Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".