Thermal Properties of Lipid Bilayers Determined Using Upconversion Nanothermometry
Bibliographic record
Abstract
Abstract Luminescent nanomaterials have shown promise for thermal sensing in bio‐applications, yet little is known of the role of organic coatings such as supported lipid bilayers on the thermal conductivity between the nanomaterial and its environment. Additionally, since the supported lipid bilayer mimics the cell membrane, its thermal properties are fundamentally important to understand the spatial variations of temperature and heat transfer across membranes. Herein, a new approach is described that enables direct measurement of these thermal properties using a LiYF4:Er3+/Yb3+ upconverting nanoparticle encapsulated within a conformal supported lipid bilayer and dispersed in water as a temperature probe yielding the temperature gradient across the bilayer. The thermal conductivity of the lipid bilayer is measured as a function of the temperature, being 0.20 ± 0.02 W m−1 K−1 at 300 K. For the uncapped nanoparticles dispersed in water, the temperature dependence of the thermal conductivity is also measured in the 300–314 K range as [0.63–0.69] ± 0.11 W m−1 K−1. Using a lumped elements model, the directional heat transfer is calculated at each of the system interfaces, namely, nanoparticle–bilayer and bilayer–nanofluid, opening a new avenue to understand the membrane biophysical properties as well as the thermal properties of organic and polymer coatings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".