Bismuth‐Tin Core–Shell Particles From Liquid Metals: A Novel, Highly Efficient Photothermal Material that Combines Broadband Light Absorption with Effective Light‐to‐Heat Conversion
Bibliographic record
Abstract
This study presents a pioneering investigation of hybrid bismuth-tin (BiSn) liquid metal particles for photothermal applications. It is shown that the intrinsic core-shell structure of liquid metal particles can be instrumentalized to combine the broadband absorption characteristics of defect-rich nano-oxides and the high light-to-heat conversion efficiency of metallic particles. Even though bismuth or tin does not show any photothermal characteristics alone, optimization of the core-shell structure of BiSn particles leads to the discovery of novel, highly efficient photothermal materials. Particles with optimized structures can absorb 85% of broadband light and achieve over 90% photothermal conversion efficiency. It is demonstrated that these particles can be used as a solar absorber for solar water evaporation systems owing to their broadband absorption capability and become a non-carbon alternative enabling scalable applications. We also showcased their use in polymer actuators in which a near-infrared (NIR) response stems from their oxide shell, and fast heating/cooling rates achieved by the metal core enable rapid response and local movement. These findings underscore the potential of BiSn liquid metal-derived core-shell particles for diverse applications, capitalizing on their outstanding photothermal properties as well as their facile and scalable synthesis conditions.
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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 | 0.000 |
| 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 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".