Templated Droplet Evaporation‐Based Supraparticles in Environmental Applications
Bibliographic record
Abstract
Abstract Designing of innovative technologies for the development of functional materials remain crucial for achieving sustainable technological advancements. In this regard, nanoparticles (NPs) possessing enormous potential properties can be obtained using various facile methods for several applications. Adopting NPs as primitive building blocks can initiate the formation of complex entities named supraparticle (SP) to create several advanced functional materials. Additionally, SPs enable entirely new material characteristics to the system, owing to their individual entities, intrinsic properties such as coupling, spatial arrangement, and co‐localization. Indeed, methods such as template‐induced evaporation‐based assembly of NPs direct the shape, size, and morphology of SPs from their respective colloidal dispersions on a solvent‐repellant solid surface. Therefore, it is important to comprehend the formation of SPs and their structure‐property relationship with respect to practical application. This review presents a brief overview of SPs assembly by outlining the templated‐assisted evaporation‐based methodologies for synthesizing SPs. Further, the effect of various factors on the interaction between colloidal entities, solvent, and substrate, leading to the genesis of SPs is elaborated. Conclusively, specific properties are described and applications of SPs for energy storage, generation, and environmental remediation are reported, which collectively brings many interdisciplinary research fields to the same podium.
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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 | 0.001 |
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".