in-depth description of each approach starts with its basic principle of accurate temperature sensing, followed by a critical discussion of the representative techniques, applications as well as their strengths and limitations. Special emphasis is given to the emerging modality of lifetime-based nanothermometry in light of the important new developments in the field. Finally, a summary and an outlook are provided to conclude this Account .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.119
GPT teacher head0.405
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2024
Admission routes2
Has abstractyes

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