Biomimetic Superstructures Assembled from Au Nanostars and Nanospheres for Efficient Solar Evaporation
Bibliographic record
Abstract
Abstract The control of the shapes and assembling structures of plasmonic nanoparticles is vital for the optical properties and photothermal conversion performance. Here, gold nanospheres and nanostars are assembled into ridges/disordered nanohole arrays architecture in nanoparticle colloids using light‐trapping Papilio Paris forewings (OPPs) as templates. In solar steam generation, water evaporation efficiency reaches 83.3% for nanostar assemblies (NSAs) and 68.6% for nanosphere assemblies (NSPAs) under 1‐sun (1 kW m−2) irradiation. Both the biomimetic superstructures exhibit significant enhancement of near‐infrared light absorption compared to OPPs, and NSAs show higher absorption than NSPAs in the range of 200–2500 nm. Finite element method simulations reveal such broadband absorption is ascribed to the hybridization of localized surface plasmon modes of continuous‐assembled Au nanoparticles in the biomimetic structures as well as the wing photonic structures with light‐transferring ridges with an inverted V shape and light‐trapping disordered nanohole arrays. It is revealed that optical properties of superstructures can be tuned not only by controlling their 3D structures but also by changing the shape of nanosized building blocks. It is hoped that this work will provide inspirations for the design and assembly of nanophotonic structures and devices for solar energy conversion.
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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) | 0.001 | 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".