Mimicking nature to develop halide perovskite semiconductors from proteins and metal carbonates
Bibliographic record
Abstract
Abstract Halide perovskite (HPs) nanostructures have recently gained extensive worldwide attentions because of their remarkable optoelectronic properties and fast developments. However, intrinsic instability against environmental factors—i.e., temperature, humidity, illumination, and oxygen—restricted their real-life applications. HPs are typically synthesized as colloids by employing organic solvents and ligands. Consequently, the precise control and tuning of complex 3D perovskite morphologies are challenging and have hardly been achieved by conventional fabrication methods. Here, we combine the benefits of self-assembly of biomolecules and an ion exchange reaction (IER) approach to customize HPs spatial shapes and composition. Initially, we apply a biomineralization approach, using biological templates (such as biopolymers, proteins, or protein assemblies), modulating the morphology of MCO3 (M = Ca2+, Ba2+) nano/microstructures. We then show that the morphology of the materials can be maintained throughout an IER process to form surface HPs with a wide variety of morphologies. The fabricated core–shell structures of metal carbonates and HPs introduce nano/microcomposites that can be sculpted into a wide diversity of 3D architectures suitable for various potential applications such as sensors, detectors, catalysis, etc. As a prototype, we fabricate disposable humidity sensors with an 11–95% detection range by casting the formed bio-templated nano/micro-composites on paper substrate.
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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".