Surface-bound metal–organic framework microdomes and hybrids via a reacting microdroplet-driven approach: Enabling recyclable photocatalysis and real-time In-situ monitoring
Bibliographic record
Abstract
Fabricating surface-mounted metal–organic frameworks (MOFs) with controlled morphology and tailored functionality remains a significant challenge due to the complexity and scalability of current methods. In this work, we present a novel reacting microdroplet-driven approach for fabricating surface-mounted MOF microdomes. This method starts with multicomponent droplet formation on the substrate via solvent exchange. The in-situ droplet reaction is triggered by introducing the precursor solution to produce MOFs and, if needed, followed by a reaction to form MOF-metal hybrids through sequential functionalization. This multiple-stage synthesis process can be completed in a single narrow flow chamber, with each step finely controlled via simple manipulation of the solution flow. Using MIL-100 as a representative MOF, we demonstrate the degree of precision in tuning the size, morphology, and surface coverage of the MOF microdomes. The as-prepared MOF/silver nanoparticles (AgNPs) hybrids exhibit unique dual functionality, enabling simultaneous photodegradation for water treatment and real-time monitoring of degradation kinetics through in-situ surface-enhanced Raman scattering (SERS). Furthermore, we demonstrate that this reacting microdroplet-driven approach is applicable to other MOFs, such as ZIF-8, MIL-88A, and HKUST-1. The reacting microdroplet-based approach significantly simplifies the processes for creating surface-bound MOF microstructures, advancing their diverse applications in smart coatings, chemical and biological sensing, water purification, and energy storage and 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".