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Record W4417288422 · doi:10.1021/acs.accounts.5c00670

FRET with Upconversion Nanoparticles

2025· article· en· W4417288422 on OpenAlexafffund
Eduard Madirov, Niko Hildebrandt

Bibliographic record

VenueAccounts of Chemical Research · 2025
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsFörster resonance energy transferNanomaterialsBiosensorQuenching (fluorescence)IonAbsorption (acoustics)

Abstract

fetched live from OpenAlex

ConspectusUpconversion nanoparticles (UCNPs) have become one of the most frequently used nanomaterials for optical biosensing and imaging. UCNPs unique properties include high photostability, low toxicity, large anti-Stokes shifts, and negligible sample background fluorescence under near-infrared (NIR) excitation. Combining these advantages with Förster resonance energy transfer (FRET) for the investigation of biomolecular interactions seems to be an obvious choice. However, UCNPs are rather large and have low absorption cross sections, which makes the development of UCNP-based FRET systems challenging. Nevertheless, various UCNP-FRET approaches have been developed over the last 20 years, and, in particular, the development of smaller UCNPs and new UCNP architectures has significantly advanced UCNP-FRET.Donor-acceptor distance is extremely important in FRET because its efficiency decreases with the sixth power of that distance. In UCNPs, the donors are the emitting lanthanide ions (activators), which can be placed all over the UCNP volume, resulting in some being close to and others far from the UCNP surface. The "far ones" may be bright because they are well protected from the environment, but they can only provide very low FRET efficiencies to an outside acceptor. The "close ones" can generate high FRET efficiencies but are also exposed to efficient quenching from the surrounding environment on the UCNP surface. This twisted tongue requires an ideal compromise between bright donor ions and a close surface distance for high FRET efficiency.The combination of different core-shell UCNP architectures with the ability to dope cores and shells with different amounts of sensitizers and activators, smaller UCNP sizes, reduced water absorption by changing the excitation wavelength from 980 to 808 nm, functional surface coatings and bioconjugation, as well as optimized FRET acceptor concepts are important parameters to overcome the limits of UCNP-FRET. Careful photophysical characterization, with spatial resolution throughout the entire UCNP volume and on its surface, and advanced modeling to better interpret the experimental results and understand the underlying mechanisms are key to translating UCNP-FRET into the application space.This Account discusses the recent advances of UCNP-FRET, including advanced UCNP core-shell architectures, UCNP surface chemistry and bioconjugation, versatility in acceptor selection, a better understanding of the UCNP-FRET mechanisms, UCNP-FRET modeling approaches, and applications in biosensing, bioimaging, and theranostics. We highlight the challenges of combining UCNPs and FRET and share our vision concerning future developments toward a complete understanding of UCNP-FRET, optimization of nanobiohybrid materials, multiplexed biosensing, and translation of UCNP-FRET technology into broadly usable applications in bioanalysis and biomedicine.

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)0.001
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 score0.002
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.032
GPT teacher head0.352
Teacher spread0.319 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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